Emergent Interaction - umu.sepeople.cs.umu.se/bopspe/publications/EI/EI-pre-study.pdf · Agneta...

Emergent Interactiona Pre-study

Niklas AnderssonAnders BrobergAgneta BränbergLars-Erik JanlertErik JonssonKenneth Holmlund & Jonny Pettersson

UMINF 01.16

DEPARTMENT OF COMPUTING SCIENCE

SE-901 87

SWEDEN

ISSN-0348-0542

i

Emergent Interaction a Pre-study

Niklas Andersson1

Anders Broberg2

Agneta Bränberg3

Lars-Erik Janlert4

Erik Jonsson5

Kenneth Holmlund6 & Jonny Pettersson7

UMINF 01.16

DEPARTMENT OF COMPUTING SCIENCE

SE-901 87

SWEDEN

ISSN-0348-0542

1 [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]

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EMERGENT INTERACTION - A PRE-STUDY

© 2002, UCIT, Department of Computing Science, Umeå University, SE-901 87 Umeå, Sweden.

Contact: Anders Broberg, [email protected]

Graphic design: Niklas Andersson.

Illustrations: Niklas Andersson and Diana Africano.

Typefaces: Adobe Garamond 10/12pt (expert font with liga-tures) and Adobe Myriad (with +50/1000 em tracking).

Printed and bound in Sweden by UmU Tryckeri, Umeå University.

UMINF 01.16

ISSN-0348-0542

iii

AbstractThis is the final report from a pre-study on Emergent Interaction. The purpose of the pre-study was to get a clearer idea of what emergent interaction is, what it can be used for, and what the problems are, as a preparation for a larger study. The project is an Umeå Center for Interaction Technology (UCIT) and Ericsson Erisoft collaboration, with participants from four UCIT labs (CCL, IDL, DML and VR-lab) and Ericsson Erisoft Research. It represents the first step of a UCIT research program on emergent interaction.

An Emergent Interaction Systems consists of an environment in which a number of individual actors share some experience/phenomenon. Data originating from the actors and their behaviour is col-lected, transformed and fed back into the environment. The defining requirement of emergent interac-tion is that this feedback has some noticeable and interesting effect on the behaviour of the individuals and the collective - that something ‘emerges’ in the interactions between the individuals, the collective, and the shared phenomenon as a result of introducing the feedback mechanism.

The immediate effect may be enhancement of the individual experience - with resulting effects on the individual’s behaviour, choice of action, and so on. The immediate effect can also be some kind of change in the observed, shared phenomenon. In particular the feedback might effect or establish some kind of collective control. The effect could also involve some kind of organizing and controlling of the collective. ‘Organization’, in this case, need not imply uniformity and regularity, it could just as well be to diversify or even randomise behaviours.

Systems in which people interact in a shared feedback loop already exist. What is new with the emergent interaction research program is a unified approach to such systems, and an agenda that seri-ously addresses the task of designing EIS. This is made possible and necessary by the new information technology. Computer, communication, and interface technologies crucially change the conditions and possibilities. First, the amount and variety of data that is possible to collect, and the speed of collec-tion increase radically. Second, the new information technology offers completely new possibilities to design and control the feedback function and thus ultimately the behaviour of such systems. Third, the feedback loops can be speeded up many orders of magnitude to match the ‘natural’ time scales of individual and collective behaviour, thus also making the existence and importance of such systems more easily recognisable. Fourth, in this new time scale, with these new capabilities, there are great opportunities as well as possible hazards that we so far only can guess about.

A number of pre-study activities are summarised in the report. Some of the concrete results are: a list of focus areas for emergent interaction; a categorisation of emergent interaction applications into nine categories; a classification of four different aspects of emergent interaction applications; a list of prototype requirements.

This report will serve as a basic framework for the continued study of emergent interaction within the UCIT emergent interaction research program. A number of suggestions for the next actions within this program are also given. First, three possible first prototype implementations (spinning, collabora-tive art, campus communities) are suggested. They cover a number of aspects that have high priority for closer investigation. Second, the EI concept should be established in the scientific world as well as in the commercial world. Third, we have identified a number of research issues to study and ideas to develop (Emergent Architecture, Emergent Architecture Protocol, Emergent Design are some) within the emergent interaction field. Other important activities, closely related to but outside of the proper research program, are market studies and work to get external financing for the program.

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AcknowledgementThe authors wish to thank the organisations and persons who contributed to the completion of this report. In particular, we wish to thank our UCIT and Ericsson Erisoft colleagues for providing vital comments, information, and suggestions during meetings, workshops or over a cup of coffee. Without this large group of people the content of this report would not have emerged to what it now became.

We would like to give special thanks to the following people; Thomas Pederson for his contribution to the physical-virtual discussion; the invited speakers at our seminars Ulf Holmgren, Ulf Jonsson, Magnus Nilsson, Per-Axel Persson, and Erik Stolterman; and Diana Africano for her help with the illustrations.

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Content

1. Introduction 1Expected Outcome from the Pre-study 2The Outline of this Paper 2

2. Background 4Human-idea-thing Interaction 4Emergence 5Enhancement 7

3. Methods and Activities 8Workshops and Brainstorming 8Focus Areas 9

4. Emergent Interaction Systems 14A Functional Model of Emergent Interaction 14Control System Perspective on EI Systems 16System Architectural Issues 16Design Space of EI Systems 18

5. Application Areas 20Existing Examples 20Go with the Flow 20The Democratic Process 21Web-based Recommendation Systems 21Sports arena 22Other examples 22New Implementations 23Ideas for Ei Prototypes 23General Aspects of Emergent Interaction Applications 30Handling the Negative Aspects 32

6. Technical System Aspects 34System Architecture 34System Architecture for Emergent Interaction Systems 35Example of Emergent Interaction System Architectures 36Open Issues 39Data Communication 40Wireless Data Communication 40Data Communication in Emergent Interaction Systems 42Protocols and Services 44Security 44Open Issues 45Traffic Patterns 46Traffic Patterns in Eis 46

7. Interaction 48Input Collection from the Actors and their Surroundings 48Feeding back Processed Information 49Visualising the Information 51Building the Actors’ Experience 52Open Issues 53

8. Sociological Aspects 54Open Issues 56

9. Data, Information, and Knowledge 57Open Issues 58

10. Pre-study Outcome 59Designing for Emergence 59Emergent Interaction in a Context 60Applications 61Technical Frameworks 61Research and Development Projects 62Journals and Conferences 63Possible Application Areas 64Positive and Negative Aspects 64Prototype Requirements 65Cost Requirements 65Technical Requirements 65

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Effect Requirements 66Security Requirements 66Summary of the Requirements 66Open Issues 67Suggestions 67Implementations 68Research and Development 69Establishing the Emergent Interaction Concept 69Market studies 70External financing 70Summary 71

Appendix A - Ideas on EI- Application 72

Appendix B - Focus Area Map 78

Appendix C - Collaborative Art 79

References 80

Introduction 1

1. Introduction

This report is the final report from a pre-study on Emergent Interaction (EI) started in spring 2001. The pre-study project is an Umeå Center for Interaction Tech-nology (UCIT) and Ericsson Erisoft collaboration, with participants from four UCIT labs (CCL*, IDL#, DML£ and VR-lab¤) and Ericsson Erisoft Research$. It represents the first step of a UCIT research program on emergent interaction. Niklas Andersson (IDL), Anders Broberg (CCL) who has also been project leader for the pre-study, Agneta Bränberg (DML), and Erik Jonsson (Ericsson Erisoft, AWARE) have been main participants in the projects. Kenneth Holmlund (VR-lab), Lars-Erik Janlert (CCL), and Jonny Pettersson, have all added valuable competences to the project. The size of the project has been approximately 1100 hours.

Our project has been inspired by the emergence of applications of a new kind, such as: Amazon.com (Amazon.com, 2002); GPS art, orienteering, and discovery (Pryor & Wood, 2001); E-street (MRC, 2001b); IT-hockey (MRC, 2001c); the SMS/WAP based FriendFinder (Telia, 2001); etc. The interaction between the individual and the collective is a common theme for these applications, i.e. the social aspects of the application are in focus. Also significant is the fact that some of these applications have emerged (all three GPS applications) from individual use of the technique, in the same sense that the web publishing of personal infor-mation emerged from Tim Berner-Lee’s ideas of making the research databases at CERN available for researchers everywhere in the world. (Berners-Lee, 1990).

An Emergent Interaction System (EIS) is defined to consist of an environment in which a number of actors share some experience/phenomenon, and in which part of the interaction among actors in the system emerges via a shared feedback loop. Data originating from the actors and their behaviour is collected and used to com-pute a feedback, which is delivered into the environment. The defining require-ment of emergent interaction to occur is that this feedback has some noticeable and significant effect on the behaviour of the individuals and the collective. Some-thing emerges in the interactions between the individuals, the collective, and the shared phenomenon as a result of introducing the feedback mechanism. The immediate effect of emergent interaction may be enhancement (see section Back-ground where the term of enhancement is discussed) of the individual experience – with resulting effects on the individual’s behaviour, choice of action, and so on. The immediate effect can also be some kind of change in the observed, shared phenomenon. In particular the feedback might in effect establish some kind of collective control of it. The ultimate effect could also involve some kind of organ-ising and controlling of the collective. ‘Organization’, need not imply uniformity and regularity, it could just as well be to diversify or even randomise behaviours.

Systems in which people interact in a shared feedback loop already exist. What is new with the emergent interaction research program is a unified approach to such systems, and an agenda that seriously addresses the task of designing EISs. This is made possible and necessary by the new information technology. Compu-ter, communication, and interface technologies crucially change the conditions and possibilities. First, the amount and variety of data that is possible to collect, and the speed of collection increase radically. Second, the new information tech-nology offers completely new possibilities to design and control the feedback function and thus ultimately the behaviour of such systems. Third, the feedback loops can be speeded up many orders of magnitude to match the ‘natural’ time scales of individual and collective behaviour, thus also making the existence and

* http://www.cs.umu.se/research/cogcomp/# http://www.dh.umu.se/£ http://www.medialab.tfe.umu.se/¤ http://www.vrlab.umu.se/$ http://www.ericsson.com/erisoft/

2 Introduction

importance of such systems more easily recognisable. Fourth, in this new time scale, with these new capabilities, there are great opportunities as well as possible hazards that we so far only can guess about.

Expected outcome from the pre-studyTo get a clearer idea of emergent interaction, in order to prepare for a larger study of emergent interaction was the overall purpose for the project. The suggested purposes for the subsequent main study can be summarised in five sub goals:

• Create new application/products based on the results

• Create an understanding of the new possibilities (including dangers) and techniques for collective control, and control of collectives

• Create an understanding of the new possibilities (including dangers) and techniques for social behaviour and applications

• Create a testing ground for various key technologies and infrastructures

• Provide new tools for analysing, simulating and understanding social behaviour

Our work in the pre-study has been guided by three general questions:

• What can emergent interaction be used for?

• What are the problems with it?

• What is the potential of the concept?

To establish an articulated and well-grounded consensus on the concept of emer-gent interaction is another way to formulate the main purpose of the pre-study. This report is the concrete result from that process of knowledge and consensus building. More specifically, this preliminary report on emergent interaction and emergent interaction systems addresses:

• Feasibility

• Usefulness

• Usability issues

• Theoretical background and requirements

• Key research issues

• Technological requirements

• Possible application areas

• Some application scenarios and design sketches

• Suggestions for whether and how to proceed with a larger project

The Outline of this PaperThere are ten sections in this report, of which the first is the present introduc-tion. The purpose of the second section, Background, is to present three concepts: enhancement, Human Idea Thing Interaction (HITI), and emergence; all three are important for the understanding of the background of the pre-study. The purpose of the third section, Methods and Activities, is to discuss the methods used and the activities in the pre-study. The purpose of the fourth section, Emergent Interaction Systems, is to introduce a high-level model of EISs aimed to more strictly discuss, compare, evaluate, etc EISs; some open research issues are also introduces in this section. The purpose of the fifth section, Application Areas, is to discuss ideas for

Introduction 3

EI systems and how to assess the value of implementing them. The purpose of next section, Technical System Aspects, is mainly to discuss emergent interaction from a technical perspective, where data communication with traffic patterns, and system architecture are in focus. The seventh section, Interaction, takes an inter-action perspective on EIS, by discussion data collection, feedback, timing, etc. The purpose with section eight, Sociological Aspects, is to take the sociological and psychological aspects of emergent interaction into account. The purpose with the ninth section, Data, Information, and Knowledge, is to discuss emergent inter-action from a perspective of Computer and Information Science, by discussing computability, information flows, and system architectural aspects of EISs. The last section, Pre-study Outcome, discusses how to continue in a more extensive project with implementation of some prototype, user studies, etc.

In addition, three appendices are included in the report. Appendix A is a sum-mary of all proposals for applications that have been discussed in different forums during the project. Appendix B is a summary of the intent of each section in the report and how different focus areas (see page 13 for a discussion on focus areas) are considered in the report. Appendix C describes an example of emergent inter-action application.

4 Background

2. Background

Where do we look for existing concepts, methods, theories and facts that can help in defining, understanding and developing emergent interaction? Some disci-plines and research areas that are obvious candidates for an information search are sociology, social psychology, ethnology, cognitive science, artificial life, in particu-lar swarm intelligence, control theory, human-computer interaction, computer-mediated communication. Less obvious fields of research and less widely known specialties might also have something important to offer. It is impossible in a pre-study to cover emergent interaction from the viewpoint of all possible disciplines and research areas, but the Focus Areas section is aimed to give as broad view of emergent interaction as possible. The purpose of this section is to introduce three important aspects of emergent interaction: human-idea-thing interaction, emer-gence, and enhancement.

Human-Idea-Thing InteractionUmeå Center for Interaction Technology (UCIT) was established in September 2000 as a centre for multidisciplinary research grounded on interaction, commu-nication and simulation technology and centred on its use from a human per-spective (Janlert, 2002). The purpose is to bring together different branches of technological science, cognitive science and human-related research, in develop-ing the concepts, theories, methods, technologies and tools that will facilitate the passage from industrial society to information society. On the central concept of interaction we take an integrated perspective, taking into account all the different interconnections and interaction possibilities between the three basic categories of Humans, Things (material objects) and Ideas (information captured in various media) - Human-Idea-Thing Interaction (HITI).

Human

Idea Thing

Human-Idea-Thing Interaction (HITI) is a concept that extends more traditional areas of research such as HCI (Human-Computer Interaction) and HMI (Human-Machine Interaction) in important ways, involving several other fields and research topics. Some examples are Computer-Mediated Communication, Data Com-munication, Media Research, wireless communication between artefacts, Signal Processing, Interaction Design, Cognitive Science, High-Performance Comput-ing, Scientific Visualisation, Virtual Reality, Tele-immersion, and more. Whereas some of these areas include different aspects of human behaviour and human

Figure 1. Basic typology of interaction: idea-idea; human-idea; human-human; human-thing; thing-thing and idea-thing.

Background 5

values, the HITI concept makes the human point of view an integral part of the research endeavour.

Thing stands for all kinds of stationary and mobile physical artefacts – equipped or integrated with information technology. Idea represents all kinds of informa-tion and other meaningful expressions in various forms such as numbers, texts, pictures, sounds, animations, simulations – that is captured, stored, processed, presented and transmitted with support of modern information technology. This categorisation corresponds closely to the social world of people and human behav-iour, the information world of databases, documents, media, computation and communication, and the physical world of everyday artefacts, environments, spaces and places – all viewed and defined from a human point of view.

The concept of HITI points out the importance of the interconnections and interdependencies between the three categories of humans, things and ideas, and the need to consider them in their entirety. For instance, addressing Human-Idea interaction in separation from Human-Thing and Human-Human interaction we are prone to neglect the increasingly burdensome role of humans as mediators between the world of information and the physical world. The HITI concept helps in identifying and analysing the critical problems and provides a framework support-ing creative and innovative development. It suggests, for instance, that information registered in the handling and operation of some object may be used for further purposes; that external, remotely accessed information may be utilised to improve an artefact’s performance, etc. By its comprehensiveness and integrative attitude the HITI concept invites participation and close cooperation with the humanities and the social sciences, involving subjects such as philosophy, sociology, ethnol-ogy, linguistics, art, and more.

By considering the interactions between humans, things and ideas as a whole, the HITI approach opens for discovery of new combinations, new patterns of relationships and new user behaviour. The privileged perspective is that of the user. How do we allocate and distribute knowledge and information between our mind, our body, our tools and informational, social and physical environments? How do we shape things and environments to fit them to our bodily, sensory and cognitive capabilities, to please and engage our hands, eyes and minds, and to facilitate everyday life? A close collaboration between researchers from the human-istic and social sciences and researchers from the natural and technological sci-ences will be a condition for a successful result.

EmergenceThe global behaviour of a simple system that consists of a few simple subparts can often be predicted. But when a system gets sufficiently complex, i.e. with spatial and/or temporal consequences due to local interactions between many subparts and/or more complex subparts, it usually becomes difficult to predict the global behaviour of the system. This means that it becomes an often exceedingly difficult task to divide the system into its subparts, analyse each subpart and then draw conclusions about how these subparts will produce a collective global behaviour. In other words, it is the interaction between the subparts that is the interesting phenomena and that is also hard to predict.

Emergence concerns such behaviours that arise when subparts interact in a suf-ficiently complex system.

Emergent behaviour can be observed in any sufficiently complex system, natu-ral or artificial. An example from nature is ants. Each ant is believed to use a few simple rules that guide their local behaviours. They are not believed to have any global view of the ant society that they are part of, or obeying any direct global control. One example is how they find the shortest way to food. When an ant walks it deposits a pheromone trail on the ground, and when an ant chooses direc-tion it will choose with higher probability those directions marked by a stronger

6 Background

pheromone concentration. The overall effect is that the ants together find the shortest way. The reason is that a shorter way will receive a higher concentration of pheromones than a longer way, since it takes less time to walk a short way than a long way.

Another example is human beings. A human being consists of a large amount of cells. These cells are organized into different organelles. An organelle displays an emergent behaviour that cannot be anticipated from just inspecting the coop-erating cells. These organelles cooperate to form a human being, which is also hard to explain behaviourally by just looking at the different organelles. And if we look at how different human beings interact in a population, it is also hard to predict the emergent behaviours of that population.

As can be seen by these examples, emergent behaviours are characterised by two main properties. First, it is difficult to predict the global emerging behaviour by just inspecting the subparts. Second, it is hard decide which subparts to use to get a particular desired global emergent behaviour. Emergent behaviours are further characterised by:

• The emergent behaviour is determined in a bottom-up way.

• The subparts that are involved in determining the global behaviour are distributed.

• The global behaviour arises from local determination of behaviour in each participating subpart.

• As can be seen by the human beings example, emergent behaviours can arise on all levels.

All this implies that emergent interaction systems are hard to design and that it is hard to foresee all resulting emerging behaviours that emerge from the system.

Although the term emergence has become very popular for describing every-day phenomena there is still no consensus how emergence should be formally described in scientific terms. There are no formal definitions and we cannot through analysis of a real system or a model system tell whether the system shows true emergent behaviour or if the behaviour just resembles an emergent one.

There is therefore a risk that the term is used as a substitute and an excuse for lack of knowledge (i.e. a theory or model) about the system of interest.

Many scientists have devoted their work much of their time to search for the universal properties and a formal definition of emergence. Several of these attempts are done within a fairly new research field called Computational Mechan-ics (Crutchfield & Young, 1989). Computational mechanics is a method for inferring causal architecture – represented by a mathematical object called the e-machine – from observed behaviour. The e-machine captures all patterns in the process that have any predictive power, so computational mechanics is also a method for pattern discovery.

Within this framework Shalizi has recently suggested a more rigorous defini-tion of emergence:

“One set of variables, A, emerges from another, B if (1) A is a function of B, i.e., at a higher level of abstraction, and (2) the higher-level variables can be predicted more efficiently than the lower-level ones, where “efficiency of prediction’’ is defined using information theory.”(Shalizi, 2001)

We will not go further into how to actually measure the “efficiency of prediction” as it is beyond the scope of this report, but merely conclude that a formal theory of emergence would be an invaluable tool when attempting to approach emergence from a design and implementation perspective.

Background 7

EnhancementEnhanced experience has been used earlier as a keyword in the Arena project (MRC, 2001a). Enhancement may apply either to emotion or to understanding, or to both. Note, that it is difficult and not always such a good idea to completely separate emotion and cognition.Emotional enhancement, some different types of:

• empathy or emotional identification with agents in shared experience – an agent may be a person, it might also be a group or team of persons – it could be an animal or a herd of animals, and it could also be some kind of artefact, a robot, a car, a machine, or even a group of such more or less ani-mated objects (such as the cars flowing around you in the Monday morning traffic) – opening interesting perspectives of perceiving and understanding our technological environment in emotional terms

• sensual amplification and/or transformation of the shared experience – basically making events appear louder, brighter, more colourful, earth-shak-ing, etc. – can also involve transformations making signals not normally picked up by our senses experienceable, may also involve synaesthetic crossovers to different modalities and senses – the same techniques may of course just as well be used for the purpose of enhancing understanding

• expression of own emotions – magnifying (compare above) natural expres-sion of emotions so as to make a stronger impression, or supporting better articulated, more nuanced expressions, or even providing new means, new outlets of expressing emotions difficult or impossible to express otherwise – similar techniques may again be used to enhance cognitive expression

• collective sense of togetherness, of being part of the collective is another plausible target for emotional enhancement, particularly in emergent inter-action

Cognitive enhancement, some different types of:

• commenting information – annotating the experience (phenomenon) with history, statistics, prognosis, etc.

• hyper reality – enabling experiencing what normally is unperceivable (hidden, beyond the visual spectrum, etc.), compare note above on sensual amplification

• overview – adding multiple, alternative perspectives to the normal single point of view of an individual, getting an ‘objective,’ generalised viewpoint

• attention – helping to focus on the relevant information in the middle of information and sensation chaos

• evaluation – providing various value assessment of the experience, opin-ions, grading, comparisons, etc.

• analysis – support for deeper understanding of the processes involved in the experienced phenomenon.

8 Methods and activities

3. Methods and Activities

The activities in the project have mainly been of analytic character: weekly project meetings, literature studies, seminars, workshops and brainstorming. We have had both internal and external activities. The purpose with the internal activities has mainly been to push the knowledge of the concept further on. The purposes with the external activities have been to spread our ideas and to get new influences into the project. All activities during the project can more or less be traced back to two activities: a workshop on a high level model for EISs, and brainstorming sessions on research issues (Focus Areas).

The overall purpose with this section is to give an insight in the project and the methods and activities used to fulfil the project goal. More than twenty recorded project meetings have been completed within the frame of the pre-study. These meetings has functioned as the base for the project and kept the project running. The atmosphere at these meetings has been open-minded and constructive.

To establish a common, well-informed and articulated view on how emergent interaction should be understood, we have also arranged a number of seminars. The following topics were selected: wireless networks, sensor technologies, system architecture, communities, and social psychology. Seminars for all topics except social psychology have been given. The following seminars were given as public seminars:

• Seminar on Wireless network by Ulf Holmgren and Ulf Jonsson, Department of Applied Physics and Electronics, Umeå University

• Seminar on Sensor technologies by Per-Axel Person, Department of Applied Physics and Electronics, Umeå University

• Seminar on System Architecture by Magnus Nilsson, Ericsson Erisoft.

• Seminar on Communities by Erik Stolterman, Department of Informatics Umeå University.

Two other seminars was arranged in association with the project:

• External seminar on emergent interaction by Anders Broberg at Depart-ment of Computing Science, Umeå University

• Internal seminar about the Ericsson Erisoft’s project ARENA/ICE by Erik Jon-sson Ericsson Erisoft.

In summary, the project has been filled with a broad spectrum of activities. The weekly meetings have stood for the continuity in the project, where the progress of the project has been discussed (well documented in the minutes from the meetings). The seminars have stood for the widening of the knowledge and for the establishment of the EI concept. The workshop activities have stood for the creativity in the project where many new interesting ideas and views have been proposed and reviewed. The literature studies have stood for the analysis and con-textualisation of the concept. The writing activities have stood for the synthesis of forming a whole of the parts. The rest of this section discusses the workshop and brainstorming activities.

Workshops and BrainstormingWe have had six workshops or brainstorming sessions, three of them have been internal to the project or extended to include other UCIT-staff. The first work-shop was aimed to identify and discuss areas of interest from an emergent inter-action perspective. This activity is discussed below in this section. The result

Methods and activities 9

from this workshop has ruled much of the activity later in the project, and the process of developing and discussing these focus areas has continued throughout the whole project. The second workshop was aimed to discuss the structure of this report and there is not so much to present from that workshop beside the fact that this report is a result. The third workshop was aimed to discuss a high-level model for EISs.

The other three activities have been external. The first two directed to Erics-son staff to discuss applications of emergent interaction and propose prototype applications, and to discuss positive and negative aspects of these proposals. The result is discussed in section Application Areas. In all these application workshops the same method was used – The six thinking hats (Adams, 2002; APTT, 2001). The method of The Six Thinking Hats is aimed to structure and focusing the meeting activities, i.e. divide the informative, creative, positive review, negative review, feeling, and conclusion activities into isolated sessions.

Focus AreasTwentyone focus areas were identified to be of interest for the study of EIS. Each of these areas are described below with a short definition: what is the intention with this area; what interesting issues are there to resolve; what possibilities and opportunities can be identified?

Application areas There could be a large number of different application areas for emergent interac-tion systems, considering that emergent interaction phenomena can be found in a wide range of cases: from swarms of termites (Resnick, 1994) to economical systems, in biological systems (Green, 1993), in the emerging of new life forms in the biological evolution, and definitely in large groups of people. The main inter-est of this focus area is to identify possible application areas for emergent interac-tion systems. Section Application Areas elaborates and exemplifies the variety of applications for EI.

Categories and characteristicsThere are groups of actors in almost all forms of communities, actors with a simi-lar way to act, behave, think, etc. These groups or categories of actors can be char-acterised by several parameters such as: how well known the group is, what makes it a group (the way they act, think, etc.), how active the group is, how easy it is to identify a group or members of a group, the awareness of being a member, the dynamics of the group, etc. The main issues in this focus area have to do with mechanisms to identify groups and group members and mechanisms to con-trol the behaviour of different kinds of groups in EI systems. There is a need to develop relevant ways to characterise groups (interesting parameters). Some areas with high relevance for this focus area are:

• Collaborative filtering

• Social navigation

• Clustering algorithms, LSA, SOM, K-nearest neighbours, and SVM.

CommunicationHow will the communication between the different parts of an EI system take place? Different techniques, protocols and equipment should be evaluated. Wire-less communications, packet data, TCP/IP, Bluetooth, synchronous/asynchronous, delays, throughput, packet loss, bit errors, user roaming and mobility, security, multicast, unicast, broadcast, etc., are examples of questions and sub-areas to look into.


CommunitiesA community is a group of people with something in common, and that some-thing is in focus for their communication. There are two main reasons why com-munities are of interest for EI. First, EISs can be used to support the emergence, maintenance, and the phase-out of communities. Second, a lot of research con-cerning different aspects of communities has been done, from studies covering quite technical aspects to studies covering more sociological and psychological aspects of communities, and studies in between (Stolterman, 2001). A commu-nity can be characterised by a number of characteristics. An open question is which of the characteristics of a community that are relevant for EI systems.

Computability AspectsTheoretical and practical limits of time and memory resources constrain what can be computed. We need to analyse the most important computation tasks in terms of time and memory requirements, and find out what it is that characterises these computations. It would also be of interest to study different computing paradigms and how well they suit EISs, for example centralised versus distributed comput-ing.

Control Systems An emergent interaction system has some resemblance to an ordinary control system (Doeblin, 1985). Control system theory talks about input, output, feed-back and timing, for instance. What is the purpose of the system? Do we have a servo problem or a regulator problem? In the servo problem the goal is for the output to follow a given reference input. In the regulator problem on the other hand the goal is to keep the output at a constant level. The system could be adap-tive or fixed. The complexity of the system is of course essential. If the system is too simple and predictable it is of less interest to us. Generally, we may expect emergent interaction systems to be much more complex than the typical control system dealt with in control systems theory.

Data CollectionData collection is a central part of an EI system. Which information is collected and how it is collected are important issues. What kind of data is can be made available and what can we do with that data? Which data would seem to be needed in order to do what we want? The data can be of different kinds, e.g. user input and data from sensors. Other issues are how complex or basic the collected information is. Is the same kind of data collected from all users all the time?

Democratic and Undemocratic systemsAn EIS collects data from actors in the system; if the system treats data from all actors equally the system is a democratic system. If the system treats the actors unequally it is an undemocratic system. It is also possible to imagine systems that dynamically change their democratic/undemocratic behaviour. For example, in the normal case the system is a democratic system, but under some circumstances the system turns into an undemocratic system, giving one or several actors a more prominent role in the feedback loop. Some of the interesting issues are social: how and when is it considered appropriate to use a democratic or an undemocratic system? There are also computational aspects of implementation, such as how to implement the control mechanisms.

FeedbackFeedback is essential; without it there is no dynamics in the system. The content of the feedback is closely connected to what the goal of the EIS is. This focus area is concerned with the form of the feedback: what forms of feedback are possible and what forms of feedback will be able to effectively reach the actors, eventually affecting their behaviour. Big displays for everyone, or small displays, one for each user? Should everybody have the same feedback? What kind of feedback should it


be? Visible, audio, smell, changing the temperature, air/gas, are some of the alter-natives. Should all actors be aware that there is a feedback? What the form of the feedback is? What the content of the feedback is? Should the feedback (normally) be conscious or unconscious? Close to the actors or in the background? Should the feedback be direct or indirect?

ModelThe main issue in this focus area is to create a high-level functional model for emergent interaction systems. This model can be used in several ways, for exam-ple, to:

• explain the concept of emergent interaction

• identify and classify existing phenomenon in terms of emergent interaction

• support the design phase of new emergent interaction systems

In section four, Emergent Interaction Systems, such a high-level model is presented and discussed.

Physical-Virtual SystemsAn emergent interaction process can take place largely (but not completely, as long as the human beings in it make use of their bodies) in the virtual world, like the “rating systems” that some web-based bookshops have. An example of a system with a more even mix of physical and virtual components is MRC’s IT-hockey project (MRC, 2001c), where the main shared phenomena is a physical activity, and a virtual media is used for the presentation. It is also possible to have situations where emergent interaction takes place entirely in the real world, for instance a theatre where the audience takes an active part in the production. We need a model for emergent interaction that covers the whole range of the physical-virtual scale.

Ongoing research in physical-virtual systems tries to explore the interaction possibilities that emerge from integrating the physical world with the virtual world. Depending on research interest, the virtual world can range from being the content in the environment provided by one single cellular phone, to the entire Internet based on and accessed through networked computers. The explored physical-virtual environments are often tuned towards specific application areas such as: digital augmentation of existing physical environments, information visu-alisation, CSCW, tele-presence, education, simulation, and art. Physical-virtual research finds inspiration in related areas such as Ubiquitous Computing (Weiser, 1991), Tangible (Ishii & Ullmer, 1997) and Graspable (Fitzmaurice, Ishii, & Bricks, 1995), User Interfaces, Mixed Reality (Ohta & HideyukiTamura, 1999), and Augmented Reality (Mackay, Velay, Carter, Ma & Pagani, 1993).

Fundamental research issues include how to create interactive systems that allow users to make optimal use of the two worlds’ unique features, decrease the costs of shifting between physical and virtual environments in the middle of tasks, sense users’ physical activities, how to make high-level interpretations of physical and virtual activities, make cause-effect relationships in physical-virtual environ-ments appear as natural as in physical environments.

Presentation and InteractionThe area of “Presentation and Interaction” concerns the part of the system that is the most “visible” for the users. How will the users experience this new kind of system and how will they interact with it*? This area will be most active in the latter phases of the EI project. The work will build on the findings and results from the focus areas of Psychology, Feedback and User Awareness, among others.

* Mostly interaction on a user interface level


Psychology and SociologyEISs are systems engaging a number of people forming a collective – in some sense an emergent unit created and maintained by the shared phenomenon and the feedback. How does such a collective “think” and “act”? What do we mean with thinking in this case? How do mass phenomena like collective panic or hysteria happen? What kind of feedback will different kinds of people react to? What kind of feedback will different persons react differently to, and what kind of feedback will different persons react in the same way to? How do behaviour patterns spread, what makes them grow and what make them die? These are some examples of questions focusing on the sociological and psychological aspects of EI. It is obvi-ous that this is a very important focus area, the human actors being by far the most complex components of an EI system.

ScenariosScenarios are fictional stories, with characters, events, products and environments. They allow us to explore product ideas and key themes in the context of a real-istic future. The stories always have a visual element – because they are the vehi-cle for expressing visual design ideas and interactions between people and things. Sketches, photographs and video or computer simulations might provide the visual element. The choice of medium depends on whether we want to depict behaviours, which benefit from dynamic rendering like animation. We might also want to use sound. Scenarios range from single page renderings and sketchy refer-ences of product concepts to detailed multiple frames of interaction sequences. The choice depends upon the resolution level of the design at that point.

SecurityEISs is a kind of applications where security, integrity, and privacy issues call for attention – people want to be left alone, they are afraid of being logged, and they worry about data being transmitted in a secure way. There may be risks of losing the control over the system with an escalation of unwanted effects. There is the risk that an unauthorised person takes control, which definitely could cause unwanted effects. It is important that secure transmission of data and authorisa-tion issues are solved for EIS architectures.

Simulation Simulation can be used as a tool to test and examine the concept of EI systems and explain how they work. Different emergent interaction systems can be tested through simulations. Different techniques, architectures and application areas can be illustrated and evaluated. What to simulate and how to do it needs to be inves-tigated. Another issue to resolve is how to handle the results from the simulations. Identifying new research areas could also be an output from this work.

System ArchitectureSystem architecture is about identifying which components, logical and physical, an EI system can be compiled of. The roles of each component need to be speci-fied. Issues such as scalability, centralised or distributed architecture, dedicated or public systems, etc need to be investigated. Different architectures can be pro-posed for different purposes, situations, and variants of EI systems.

System AwarenessThe system perspective of awareness deals with questions related to user mod-elling, i.e. to make the system aware of the ongoing processes in the “shared phenomenon” and its surrounding, but also questions such as direct or indirect feedback and foreground/background aspects of feedback. The important issue is to identify properties of EIS to make it easier to choose techniques for user model-ling and design the feedback.


TimingIf the feedback is too slow in an EIS, the persons involved might lose interest. On the other hand, if the response is too fast and the pace becomes too high, the result might be that the system runs away, producing unwanted effects. Clearly, the timing aspects of EISs are important. If the timing should be considered fast (or slow) depends on what the users know about the system. Other factors impor-tant in considering timing, is if the system is synchronous or asynchronous. Real time is also an important issue. What is to be considered real time and what is not?

Traffic patterns The traffic in an EI system will create patterns. Different systems and applications can create different traffic patterns. The composition of the traffic, the amount of data, variation and symmetry are examples of properties that can identify pat-terns. Which categories of patterns can be identified and how can they be han-dled? Can the traffic be altered to create, remove and/or change the traffic patterns in a system? The ability of different technical solutions to handle different traffic patterns is another issue.

User AwarenessThe user perspective of awareness deals with psychological and cognitive aspects of users’ awareness, for example the level of awareness of users participating in an EIS, and the level of awareness of their possibility to affect, etc. Important issues are to identify and characterise levels of user awareness, maybe create tax-onomies.

14 Emergent Interaction Systems

4. Emergent Interaction Systems

An EIS is an environment with a number of actors who share some experience/phenomenon, and whose behaviour is significantly influenced by a shared feed-back loop picking up data from the individuals and their actions. From that per-spective, emergent interaction is not new as a phenomenon. The next section, Application Areas, gives some examples of existing EISs with no or little involve-ment of data communication and computer controlled feedback. It also shows the great potential that sensor technologies, data communication, and computer-controlled feedback add to this kind of complex systems.

The purpose with this section is to discuss a high-level model of emergent interaction that covers the important aspects of emergent interaction and points to some open research issues related to the concept. The section is divided into three parts. The first part, A Functional Model of Emergent Interaction, analyses EISs in terms of a number of different logical units. The second part, Control System Perspective on EI Systems, makes some reflections on the similarities between control system and EISs. The third part, System Architectural Issues, demonstrates the openness of the model by giving two examples of system architectures.

Emergent interaction is a concept that appears to be related to ideas in many different areas, such as social navigation, communities and collaborative filtering; and many sciences, e.g. social psychology, ethnology, control theory, and comput-ing science study related questions. In this situation a slightly more formal model will be helpful for several reasons. First, it serves as a consistent conceptual frame-work for discussing emergent interaction, also across different disciplines. Second, it can be used to identify and discuss existing phenomena as cases of emergent interaction. Third, the design and implementation of EISs needs a basic model as a guide. Note that the functional model proposed here is not a system architecture design nor a model of the traffic pattern generated in EISs — the model leaves many of the aspects related to these issues open, serving rather as a framework for discussing those aspects.

A Functional Model of Emergent InteractionIn a typical EIS, the actors and other functional units in the system are logically divided into a few distinct categories, e.g. the players, the referees and the audi-ence, etc., in an ice hockey arena. Another characteristic of EISs is that they are situated in a context. That makes it relevant to talk about EISs that interact. One consequence is that the boundary of a particular EIS, physically and logically, is rather pragmatically determined. A third characteristic of EI systems is their dynamic character: an EIS can be a part of some larger EIS, that also can be a part of something larger, and EISs are momentarily defined by which units and sub EISs that are up and running.

Our functional model of an EIS is displayed in Figure 2, and the logical units are explained below:

Shared Phenomenon, a (partially) shared and synchronous experience or phenomenon that has an existence independent of what the PID and the feedback system might deliver (but whose particular content and develop-ment in time may be affected by the emergent interaction). There is always, on some sufficiently general level some kind of shared ‘reality’, like the current traffic situation, a sports arena, a theatre, the weather or the stock market, etc. If the shared experience gets too thin, abstract, and/or incoher-ent, however, the whole set-up will become rather uninteresting.

The Actors are participants in a shared phenomenon from which data can be collected. The actors play different roles, some of them are more in focus and other act more in the background, e.g. the players and the audience. Their roles and participation may shift over the time.

Emergent Interaction Systems 15

Compileand

Compute

Administration

Externalinput/output

Actor

PID

Application

Feedback

Weightfunctions

Sharedphenomenon

f (...)

f (...)f (...)

Data

Weight Functions make it is possible to control the “importance” or the impact that an actor or group of actors has on the behaviour of an EIS. It is possible to apply weight functions f(…) to each actor’s data stream, and also make changes to them.

Personal Interaction Devices hereafter ‘PID,’ could be cellular phone, PDA, wearable computing technology, some remote sensing tracking technol-ogy, etc., in collective use. It is a fundamental part of the assumptions that several people use these devices.

Compile and Compute Unit hereafter ‘CCU’, is obviously a key parameter of the whole process. By choosing different functions to compute the feed-back, we would expect quite different effects (including failure to produce any significant effect). The informed choice of what information to feed back should be a basic step towards predicting and controlling emergent interaction towards desired overall effects.

The data can be data from the use of the PID (for some other and primary purpose), it can be more elementary personal data such as position data and biosensor data, and it can be data explicitly and deliberately input or captured by the actor for the very purpose of the EIS. Environmental data not stemming from the individual actors can also be used. The figure above shows these two possible sources, data going from the PIDs and from the shared phenomenon itself to the Compile and Compute Unit.

The feedback can be a shared presentation (in any kind of medium). It can also be some other kind of result or change in the shared phenomenon that is collectively valid. That means that the result is objectively the same for any individual and not tailored to specific individuals as such. The ulti-mate effect (importance, meaning, consequences, etc.) of the result on the individual may however vary and be context dependent. As a simple exam-ple, you might be able to directly experience (parts of ) the result only at certain locations and orientations, so you will miss out on (or be spared) that (partial) result unless you happen to be there or you move to that area and face that direction. That kind of circumstantially selective effect may clearly be an important part of the process.

Administration facility gives the potential to start up, maintain, and close down the system, but also gives the potential to control the behaviour in an EIS. The control can be in form of restraining or emphasizing the input from individuals or groups. A particularly simple way of manipulating the feedback function is to adjust individual inputs with different weights.

External output is the output of an EIS to some other EIS; this kind of data feed to the outside world can be broadcasted or directed.

External input comes from other EISs in the surroundings, directed or broadcasted, requested or not.

Figure 2. A functional model of an emergent interaction system.


Application is a system set-up dedicated to some high-level purpose or event, e.g. theatre, interactive art, etc. Section Application Areas, discusses different applications and identifies nine groups of emergent interaction applications. There are several parameters that can be used to characterise an application, such as openness, software, kind of sensors, distribution (in time, space, and among individuals), timing, security, integrity, etc.

Control System Perspective on EI SystemsFrom a technical point of view an EIS has much in common with a control system for the process industry. Of course the behaviour of systems where humans and interaction between humans are in focus are far more complex, with a larger dose of indeterminism. The intention with running an EIS where the emergence of the unexpected is in focus is very different from the intention with running control systems for a process industry where the controlled process is in focus. Still, in discussing EISs and building a functional model EISs, there is much in the notion of a control system that is useful.

“…understanding emergence has always been about giving up control, letting the system govern itself as much as possible, letting it learn from the footprints”(Johnson, 2001)

It is a well-known behaviour for most systems that the value of the output, at a given time, not only depends of the value of the input at that time, but also depends on earlier values of the input. Systems that “remember” old inputs are called dynamic systems. A static system on the other hand means that the output only depends on the current input.

A common definition of the regulation problem is “to decide, with knowledge of the output, an input, such that the system’s purpose is fulfilled”. But what is the purpose of the system? Well, the “system’s purpose” can be very hard to define. Take a minute and think about the economical system of a country. What is the purpose of that system?

The two most common ways to define the purpose of a system is, the servo problem and the regulation problem. In the servo problem the goal is for the output to follow a given reference input. In the regulator problem the goal is to keep the output at a constant level.

In any case there should be some kind of feedback to form a closed loop system. The feedback could be either positive or negative. Positive feedback means that a piece of the output it brought back to the input, adding to the original input, resulting in a drift of the output towards a maximum level or even start-ing an oscillation. Negative feedback on the other hand can be used to stabilise a system. The system can be a first-order or a second-order system. It can be over-damped or underdamped. Both transient responses and steady-state responses can be relevant parameters to think about and measure. Timing is important in a con-trol system as well as in an emergent interaction system. For more information about control systems, see (Doeblin, 1985).

System Architectural IssuesThe functional model leaves many options open for the system architecture design. The same model can be implemented using several different architectures. That leaves many other issues are open as well, since the architecture affects traf-fic patterns, system awareness issues, etc. In order to highlight the openness of the model and to emphasize some critical issues, two architectures are shortly described below: the ad-hoc architecture (decentralised) and the client server architecture (centralised).


Basically, an Emergent Architecture (EA) is compiled of autonomous units (more or less centralised); each unit is dedicated to some high-level function. The basic functionality that every unit has is the potential to interact with other units in their surroundings. The ad-hoc architecture has one-to-many communication as the main principle for the interaction. A client server architecture has one-to-one communication as the main principle for the data collection and one-to-many communication in the feedback (see the discussion in section Technical System Aspects concerning the communication aspects). In addition to the interaction potential, the units have a high-level functionality that is a combination of basic functionalities, such as:

• Sensor/measure

• Presentation

• Compile and compute

• Administration

The emergent interaction concept has a potential to open up a new market, a market for emergent interaction units, such as personal devices/tools, presentation units. Below is a list of examples of such units. PIDs – are basically units with a combination of sensor and presentation functionality, aimed for the personal interaction. The PIDs, especially in ad-hoc solutions have some level of a compile and compute functionality that makes them more autonomous. Public Displays – are units with presentation functionality, aimed to present public information. It is possible to imagine intelligent displays with a compile and compute func-tionality added, especially in an ad-hoc architecture. Status units – are basically units with sensor functionality, they measure and report some aspect of the system status. Application Server Units – are basically administration units for EI applica-tions. An application server serves actors with information about kinds of applica-tions possible to join and application software general and dedicated. One such unit can administrate several applications. Bridge Units – are used to bridge spatial distances in EIS, and to connect one EIS with other EISs. They have very simple compile and compute functionality. System Awareness Units – are basically units

Figure 3. Examples of EIS archi-tectures. Left: An ad-hoc decentral-ised architecture, with a very loose infrastructure for the communica-tion. Right: An Emergent System based on a centralised architecture, where all the communication goes via a central CCU.

with a compile and compute functionality, they are aimed to keep track of the behaviour in the system, or part of the system. History is also a function of the system awareness units.

The simplest way to explain ad-hoc architectures is to say that they copy the LEGO™ concept. They are architectures with a very loose infrastructure, but a well-defined interface or protocol that gives a high level of flexibility. There are many interesting research issues for this kind of architecture, e.g. to study traffic


patterns in order to make the communication more efficient, security issues, etc., but the most outstanding issue is the design of the protocol.

Client Server architectures (CSA) are more traditional architectures for distrib-uted system. The main idea in this kind of architecture is to centralise the intel-ligent functionality in the system to one unit, and to have dummy clients that interact via this unit. That means that all sensors, PIDs, and feedback units, in an EIS talk with each other via a central unit, and the compile and compute func-tionality is also in this hub (see Figure 3).

Design Space of EI SystemsThe emergent interaction concept and the functional model of emergent inter-action systems imply a systematic approach to a broad range of applications in which interaction between the collective and the individuals is a common theme. New computer, communication, and interface technologies give us entirely new possibilities to design EI systems. In fact, a vast design space opens before us:

• First, the amount and variety of data that is possible to collect and the speed of collection are radically increased. There are now sensors for almost any basic physical variable, and we can make massive, real-time and continuous data collections to maintain very complex models of the environment.

• Second, the feedback loops can be speeded up many orders of magnitude, and can be made to match the ‘natural’ time scales of individual and col-lective behaviour. The computerised CCU makes it possible to control and dynamically vary response times.

• Third, manipulation of the weight functions gives a relatively simple way to control the behaviour of the system. For example, amplify the feedback from quiet individuals in order to calm the collective.

• Fourth, the computerised CCU offers completely new possibilities to design and control the feedback function, without any real limit to the complexity.

• Fifth, continuously updated environment models can be used to study and simulate collective behaviour. In some situations one may choose to run the system in simulation mode, switching off (parts of ) the data collection, e.g. to avoid a system break down because of traffic overload.

Time

Level ofcentralisation

Size

Centuries

Instant

Day

Singleindividuals

Earth's �population

City, region

Centralised

Decentralised

Figure 4. The time, size, and decen-tralisation design space.


This makes for a highly complex design space. We may try to bring some order into this space by distinguishing different dimensions, different design parame-ters, such as time, size, democracy, technical solutions, etc. Even though the scope of this report is very broad – twenty-one focus areas described in the previous sec-tion! – many dimensions of the design space are just mentioned in passing, and surely some are not even mentioned.

Figure 4 highlights three (rather coarse) design dimensions of EI systems, which are important from a model and system architecture perspective:

• Time — the response time of the feedback, the speed of emergence, the speed of pattern propagation (if there are such phenomena), the time-span of an event are some of the factors having to do with time. Although perhaps not necessarily correlated with each other, we have simplified and collapsed them into one dimension; intuitively, an EIS can be fast or slow.

• Size — the number of actors, units, the area spanned by the shared phe-nomenon, the actors, etc. Again, several measures are collapsed into one.

• Centralisation level — the compile and compute function can be com-pletely centralised; the other extreme is to let it be distributed over all the actors and units in the event.

We note that the conditions for a market for EI units discussed above are totally different for the ad-hoc architecture approach and the client-server architecture approach. The ad-hoc situation implies a market built up around standard proto-cols for the interaction between the units, i.e. a LEGO approach. The client-server situation implies a market built up around a fixed infrastructure, i.e. a system design approach. Where the ad-hoc situation implies an open and flexible market, like the market for web-application based on the http protocol, the client-server situation has more in common with traditional design of information system. Irrespective of the system architecture, however, the design of a protocol for the interaction between EI units is a central research issue.

20 Application Areas

5. Application Areas

One purpose with this pre-study has been to discuss and suggest applications of EISs. Several activities have been aimed to that purpose: a brainstorming session on existing examples; a workshop focused on possible applications/prototypes to implement; and a brainstorming session on requirements for implementation of hypothetical prototypes. This section summarizes these activities. First, we discuss some existing examples of EISs. Then, some ideas for new implementations of EISs are introduced, and the question of how to evaluate these proposals is dis-cussed.

Existing ExamplesIt is possible to identify a number of examples of emergent interactions in our daily lives. In many of these examples the compile and compute part plays an inconspicuous role and the data is not collected under controlled forms. The fol-lowing list is far from a complete list of existing EIS; it serves more to give an idea of the breadth of the concept. In order to study the impact and the conse-quences of the introduction of computerised emergent interaction artefacts, these examples can serve as starting points for construction of scenarios. Each example is illustrated with an instance of the high-level model from previous section. An attempt to visualise the placement of the example into the time, size, and level of centralisation dimensions of EISs complements the model instance.

Go with the FlowIn today’s industrialized world where almost every teenager has a mobile phone, which they use to interact and communicate with their friends, information trav-els fast among groups of youths. It is now possible for them to have an idea of what it is going on in a much larger area than before. One effect is that youths at youth centres tend to go with the flow as movements become more “visible” to them, and it has consequently become harder for the adult community to have control over the situation. This system is distributed with a very decentralised compile and compute, and with no external control (see Figure 5). We note that this kind of phenomena has some characteristics in common with ant algorithms and similar computational mechanisms (Bonabeau, Dorigo & Theraulaz, 1999).

Figure 5. Having the control of the overall situation.

Time

Size

Level of centralisation

CenturiesInstant Day

Single individuals Earth's populationCity, region

Centralised Decentralised

Application Areas 21

The Democratic ProcessThe political system in a democratic society is a good example of emergent inter-action, with many kinds of actors participating (politicians, different pressure/interest groups, ordinary citizens, etc). Politicians and the rest of the society inter-act via a shared feedback loop, e.g. politics - public opinion survey - politics- public opinion survey - politics - elections - politics - public opinion survey - … Public opin-ion surveys and elections have the compile and compute functionality. They may also produce feedback direct in form of statistics etc, but also more indirect in form of new political constellations and activities. The shared phenomenon in a democratic process is the democratic society. Concrete results such as new build-ings, roads, laws, etc are a part of it. The laws rule a democratic process, and the laws and the control mechanisms for the laws serve as the external control of this kind of system (see Figure 6). Moreover, democratic processes are not isolated entities. They are situated in a larger context in which they interact with other processes. Political trends and how they spread in the world community exemplify this.

Web-based Recommendation SystemsCollaborative filtering or recommendation systems like those on web shops, e.g. amazon.com, MovieLens, alexa.com, etc, serve as examples of another kind of EIS where the physical and time distances can be extremely large. The system tracks the articles that different customers buy, in order to present this information for other customers buying the same article. E.g. some people that buy Astrid Lind-gren’s “The Tomten” also buy Elsa Beskow’s “Peter in Blueberry Land”. Another form of data collection is the possibility for the customers to rate the articles, which is used to compute an average rating for the articles in the shop based on the customers’ judgement. Characteristic for such applications beside the large distances between the different actors in the system is that very much of the system is in a virtual/digital world, which implies that it is relatively easy and cheap to collect data, compile and compute, and produce feedback in a centralised way (see Figure 7).

Figure 6. The democratic process in a community.

Time

Size





Time

Size





Figure 7. Virtual application.


Sports arenaA sports arena with a sport event in focus and several kinds of actors (players, referees, spectators, coaches, etc) all with their specific roles in the arena is also a good example of an EIS. More or less every actor in a sport arena adds something to a shared phenomenon via a shared feedback loop.

• The audience reacts to the actions of the players, coaches, other spectators, and referees.

• The players react to the actions of coaches, referees, audience, other play-ers, etc.

Basically, every actor in a sport arena has some level of the compile and compute function. Further more, the CCU is traditionally focused on the interaction between the referees to the audience via the speaker, e.g., which of the players that scores, the reason to cancel a goal, etc. The speaker takes part of the referees’ interpretation of the game and from that produces a visual and/or audio feedback to the other actors in the shared phenomenon. It is also possible to identify actors taking a more salient role of the compile and compute task, e.g. cheer leaders, media actors, etc. (see Figure 8)

Other examplesThe examples above give an impression of the wide scope of emergent interac-tion, with one example from the digital world (Web-based recommendation sys-tems), one from a mix of digital and physical world (the democratic process), one from the world of sports, and one example of a social phenomenon (the youths example). The four examples share many characteristics; they also differ in many characteristics. A democratic process is quite slow compared to a hockey game. A web-based recommendation system has a quite explicit purpose of compiling and computing information from the participating actors in the system, in contrast to the other three. It is an important research issue to identify useful characteristics of EISs. Such list will be a valuable tool in the design of EISs as well as for the evaluation of EISs.

Time

Size




Centralised DecentralisedFigure 8. Sport events.

Time

Size




Centralised Decentralised Figure 9. Knowledge creation


It is possible to imagine other examples that illustrate the extremes of EISs. E.g. the view of knowledge as a social construction (the view of a common phenom-enon, such as the conception of the world) is an example of an extremely slow EIS. It also exemplifies an EIS where the feedback is just distributed to the actors and does not affect the shared phenomenon directly, which is quite opposite to the democratic system example (see Figure 6 vs. Figure 9). Dance floors, traffic systems, theatre events, exhibitions, music festivals, and aqua parks are all example of systems for which the emergent interaction model is relevant.

New ImplementationsIn some sense, the media interest for MRC’s IT-hockey project and similar projects (articles in newspapers and more technical journals) may be taken as a sign that the time is ripe for EI systems. One goal of this pre-study was to suggest EI applications suitable to implement as prototype systems. A number of brain-storming sessions has been the main activity in this invent phase. The result is about seventy ideas (see Appendix A, for the results from these sessions), which have been reviewed and compiled into nine categories:

• Knowledge Society Events

• Exercising Events

• Home Activity Events

• Public Space Events

• Professional Events

• Political Events

• Communication Events

• Arena Events

• Community Events

The key ideas in each category are discussed below. Identifying pros and cons with each application idea was part of the brainstorming sessions. The discussion can be summarised in terms of a number of key ideas and four different types of aspects: effects, implementation, values, and security. The results from these activities serve also as input to a discussion about the feasibility and usefulness aspects of EIS reported in section Pre-study Outcome.

The effects aspect concerns how the EIS affects the actors, environment, soci-ety, etc. These effects tend to be rather specific for a particular application. The implementation aspect concerns the possibility to implement EI applications: technical demands, how difficult it is to find a test group, how difficult to collect and interpret data, etc. Also these issues tend to be rather specific for the applica-tion, but not as specific as the effects. The values aspect is a bit more general: is it good or bad? fun or boring? easy or hard to get financial support? etc. The security aspect is about taking care of privacy, security, and integrity. These issues are quite general, especially in their negative aspects.

The rest of this subsection is dived into three parts. The first part makes a walkthrough of the application ideas from the brainstorming sessions. The second part discusses various aspects of emergent interaction applications. The third part discusses in quite general terms how to handle the negative aspects of EI applica-tions.

Ideas for EI PrototypesWe have identified nine clusters in the seventy proposals for EI applications. The resulting categorisation should be viewed as a provisional mind tool for thinking


about EIS and not as a definite way to categorise emergent interaction events; some of the proposals may fit multiple categories. The aim with this section is to describe the categories, give some examples from each category, and present some possible positive and negative aspects for each category of events. The tables in each of the clusters summarise the results from the brainstorming activities. They should obviously not be taken as complete and reliable lists of relevant aspects of EI, but are still useful as a first indication of plausible beliefs about emergent inter-action applications. There is much work to be done to make these tables more complete and to verify what peoples in general really believe about these things. Obviously, still more important than what people may think about these hypo-thetical systems in advance is what the objective effects and the objective require-ments and consequences with regard to implementation, values and security are in the fully implemented and working system.

Knowledge Society EventsWe view much of our known knowledge as quite stable entities, and we call them facts. One important aspect of these facts is that they to great extent have emerged in the society as a social phenomenon. It is easier to see that knowledge is an emer-gent social phenomenon if weaker knowledge constructs such as notions, under-standings, views, etc. are taken into consideration. E.g. our conception of the world has developed from the belief that the earth is the centre of the universe to the view most of us have today that earth plays a very minor role in the universe. Our knowledge about the universe has not been fixated, however, cosmological research is a very active field and our conception continues to change. On a more individual base, a view of knowledge as something that emerges over time in an individual’s interaction with a community is fruitful (Broberg, 2000). In short, knowledge can be considered to be a social entity (Vygotsky, 1978). The systems in this category are aimed to support the emergence of knowledge on the indi-vidual level or on the society/community level, or both. Some examples of proto-types from the workshop activities are classroom situation, knowing what one knows and doesn’t know, what is hot or not in a research field, and Self-knowledge.

Positive aspects Negative aspects

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+ Can make the classroom more appealing+ Experience/ event+ Quality+ Wake up the students+ Involvement+ Effectiveness of resources+ Possibilities to stop harassment, bullying

+ Knowledge is an emerging and social phenomena+ New phenomenon/behaviour

_ Brainwashing_ Stress

_ Dangerous to lose the control of the system_ Integrity_ Privacy issues Table 1. Aspects of Knowledge

Society Applications.

Exercising EventsPhysical exercise in a group (spinning, aerobics, dance, etc.) is a quite common activity in today’s modern society. The idea is to make exercise less boring and more enjoyable; to enhance the experience by doing it together. It has been shown in social psychology research that the individual’s performance of quite individ-ualised activities like biking, solving crossword puzzles, etc., typically increases when done collectively, even without any direct help from each other (Asplund, 1987). The interaction in the collective is through a shared feedback loop, in


some kind of social interaction. In the case of physical exercise there is often a dedicated person (leader) having control over the feedback loop. What we have here is essentially an EIS, but without the involvement of computerised compile and compute, data collection, and feedback. The question is if computerisation would make it possible to enhance the experience even more. The proposals in this category are aimed to enhance the experience, and to widen the concept of collaborative exercising. Some examples are spinning, aerobics, dancing, and swim-ming.


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+ More effective+ Easy to enhance the experience+ Pepping+ Feedback to the leader+ Good for your health

+ Atmosphere+ Cool

+ Categories+ Profiles+ Clear border+ Closed community+ Distributed+ Dynamic+ Nearness+ Easy to measure+ Often regular activity+ Receptive persons+ High acceptance for data collection+ Social activity

_ Risk for self oscillation_ Unpredictable

_ Hard to start_ One-track_ Short-term_ Too narrowed activity_ Not everyone is doing it_ What to measure_ How to give feedback

_ Hard to replace the human leader_ Removes traditionTable 2. Aspects of Exercising

Applications.

Home Activity EventsA large part of life is spent at home. A rough estimation is that we spend at least 50% of our time in our homes eating, sleeping, cleaning, watching TV, etc. Most of these home activities are quite closed; i.e. we do them alone or in small groups in privacy. The key idea for the applications in this category is to support the emergence of communities focused on home activities: i.e. to create applications aimed to make them easier, more fun, feel affinity, etc. Some examples are eating dinner, 9 o’clock in front of the television set, sleeping and home economy administra-tion.


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+ Positive influence on what people are eating

+ Not yet IT-polluted

+ Common experience+ Everyday activity for everyone+ Many social navigation possibilities+ Social activity

_ Somewhat hard to measure (taste)

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Table 3. Aspects of Home Activity Applications.


Professional EventsWe have entered the knowledge or information society, a society where more and more labour has to do with knowledge, e.g. communication and refinement of data and information. Journalists, stockbrokers, designers, architects, and researchers exemplify the growing category of knowledge workers. Knowledge work tasks also become more and more involved in all kinds of work (Fällman, 2001). Knowledge work typically has the very useful and appreciated property that it can be distributed in a way physical work often can not. We may safely assume that IT will continue to be used to cut the costs for travel, to cover larger areas, to bring together widely dispersed competencies and resources, etc. As a result the physical distance between workers may increase, with fewer face-to-face contacts. Therefore, there is a need for applications supporting people’s sense of presence over a distance. The applications in this category are aimed to support activities associated with professional activities. Some examples are meeting, fac-tory floor, problem solving and design and empathy training/development.


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+ Effective use of resources+ Possible to speed up time+ Solves the lack of empathy in society+ Creates knowledge about forests+ Experience/event

+ Focused on human needs+ Humanity

+ Safety

+ Easy to collect data+ Easy to measure data+ Easy to create adrenaline shocks

_ Hard to interpret data

_ Not any obviously emergent interaction in the application

_ Stress

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Public Space EventsEven if we spend most of our time in quite closed communities like families and work teams, there is still time left for other activities. Some of them are things we need to do, such as shopping, waiting for a bus, etc. Other activities are for pleasure, such as visiting pubs, restaurants, exhibitions, etc. There are questions of rationality and effectiveness in both types of public events such as to find out where to go to find the cheapest, most attractive products or services as effectively as possible. On the other hand there are also questions concerning communica-tion, meetings, and beliefs: e.g. what is hot in an area? are there any people with similar interest as me here? etc. Applications in this category are aimed to sup-port this kind of public space activities. Some examples are exhibitions, atmosphere detection, pubs, restaurants etc, silent areas and parties.

Table 4. Aspects of Professional Applications.


Political EventsPolitics or the political process in a democracy is given as one example of an exist-ing EIS. Politicians, political parties, media, public opinion survey makers, and citizens are all actors in the system. A fundamental requirement is that all indi-viduals have equal rights to communicate their opinion, within a given framework of rules. Another requirement is that there is an apparatus to control that every one is following the rules. The applications in this category are aimed to support the democratic process, both communication activities and the control apparatus. Some examples are demonstrations, IT-democracy, avoid/track crime activities, and war.


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+ Effective use of resources+ Spread load+ Better price+ Faster + Winning time+ Quality effects+ Environmental+ Experience/Event+ Opportunity to make new acquaintances

+ Humanity

+ Safety

+ Great diversity of people + Individualised+ Common intentional+ Not common intention+ Neutral (nobody owns the territory)

_ Drunk people might ruin the equipment (puke warning)_ PDA is needed_ Willingness to carry the sensors_ Too fast

_ Boring application_ Destroys the event_ Competition problems_ Whom does it benefit?_ Wrong target group_ Small target group

_ Dangerous to lose the control of the system_ Unwanted effects_ Less development and considerations_ No spontaneity _ Segregation_ Standardisation_ Stress

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_ Integrity_ Privacy_ Unclear who should have the controlTable 5. Aspects of Public Space

Applications.


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+ Democracy+ Organisation + Less violence + Control the situation

+ Economy

+ Easy to measure+ Everybody+ Right equipment+ Surveillance

_ No surprises

_ Does it work, is the result a better democracy ?_ Crowds_ More violence_ Escalation_ Injustice_ Mobbing

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Table 6. Aspects of Political Appli-cations.


Communication EventsIt seems that we human beings have a basic instinct to communicate with each other, in a broad sense (Aronson, 1995; Asplund, 1987). There are many tech-niques that serve to extend our possibility to communicate. Most of these techni-cal artefacts are designed to shorten or bridge different types of communication distances, and some may be intended to completely eliminate distance. Com-munication distance is more than kilometres (Broberg, 2001). distinguishes four kinds of distances in learning situations: time, space, way of learning, and distance to relevant material. This can be extended and generalised to communication in general, taking also cultural, sociological, and linguistic distances into account. The applications in this category are aimed to support shortening and bridging of distances. Some examples are car-pooling, tourism/guiding, implicit traffic control, and extended video/phone-conferencing.


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+ Effective use of resources + …of advantage to society+ Democracy+ Distance spanning+ Economy+ Environmental+ Possible to compare groups in similar situation+ Commitment

+ Easy to find money for+ Humanity

+ Safety

+ Huge population+ No one can escape+ Nothing else happening+ Repeatable

_ Demands on capacity_ Not in Umeå_ Computational problems_ Too big

_ Not something that is new_ Boring, grey

_ Interference_ Stress

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Applications.

Arena EventsHumans are social animals (Aronson, 1995). Our basic instinct to do things together is manifested in small, intimate groups such as a family, as well as in large gatherings in venues such as theatres and sport arenas. Many activities in large arenas have to do with entertainment in some form, but there are exceptions from that, e.g. political rallies. The applications in this category are meant to extend and enhance arena events as we now know them, for example making the audi-ence more active or participating, or widening the arena into the virtual world. Some examples of applications are theatre, track and field, docu-soap like Survivor, and collaborative art.


Community EventsThe society consists of communities (subgroups or subcultures), that is, people with something in common that brings them together. That something (hockey team, a hobby, political ideas, the work, etc) is in focus for their various activi-ties, such as meetings, exchange of advice, communication, giving support to a team, etc. Such lists of community activities can be very long. The applications in this category are aimed to support activities intended to increase the engagement, attendance, kinship and similar qualities. Some examples are hunting, instant com-munities, gardening and pets, and social life, create new contacts.


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+ Can create more interaction+ The public (as paying) is allowed to decide more+ Easy to please+ Enhances experience+ Experience/event+ It would help the drivers, the teams at the pit stops+ Fewer extra tracks+ Less tomatoes thrown

+ Collaboration with (Strix)+ Commercial interests+ Enormous market+ Maybe a growing area+ Money+ Fun+ Hot+ Humanity+ Tokyo as a lab (NTT DoCoMo)+ Playfulness

+ Already based on interaction+ Many existing concepts to expand+ Closed area/local+ Easy to find test pilots+ Everyone is a participant, 24-7+ Brainstorm with children/youths

_ Few events_ Once a year event_ Very few events_ How to charge?_ Loads of technique

_ Difficult to understand the market_ Who profits?_ What is new?_ Does it already exist?_ Just a game_ Expensive prototype_ Expensive system_ Difficult industry ( TV )

_ Demands that you engage in the play_ Dependent of the audience_ Huge demands on the actors_ Does it have any positive effect?_ No new songs_ We don't want to choose

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+ The group could later on be used to: send out information requests reminders+ Groups could be created (?) recreated or consolidated afterwards from logged data (who was there and personal profiles) when a need is identified

+ New+ Cool+ Business and pleasure + Young market

+ Individualised+ Scalable

_ Where to find the people_ Critical mass_ Hard to get started_ What terminals to use?

_ What do they have in common?

_ Uniform

Table 9. Aspects of Community Applications.


General aspects of Emergent Interaction ApplicationsWhy should we develop EI applications? There is an unspoken need for some-thing to show to the world*. On the other hand there is a need for a good platform for testing important concepts. At first glance, this may seem to imply a balance act between the “just a game” application and the “boring” application. There is also a balance act between innovative thinking to enhance existing events or create a new type of event, and the risk of destroying (the existing values of ) an existing event or previously existing related events.

The above walkthrough lists positive and negative aspects for each of the nine categories of EI applications. More general aspects have also been discussed during the project. The purpose with this part is to compile all discussions on aspects into a more general tentative assessment of EISs. One result from this work is a list of prototype requirements, designed to guide the choice of prototype in the next phase. These prototype requirements are presented and discussed in the last section in this report, Pre-study Outcome.

The general discussion has identified one important purpose with the con-cept: to increase or enhance social life with participation and engagement in some sense, and to provide new ways of forming acquaintances. To build prototype sys-tems of this kind based on emergent interaction creates an attraction and makes the concept more concrete and visible; of course, there is also a general feeling that EI applications are cool and trendy.

The realisation that there may be some sinister similarities between EI applica-tions and the scenario of George Orwell’s 1984 puts the finger on the main weak-nesses of the EI concept - the secrecy issues, the integrity issues, and the risk for breeding and amplifying negative tendencies in society. The general discussion has also identified a number of other worries. First, there is a fear that EI will contrib-ute to a more uniform society, i.e. a situation characterised by flock behaviours, lack of individuality and personal initiative. Second, it seems to be complicated to implement EIS technically; e.g. many of the ideas seem to assume wireless com-munication techniques, there are many completely new and untried ideas for the compile and compute stage, as well as a heavy reliance on new technology in gen-eral. Third, the complexity of the systems is quite high, escalating the risk of oper-ational disturbances. Fourth, there are worries about the level of radiation with radio-based wireless communication techniques and sensors everywhere. Fifth, and finally, we do not want boring applications for nerds only; on the other hand, we do not want to launch a prototype implementation project that does not give a serious impression in the eyes of the public. The rest of this subsection is an attempt to compile the directed discussion to a general level. The aspects related to each of the categories confirm the general feelings about EI. In addition these category-directed aspects extend the positive feelings of the concept.

Discussions About the Effect IssuesThe effect point of view raises concerns about the effect that emergent interac-tion application can have on individuals but also on the collective. First, many of the proposed applications are aimed to utilise resources in a more effective way, e.g. saving time, better prices, spread the load, etc. Second, another category of applications is aimed to more qualitative effects both on the personal level and on society level. For example, put a stop to harassment, preserve and improve health, create a better understanding for a phenomenon, positive effects on the environ-ment, etc. Third, emergent interaction has the potential to enhance an event or experience of a phenomenon. For example making the class room more appeal-ing, distance spanning, increase the social interaction, etc. Fourth, there is a group of ideas serving to strengthen the democracy process. For example, the citizens may have new ways of affecting the democratic process by just acting; have or take control over violent situations implying less violence; etc. All these things must be classified as positive effects.

* This is true for all research and development projects, this is true also for a R&D project on emer-gent interaction. Basically, this may be important for future funding of the research and development, but also in order to commercialise the concept.


On the other hand, for many applications, some unwanted effects can be iden-tified as critical; losing the control over the situation (resulting in an escalation of negative effects such as violence, mobbing, stress, chaos), system breakdown, spreading effects beyond the scope of the system, etc. Another cluster of negative issues can be summarised as a general scepticism about workability: whether it really will work, whether it will have any positive effects at all, whether it will be worth the trouble. Finally, many of the proposed applications put new demands on the actors in the system such as more engagement, having to handle new situ-ations, coping with more interference from the environment.

Discussions About the Implementation IssueThe implementation point of view raises concerns about how easy it is to get started and what kind of activity should be in focus for the application. In that sense the implementation aspects are typically directed towards the adequacy of the particular idea as a prototype. Hence, these aspects are useful for setting up requirements for prototype implementations, (see the discussion in last section about prototype requirements, page 65). In the longer perspective these imple-mentations issues are a good tools for judging the adequacy of implementing ideas to full-scale applications.

The kind of event plays an important role for the difficulty, effort and ade-quacy of implementing an application. Applications focusing on emergence and social phenomena like knowledge and democracy, i.e. applications with a focus on human needs or the good for the society may be more relevant to implement than a “just for fun” application. There is a hard balance act between these two aspects, especially when concerning the positive aspects of the market values, such as the potential to find a financial partner to collaborate with. For example, an enormous market or a young market with a potential to grow will increase the financial potential. Hence, applications associated with that kind of values are rewarding to develop. On the other hand, there are other values not immediately related to the economy that are important to have in mind in the balance act between utility and attraction value. For example, is the event already “IT-pol-luted” or not? Does the application combine business and pleasure? Would an implementation bring something special?

For a good implementation project, the application must not be too narrow, and it must not be too wide. A too narrow activity means that it will be hard to reach the critical mass for the application: the necessary facilities may not exist in the near surroundings; it can be hard to find enough people willing to carry sen-sors and other necessary equipment. The continuity or frequency of the event is also important for the possibility to run an implementation project.

The character of the room (room in a quite broad sense, including both spatial and social aspects) of the event also plays an important role for the implementa-tion. It is easier to implement a system if the activity takes place in a closed room than in an open public space. On the other hand the ownership of the room also affects of the possibilities to implement a system. A closed room often has a clear ownership that can make it easier to get money for the project but may constrain the application. A public space with a weaker ownership can give more freedom in the choice of application, but can make financing a more intricate task. A closed room makes it possible to involve all actors in the room, and is hard to escape from (in a positive sense). Of course there are lots of activities not related to a particular closed room, in which “everybody” or very many are involved, e.g. every day chores in the home, shopping, etc. The closeness also implies that nothing else important is happening in the room.

A proper feedback mechanism is crucial for every EIS. The relative difficulty and effort of implementing the feedback loop (including the data collection, com-piling and computing, and presenting the data), technically as well as socially, must be considered during the early phases of the design of applications. There are more concerns about the feedback mechanism. First, a high acceptance for


data collection among the participants is an important factor. Second, the measure issue: it could be hard to identify what to measure, and it may be hard to do the actual measuring of important parameters of the activity. Third, even if we can get data from the activity it can be hard to give it a relevant interpretation. Fourth, to design the feedback is not a trivial issue - exactly what the feedback should be is in many applications an open question. It seems that many of these feedback loop requirements are more easily satisfied for a closed room activity than for a public space activity. Fifth, in many systems, there is one person or a small group of individuals having the compute and compile function, and it may be undesir-able to replace humans with a computerised functionality.

Most of the proposed applications are quite complex systems with heavy demands on technique, and many of the proposals are in public spaces or quite open spaces - thus raising questions of how to protect the equipment from being stolen, destroyed, etc. Considering the complexity of EISs we may have worries about the implementation costs. In the long run it is necessary to take market aspects into consideration: whom does it benefit, who profits, how to charge, competition problems, etc. Another question related to the cost aspects that needs to be considered is what terminals or technologies to use.

There are many interesting concepts and research issues related to emergent interaction, e.g. social navigation issues, scalability issues, new technical solutions, etc. The adequacy of the application increases with its potential to expand and study EI-related concepts. This is also true in particular for ideas that are associ-ated with a purpose to cause new behaviours or phenomena.

Handling the negative aspectsEI systems are very complex systems. The idea of designing computerised EI sys-tems where the interaction between the individual and the collective is in focus is quite new. Together, these two facts imply a high risk of running into prob-lems with a prototype implementation, especially without a proper understanding of the potentially negative aspects of the application. The hazards, dangers and potentially negative effects are important to keep in view, to investigate and nego-tiate - not only for any particular implementation - but also and more importantly for long-term basic research on the operation conditions and behaviour of EI sys-tems, giving the basis for more predictable and safer applications. The purpose with this part is to summarise our discussion about how to address these misgiv-ings and negative expectations about emergent interaction.

The potentially high cost for implementing and introducing an EI system is a negative factor. We have considered what might be done to keep down costs. One way is to start with a small-scale or stripped-down implementation using already proven techniques. This would also give a smooth start on the project. E.g. it might be possible to skip the mobility issues in the early stages of an implementa-tion project. Another way to cut costs is to engage master thesis students in the project. This will have a dual effect: it is also important to introduce the EI ideas to the system developers of tomorrow.

Except for the worries about the costs and technical problems to set up EISs, the worries about security, integrity, and privacy issues draw much attention - people want to maintain some level of privacy, they are afraid of being logged, and they worry about the data being transmitted in a secure way. There are gen-eral concerns about losing the control over the system. There are concerns specifi-cally related to authorisation. Per definition we would not want an unauthorised person to take control over the system, but we can imagine situations where it would be unclear who would or should have control authority.

The “Big Brother scenario” for emergent interaction represents the most nega-tive beliefs about the concept and the project that we have been able to imagine. We have worked a lot to find ways of alleviating our own concerns and misgiv-ings (and thus preparing ourselves for the job of convincing others that there is


no real problem). The difficulties of solving problems with unauthorised control of the system can be illustrated with the fact that some of the methods we have discussed to solve the authorisation problem definitely contributes to the “1984 feeling”, e.g. censoring, filtering of data, tracing authorisation procedures, etc. It is important emphasise that there is nothing of the original intention with emergent interaction systems in this “Big Brother scenario”, which is a scenario that implies a centralised control apparatus for regimentation of the masses. Emergent interac-tion stands for the opposite of this scenario, i.e. individual thinking and initiatives taken from the considerations of the shared feedback.

On the other hand, these worries for a “Big Brother scenario” indicate a real risk for EI systems, i.e. the misuse of the basic technique, which is important to have in mind for the further development of the concept. Therefore, the need for good examples to show is very important, especially to clarify the intentions and to remove the cause for these worries. Engagement, together with ethics and moral must also be seen as key issues for preventing unauthorised control and misuse of the basic techniques for EI systems. Moreover, to inform all the partici-pants of the value of being in the system, and to be absolutely frank and forthcom-ing as to how it works is part of this work to establish trust for the application and the concept.

In summary, the main problem, we now believe, has much the character of an information problem*. It is always important to analyse and identify what the wanted effects are, and what unwanted effects could arise. This is important for all kinds of actors in the system. Therefore, it is necessary to develop methods for risk and consequence analysis for implementation of EIS and applications. Such method, must take care of what happens if the system goes down, is it a risk that an application can cause panic in some situations, is it risk for misuse of the system, etc. We believe that simulations can be or must be a part of this method. To verify, test, and develop the model are other open issues related to the model.

* As so many other problems with EI systems.

34 Technical System Aspects

6. Technical System Aspects

In this section system architecture, data communication, and traffic patterns are discussed from an EI perspective. Each system will have its own communication solution and one way to understand this is to analyse the EIS from a System Architecture view. System Architec-ture describes how a system is designed and constructed. The Data Communication in the EIS is another important area for discussion. We can identify several places in the EIS where there is a need to exchange data between nodes in the system.

If we analyse the logical EI model we find that all arrows in the model represent data exchange, i.e. Data Communication. Never-theless, not all EISs have the same needs and demands for Data Communications and therefore a wide range of communication technologies and solutions will be discussed in this chapter. Last, but not least, the data traffic generated by an EIS can be analysed to identify patterns, i.e. Traffic Patterns. A study of such patterns can give information about how a system works and how users interact with the system.

System ArchitectureThe purpose with this section is to give an introduction to the concept of system architecture and then discuss architectural aspects for EISs. System architecture is about identifying which components, logical and physical, a system can be con-structed of. The roles of each component need to be specified, such as the inter-faces, links and protocols used to communicate with other components. Issues such as scalability, centralised or distributed architecture, dedicated or public sys-tems, etc., needs to be investigated. Different architectures can be designed for different purposes, situations, and variants of EI systems.

There is no common definition of system architecture but in (Luckham, Vera & Meldal, 1995) the following simple definition can be found: “An architecture is a specification of the components of a system and the communication between them.” This definition is used in this section with the following additions:

• System Architecture is a high-level structure of a system.

• It is a level of abstraction from which the system can be viewed as a whole.

• All implementation details are hidden.

• The structure must support both functional and non-functional require-ments of the system.

There are a number of factors one should have in mind when discussing the architecture for a system. Functionality, the architecture must support the required functionality, i.e. what is the purpose of the designed system? Performance, it must give the system ability to meet the performance needs. Environmental, in which environment will the systems operate? Does the architecture support the imple-mentation of the system for different environments and contexts? Some environ-ments are more hostile or demanding than others. Different parts of the system are operating under different conditions. The same system may need to be able to operate under different temperature, moist, load, and current conditions, etc., depending on where they are deployed. Usability, how will the users interact with the system? Do we have the support we need to implement a system with appro-priate usability? Reliability, the system must be stable enough and able to operate under high workload for a sufficiently long time. Future Proof, the architecture could be prepared for future upgrades of the system. Production, if the system is

Technical System Aspects 35

to be a product it must be possible to produce and install it at reasonable costs. Budget, the system must be possible to implement within the available budget, time and economical resources. Who will implement the system? Maintainabil-ity, the system should be possible to maintain and administrate. This should be reflected in the System Architecture. Scalability, if the system is to be scalable this must be an issue to have in mind when designing the architecture. Understand-ability, it must be possible to understand how the system operates by viewing the System Architecture. The Architecture can be used when demonstrating scenarios for how the system will perform. It can also be a tool when analysing malfunction in the system. Available Technology, should the system be implemented using only available technology or can we wait for future solutions or do we need to develop something new? What technology is available today? (Nilsson, 2001).

Note that the System Architecture should be specified early. This will help to divide the system into logical and physical parts and make it possible to analyse how to implement each sub-system. It should also make it easier to decide if it is possible to implement the system or not and if the system can fulfil the require-ments.

We can have different levels of architectures depending on what we want to specify or analyse. An important requirement of System Architecture is that it takes an overall view of the system. Different levels of abstraction give different types of architecture. We can also analyse software architecture, network architec-ture, computer architecture etc. Some part of the system could be more advanced and complex than others and should therefore be analysed further by creating some kind of sub-system Architecture. This is the reason why very large systems have many levels of architectural descriptions.

There exist tools and concepts for the design of System Architectures. In (Luckham et al., 1995) three different concepts of System Architecture are pre-sented: First, object connection architecture, second, interface connection archi-tecture, and third, plug and socket architecture. Of these three the plug and socket architecture is the most interesting for EI systems. Useful tools when designing a system are Design Patterns (Gamma, Helm, Johnson, & Vlissides, 1995) and Anti Patterns (Brown, Malveau, McCormick, Mowbray, & W.Thomas, 1999). Design Patterns are known, proven solutions for common design problems, which can be used as templates when designing systems. Anti Patterns is the opposite: examples of common, bad solutions which one should avoid to use and also examples of how to avoid them. Both Design Patterns and Anti Patterns supply a common vocabulary and terminology, which makes it easier to describe and discuss the architecture of the system.

System Architecture for Emergent Interaction SystemsWhat architectures would be suitable for EI systems? According to the EI model an EIS seems to have a distributed System Architecture. Different types of units and interfaces can easily be identified in the model. Units: PID, CCU, Control unit, etc. Interfaces: PID – CCU, CCU – PID, Control – CCU, shared-phenom-enon – CCU, CCU – shared-phenomenon, EIS – EIS, etc. Note that most of the terminology used in this section was defined in the section Emergent Interaction Systems, sub-section System Architecture Issues.

These components and interfaces are all on the same level of abstraction in the EI model. If the resolution is increased we can se more components and more interfaces, e.g. in the PID the Sensors-PID interfaces and different units, which handles different interfaces. It’s all about resolution and level of abstraction. What level of abstraction is the right one? It depends on the purpose of the architecture. If the purpose is to show an overview of an EIS, a level that doesn’t show too many details in each unit is needed. If the purpose instead is to guide the design of the PID or the CCU the architecture must show these units in more detail.


In this pre-study we don’t need to go into details but if a system is to be imple-mented the Architecture should be carefully designed and analysed as early as pos-sible to ensure that the system is possible to implement. It is also important to verify that the architecture fulfils the requirements, and enables estimation of the work needed for the implementation.

There are many ways to describe the difference between two system architec-tures. For an EIS it is possible to identify many architectural characteristics that are important for the system’s possibilities and limitations. These are some exam-ples of such characteristics:

• Homogenous systems, all PIDs are of the same type or they have the same capacity and functionality. Only one communication interface is used by the PIDs in the system.

• Heterogeneous systems, the system supports different types of PIDs with dif-ferent capacity and functionality. More than one communication interface can be used by PIDs in the system.

• Centralised systems, the system is (functionally) centralised around a central CCU, which controls the activities in the system.

• Decentralised systems (partially or totally), the CCU is distributed in the system. More than one node has the CCU functionality. Those distributed CCUs can be standalone units or be included in some other system unit, such as a PID.

What are the maximum numbers of actors for different architectures? The number of actors a system is required to support is an important factor in the design of the system architecture. Different requirements on the available capacity per actor lead to different solutions. If the system is supposed to support a small number of actors the communication to and from each actor can be allowed to use a large part of the available network capacity. On the other hand, if the system must be able to support a large number of actors each actor can only use a small fraction of the available network capacity.

Example of Emergent Interaction System ArchitecturesIn this section some examples of different architectures of interest for EIS design are shown. The architectures shown are extreme solutions selected to illustrate the possible variation of EIS architectures. In the design of a real EIS more effort must be laid upon defining a suitable architecture that fulfils the requirements for the actual application. The range of architectural variation is illustrated in Figure 10, where the examples span the axes in a two-dimensional space where EIS architec-tures can be defined.

Small

Large

Centralised

Decentralised

Time

Level ofcentralisation

Size

Figure 10. Illustration of the variety of possible EIS architectures.


Centralised Architecture In this architecture all PIDs send their data to a central CCU. The CCU distrib-utes the feedback as a broadcast or multicast message to all PIDs and displays. This is a much more effective way of communication than if the CCU would have to communicate with all the PIDs one-to-one (unicast). The communica-tion needed for the feedback using unicast would be proportional to the number of units, whereas it only depends on the data quantity if multicast/broadcast is used.

The data collection from the PIDs to the CCU is one-to-one (unicast) and will therefore be proportional to the number of units in the system. An interest-ing issue is whether the amount of communication needed for the data collection could be reduced. Assuming that the data sent from the units to the CCU is of a kind where the CCU is not interested in duplicated data. In such a system the PIDs could send their data to the CCU on a broadcast or multicast channel so that all other PIDs can receive the data. Each PID that receives data identical with its own now doesn’t need to send it to the PID. An EIS build upon a centralised architecture is shown in Figure 11.

Examples of positive and negative aspects of the architecture from an EI per-spective

+ The architecture corresponds well with the EI model.

+ Easy to understand.

- Well known architecture, and therefor not a very interesting architecture to study.

- The data collection needs a lot of network capacity per PID, which leads to scalability problems.

Compileand

Compute

Presentationunit

Application

Ad-Hoc (Decentralised) ArchitectureThis is a decentralised architecture for an EIS where all data sources (such as PIDs, sensors, etc.) broadcast their data and each PID receives data from all sources (within range). Each PID includes a CCU to process all data, received and the PID’s own. Other units than PIDs, such as the displays could also have their own CCU to process data received from the PID. Such units could also be connected

Fugure 11. EIS with centralised architechture.


to other systems to exchange data and/or compiled results. If so, also data and results from other EISs or subsystems could be sent out to the PIDs so that their CCUs can use that information in their data processing.

This architecture reduces the communication interfaces in the EIS so that the only wireless communication needed is a broadcast interface. Then it is up to all units with a CCU to receive all data and compile a result. If all units receive the same data and use the same algorithm they will produce the same result. Because of the limited range of the wireless broadcast, however, it is possible that not all units receive the same data and they will therefore compile different results. It is also possible to allow that the CCUs use different algorithms to compile different result even with the same data. An EIS build upon a centralised architecture is shown in Figure 12..


+ Generally easy to implement. In the simplest case only one node has to be specified, the PID.

+ The communication is also simple, only one broadcast interface needs to be implemented.

+ Ad-Hoc, no infrastructure needed.

+ Easy to expand with infrastructure support for communication.

+ A new market is created for EI units.

- Hard to control and administrate the system

- Capacity for a CCU needed in the PID. To solve this problem a number of powerful PIDs could have CCUs and broadcast their result to PIDs lacking a CCU.

- Scalability: How should the communication be implemented to avoid capacity problems in large systems? Another problem is whether the PIDs have capacity enough to handle all data received in a large system.

Applicationserver

Actor outsidethe application

range

Actor insidethe application

range

Application

Sharedphenomenon

Compileand

Compute

Intelligentdisplay

Figure 12. EIS with ad-hoc architec-ture.


Minimal System ArchitectureThe CCU and the wireless communication infrastructure needed (if any) are all implemented in a laptop PC. This gives a portable or even mobile system which can be used everywhere and is easily installed in new locations. The communica-tion could for example be some kind of WLAN. A system like this can be seen as minimal in terms of complexity, capacity, costs, needed resources for CCU and communication, space needed for the installed system, etc. The number of units the system can support could still be large.


+ Inexpensive system.

+ Portable and mobile systems.

- Scalability, hard for a laptop to handle massive computing tasks and for the WLAN to handle large number of PIDs.

- Only a limited amount of data can be collected.

Very Large System ArchitectureThis is an architecture where the CCU functionality is duplicated in a cluster of servers, which can perform massive computation tasks in (almost) real-time. The wireless network (one or many access networks) are used to handle large numbers of PID’s. The CCUs communicate with each other over a high capacity wired network. Large amounts of data from the PIDs and sensors can be collected. Complex and bandwidth demanding feedback can be given to the PIDs and the displays. A system like this can be seen as large in terms of complexity, capacity, costs, needed resources for CCU and communication, etc. The physical size of the system (also known as “foot print”) on the other hand could still be small.


+ High capacity system.

+ Scalability, possible to construct systems with different sizes including very large systems.

- Not portable.

- Expensive to build.

- Complex to design, implementation, and administrate.

Open Issues Many issues remain to be resolved for how to design EI system architectures. What is the maximum performance and capacity for different architectures? What are the bottlenecks of the systems? What network capacity and response times are essential for different applications? What amount of data is relevant and realistic for the feedback and/or the data collection? How and why should ad-hoc EISs be designed? Should the system be stationary, portable or even mobile? Can we have virtual systems? These are some examples of interesting issues for further studies of system architecture for EIS’s.

To summarise, both the ad-hoc architecture and the centralised architecture are potential architectures for EISs. The major advantages with a centralised architec-ture are the stability in the basic architectural concepts, and that there is lot of practical experience reported from building applications with this kind of archi-tecture. The latter advantage points out the major weakness of the ad-hoc solu-tions: it is a relative new idea of building applications, and not so much practical


experience has been reported. On the other hand, many of the basic system archi-tectural concepts and their consequences have to be studied, implying that the news value in building EI applications on that kind of architecture is much higher. Clearly, emergent interaction has the potential to open up a new market for EI Units, such as personal devices/tools, presentation units, etc. An ad-hoc architec-ture gives better conditions for such a market than CSA architectures do.

Data Communication How will the different parts in an EI system communicate? Which different tech-nologies, protocols and equipment can be used? Today there are many solutions for wireless communication available that make it possible to build advanced and flexible EIS. First, the needs must be analysed and then matched against the available solutions. This section analyses the possible and probable needs for data communication in an EIS. First, a short introduction to some relevant com-munication technologies and systems will be given. Hopefully this can provide a meaning for concepts such as packet data, TCP/IP, Bluetooth, synchronous/asynchronous communication, delays, throughput, packet loss, bit errors, roam-ing and mobility, security, multicast, unicast, broadcast, etc., in the EIS context.

What is data communication? For our purposes we can use the following defi-nition: Data communication is when computers exchange data with other com-puters. The computers can talk directly with each other or communicate via some communication infrastructure, i.e. a network.

By using this definition it is possible to identify the need for data communica-tion in the EI model. All nodes in the EI model can be seen as computers and all these computers need to exchange data with other computers in the EIS. There-fore, data communication is an important part of every EIS. Data communication is needed both for the data collection and the shared feedback. Another observa-tion is that some kind of wireless (i.e. radio) communication could be used to make it easy for the PIDs to communicate with the rest of the system. Other parts of the system could either use wireless communication or wired network solu-tions.

The protocols and interfaces used should be standardised in order to make it easier to replace one node with another, i.e. one sensor should be possible to replace with another without redesigning the system. What protocols do we need? Is TCP/IP the best choice or do we need something, which better handles real-time traffic and Quality of Service* (QoS) in wireless networks. An overview of QoS in an Internet perspective can be found in (Xiao & Ni, 1999). These ques-tions are candidates for further study in following EI projects.

The wireless communication in the EIS is an important issue to discuss because many different technologies exist today and it is not obvious which solution to use. Most likely, different solutions will be interesting for different kind of systems in different contexts and in systems designed for special purposes. The wired com-munications solutions will probably be easier to find and the technologies used should not vary much between different EISs. That is the reason this section is focusing on wireless data communication.

Wireless Data CommunicationThere are many different types of wireless data communication and there are dif-ferent ways to categorise wireless communications. One way to categorise wire-less systems is by the purpose of the system. There are broadcast systems such as those for television and radio broadcast. Other systems are specialised for mobile telephony, cellular telephony systems such as GSM, TDMA, PDC, CDMA, etc. There are also wireless data systems, which are specially designed for TCP/IP data communications networks.

* Quality of Service is a network-ing term that is used when a net-work connection can guarantee a certain bandwidth.


Another way to categorise wireless systems is to analyse their range. If we only discuss the range and coverage of different wireless network technologies we can organise existing technologies into several categories: Global or national cover-age, regional coverage, cellular (medium range) coverage, local area coverage, and personal area coverage. For each of these categories there are technologies that are interesting from an EI perspective. Here is a list of some of these technologies organised into range and coverage categories:

• Global or national coverage, Digital Video Broadcasting for Satellite broad-cast (DVB-S)

• Regional coverage, digital terrestrial broadcast of television and radio (DVB-T, DAB, RDS, Paging, etc)

• Cellular coverage, mobile or cellular telephony with data services (GSM, GPRS, EDGE, UMTS, CDMA, MobiText, etc)

• Local area coverage, Wireless Local Area Networks, WLAN (802.11, Hiper-Lan2, etc)

• Personal area coverage, Personal Area Networks, PAN (Bluetooth, IrDa, etc)

There are many different standards for wireless communications specified by different standardisation organisations such as ETSI (ETSI, 2002), ITU (ITU, 2002), IETF (IETF, 2002), and IEEE (IEEE, 2002). Sometimes the same stand-ard is defined by more than one organisation. Sometimes there are several differ-ent standards for the same purpose. One example of this is the large number of (2G) standards for cellular telephony. In Europe the GSM system was standard-ised, Japan developed the PDC system, and North America uses GSM but also the TDMA and CDMA systems. One task when standardising the next generation (3G) of cellular telephony system was to define a worldwide standard (Universal Mobile Telephony System or UMTS). Unfortunately, the outcome was a set of standards instead of a single standard. In order to design a unit that will work worldwide, all of these standards must be implemented. These standards are at least possible to combine in one unit but we have to wait for the next generation of cellular telephony for the world’s first worldwide standard.

Other standards are accepted worldwide but are for some reason less attractive in some countries. One such example is the IEEE standard for wireless LAN (WLAN), 802.11b. The standard is available worldwide but with different number of channels available in the assigned frequency spectrum. In North Amer-ica and most of Europe a large set of channels (11-13) are available, but in France, Spain and Japan only one or a few channels are available. This makes this standard much more interesting to use in those countries where many channels are avail-able.

There are a few factors to have in mind when discussing wireless data com-munication in an EIS perspective:

• Range, how large area can the wireless network cover?

• Bandwidth, what is the bandwidth capacity in the network?

• Scalability, how many users can the network manage?

• Mobility, how does the network support mobility for the users?

• Security, does the technology have support for security and privacy?

• Infrastructure, does the technology need infrastructure and if so how expensive is it to use?

• Protocols and Services, which protocols, services and network mechanisms are supported by the network?

• Interactivity, does the network support interaction between the users and the system?

• Standards, are there standardised technologies that fulfil EIS requirements?

• Quality of Service, is there a need for QoS in the system and if so how should it be implemented?


The answers to the questions above will be different for each wireless network. As we can see there are many technologies for wireless data communication and it can be hard to decide which one to select for a system. But do we need to choose only one? The short answer is no. A solution where more than one access network is supported by a system, to enable that different access forms is used to interact with the system, is called Multi Access. (Multi Access is short for Multiple Access Network. The Access Network is the wireless part of a network that interacts with the users.) Multi Access makes it easier to support different kinds of units in a system. Since the different access forms have different characteristics, such as bandwidth capacity, mobility support etc, the design of a system supporting Multi Access is a somewhat complex task. Much effort is needed to ensure that the same services are supported for users independently of the access network cur-rently used.

The Multi Access solution is defined from a user perspective. Multi Access could also be discussed from a system perspective. In this case we can se different wireless access networks for different purposes in the system. For example, in an EIS one network could be used to handle the communication between the PIDs and the CCU, another network could handle the communication between sensors and the PID, and a third could handle the communication between the CCU and the Shared Phenomenon.

Always Best Connected is another concept where the fact that different net-works have different coverage and capacity is used to optimise the available communication capacity for a user. The most common characteristic of wireless networks is that the maximum bandwidth decreases when the coverage increases. Therefore it is interesting to use more than one (access) network to be able to always choose the best connection available. Seamless handover (roaming) between different access systems has been a hot topic lately, and many organisa-tions now claim to have solved the problem. One example is a system where the users have access to both WLAN and GPRS (GSM) networks. The bandwidth capacity of the WLAN is much higher than the capacity of the GPRS network but the GPRS network has a much better coverage. If the user automatically chooses the best available network he will choose the WLAN whenever it is available and use the GPRS network when the WLAN is out of range.

Data Communication in Emergent Interaction SystemsIt is possible to identify where in an EI System data communication is needed and which requirements the communication techniques must satisfy. The following communication links can be identified in the EI model between:

• the PIDs and the Compile and Compute Unit (CCU)

• the shared phenomenon and the CCU

• the Administration unit and the CCU

• the EI system and other EI systems

We could also add three links, which are not actually visible in the model:

• between distributed parts of the CCU

• between the PIDs and sensors

• or between other units in the system

Each of these seven links will be discussed below. Note that the communication between two nodes does not need to use the same link in both directions. There can be reasons to use different communication links/technologies between two nodes. The reasons can be capacity, speed, mechanisms, scalability, etc. It is also


possible that Multi Access solutions are wanted for one or more links in a system.All communication in the EIS must be designed to make it possible to give real-time feedback to the users and participants in the shared phenomenon. This sets high requirements on the data communication solutions. Different systems will have different purposes and capabilities and therefore the exact demands on the communication support will be different. Each scenario must be analysed to iden-tify the needs and only then it is time to try to assign different communication techniques to meet those needs.

Communication between PIDs and CCUWhat are the requirements for data transmission between the PIDs and the CCU? The amount of data to transmit and in which situations and locations, are factors that will affect the choice of communication. It is very likely that some kind of wireless (radio) transmission should be used for this link. The solution should be scaleable to be able to handle a varying number of actors. The amount of data can vary between different systems and different PIDs. Because of scalability issues the communication technology used should be an inexpensive, standardised technol-ogy with capacity to handle a large number of units. The communication could go via public or dedicated networks. Some examples of existing communication systems and techniques that could meet those needs are Carrier Pigeons, Cellular Phone Systems (GSM, CDMA, UMTS, etc), WLAN, etc.

One way to ensure the scalability of the communication network is to use multicast or broadcast (one-to-all mechanisms) instead of unicast (one-to-one mechanisms) when distributing data. A problem with multicast is that it is not supported by all wireless network technologies. The reason for this is that the wireless network usually has much more disturbances and data loss in its transmis-sions. This is usually an unwanted network situation for multicast distributions. There is research in an area called Reliable Multicast, which is applicable when distributing multicast messages over wireless links. An introduction to Reliable Multicast can be found in the IETF Reliable Multicast Transport Charter (Ker-mode & Vicisano, 2001).

The bandwidth requirements are probably high for the links (especially if uni-cast is used) and the response times must be short to ensure real-time characteris-tics of the system.

Communication between Shared Phenomenon and CCUThe communication between the shared phenomenon and the CCU could use wired or wireless networks, whatever is most suitable for the specific application and context of the EIS, i.e. depending on what the shared phenomenon is and what the purpose of the system is. The real-time requirements must also be ful-filled for this link.

Communication between control and CCUThis communication link enables the system to be administrated and controlled. The demands on security and authentication are high for this link. Availability must also be ensured so that the system always can be administrated and perhaps stopped if something is about to go wrong. The bandwidth requirements are probably not as high as for other links such as between PIDs and CCU. If the system should be possible to administrate from a wireless client or not has to be decided for each implemented system.

Communication between EI system and other EI systems (External Feedback and Data)For this communication link unicast communication could be used if there are only one or a few other systems, but if there are a large number of external systems multicast should be taken into consideration. If a wired link is used the network conditions are probably good enough for implementation without the use of Reli-able Multicast solutions.


Communication between distributed parts of the CCUThis communication link exists in system architectures where the CCU function-ality is distributed in the system and located in different system nodes, i.e. the PIDs or in distributed CCUs. This link has a lot of characteristics in common with the link between PIDs and CCU, but here the distributed computation in the CCU must also be taken into consideration. In this scenario the distributed CCU have a lot in common with parallel computers, and therefore suitable solu-tions could probably be found in that area of computing science.

Communication between PIDs and sensorsThis communication link exists to enable the PIDs to collect data from sensors (which are not located inside the PID). The data is sent from the PIDs to the CCU. The physical link between the PIDs and the sensors could be wired (a cable) or wireless (e.g. Bluetooth). Whatever physical link is used the interface should be some kind of standardised interfaces for communication with sensors. The reason is that it should be easy to replace one type or brand of sensors with another without redesigning the interfaces.

Other links between units in the systemThere could also be other links between units in the system (unit-to-unit), e.g. between PIDs. In some architectures there could be a need for other communica-tion links. The characteristics for these links will differ from system to system and will not be further discussed here.

Protocols and ServicesDifferent networks support different sets of protocols and services and that must be taken into consideration when selecting a network technology for an EIS. Eth-ernet networks, for example, support the TCP/IP suite of protocols, which are used all over the Internet. TCP/IP is not always the best solution over wireless links so it is possible that some other protocols could be more effective to use.

Cellular networks, such as GSM and UMTS, support other protocols and serv-ices such as SMS, EMS, MMS, WTP, and WAP. These are specially developed to work well in wireless conditions. For some EISs this could be suitable.

Other efforts have been made to adapt the TCP/IP protocol suite to wireless conditions. One example is the development of MobileIP (Roberts & Patil, 2002), which enables IP clients to move around and have the same accessibility (e.g. address) wherever they are connected to the network. Another adaptation to TCP/IP is IPSec (IETF, 2002), which implements better security in Internet networks, something that is very interesting in easily accessible wireless networks.

Other networks have security built in from the start. One example is the 802.11b WLAN standard and its Wired Equivalent Privacy (WEP). Unfortu-nately, this is a very weak security standard, which easily can be broken.

There are different networking mechanisms supporting different types of com-munication. The most commonly used mechanisms are Multicast, Unicast, and Broadcast. Multicast (one-to-many) allows that the same packet/data can be sent efficiently to many different destinations. Unicast (one-to-one) sends packets/data to one designated recipient. Broadcast (one-to-all) sends data to all recipients in a network. There are also some less known and more specialised mechanisms such as Anycast and Geocast (Ko & Vaidya, 2000), which can be useful in special situ-ations. Geocast is a mechanism to deliver messages to all hosts within a given geographical region. In EI the impact multicast and broadcast could have on the scalability is very interesting to evaluate.

SecuritySecurity in EI is an interesting and important topic to discuss. What requirements on security, privacy, integrity, and authentication can be identified for EI and what techniques can be used to satisfy those requirements? Security is a complex


topic and there are many different definitions of security and different opinions of what risks are acceptable. For any particular EIS it is important to define a secu-rity policy, which specifies how the security mechanisms shall be implemented in the system. The hard part is to find a level of security that gives enough protec-tion without making the system too difficult to use. Different applications should have different security policies that match the risks that can be identified for each application.

The need for security is higher in wireless systems because it is much easier to gain access to the physical links in such a system. Security in wireless systems is a very interesting area to study and there are examples of systems where large effort has been spent on ensuring appropriate security. One example is the FlyingLinux project (Escudero, Hedenfalk & Heselius, 2001) at the Royal Institute of Tech-nology in Sweden. This project aims at providing wireless access to the institute’s computer network for their students and employees at their premises and at the student apartments near by.

Another example of wireless security is in cellular telephony, for example GSM, where a SIM (Subscriber Identification Module) card in every mobile telephone is used to identify a subscriber and storing some vital information needed to securely communicate over the wireless part of the network.

Different methods exist for implementing security in data communication sys-tems. One commonly used method is encryption, which can be used to encrypt data so that only the intended receiver understands the message. Another usage of encryption is for authentication of the participants in a data exchange.

Another interesting issue, which applies to wireless communication, is location privacy, i.e. keeping a person’s position and location secret (Escudero, Pherson, Pelletta, Vatn, & Wiatr, 2001).

Information about computer security can be found in The Department of Defence Trusted Computer System Evaluation Criteria, (DOD-5200.28-STD), also known as the Orange Book, which is somewhat of a de facto standard for computer security today

Security in EI systems is a good candidate for further studies in upcoming EI projects. When designing an EIS it should be a prioritised task to analyse the needs for security, authentication, authorisation, encryption, etc.

Open IssuesA this stage not many answers have been formulated to the question of how the units in EISs should communicate. So far, we have identified a set of questions that need to be answered in the continued work on EI. This section lists some of these open issues, which needs to be resolved.

Ad-hoc or Infrastructure Network: We can divide communication networks into those with infrastructure and those without. In Ad-Hoc (or peer-to-peer) systems all communication takes place directly between the units without pass-ing through other nodes in a supporting network. Infrastructure systems such as cellular telephony systems are built and maintained by an operator, who delivers communication capacity and services to the users.

Another difference between systems is if they are public and for general pur-poses or not. Can everyone use it for free (or for a charge) or is it a private and dedicated network built for a special purpose and only available for some special group of users such as a company or an institute?

Here follows an unsorted, incomplete list of communication issues to have in mind when studying the communication for an EIS application.


• Should the system support Multi Access or not?

• Should radio signalling use free or licensed radio frequency spectra?

• What are the requirements on range and coverage for the wireless net-work?

• Scalability, how many users should the system be able to handle and how do we design systems that can handle different number users?

• What are the systems requirements on security?

• Should the wireless network support user roaming between different cells and networks?

• What type of addressing should be used (IPv4, IPv6, etc)?

• Can the system be built with existing solutions or must new technologies be developed?

• What bandwidth needs and requirements can be identified for the com-munication links in EI systems?

Traffic PatternsThe data traffic generated by an EIS can be analysed to identify traffic patterns. Different systems and applications create different categories of patterns. The composition of the traffic, amount of data, variation and symmetry are examples of properties that can identify patterns. By observing those patterns it is possible to analyse how the system really is working and how the users interact with the system. Categories of patterns can be identified and utilised to create, remove and/or change the traffic patterns in a system. If different technical solutions are needed to handle traffic measurements in different kinds of EISs is another issue to resolve.

Traffic Patterns in EISThe traffic pattern in an EIS depends on different factors such as the system archi-tecture, number of actors, how active the actors are, how much each actor inter-acts with the system, the contexts and the data communication networks capacity, characteristics and limitations, and the applications used. By analysing the traffic it is possible to find answers to question such as: who are communicating, how often and by sending how much data, and what are the response times of the system? Trends, categories, different usage, roles, and bottlenecks could also be identified. Other aspects to analyse are the dependency between the network traf-fic and the number of actors in the system, and how the system architecture affects the patterns. What effects does the data communication capacity and characteris-tics have on the observed traffic?

An interesting question is whether the traffic in the system can be controlled, thus altering the traffic pattern. How can some trends be amplified or attenuated? How do we measure the traffic and which traffic in the system is interesting to measure? Can the specification of an EIS be analysed to predict how the traffic will flow in the system? One example of a study of the patterns of network traffic in a system can be found in (Greenhalgh, Benford, & Craven, 1999), which describes the following method of analysis:

• capture the network traffic in the system

• identify the main types of traffic

• identify the bandwidth usage for different nodes in the system and com-pare with their roles and their level of participation


This method, although used to analyse the traffic in another type of system, can be applied to EI systems. If the task is to analyse a real EIS in order to find out how it is working and how the actors use it, suitable tools for capturing the network traffic must be found. One example of such a tool for ethernet networks is Ethe-real (Etherreal, 2001). How to use another tool, ntop, for traffic measurements is described in (Deli & Suin, 2000). Another tool that could be used to analyse the traffic in an EIS is the Multi Router Traffic Grapher (MRTG) . Tools to analyse the traffic in wireless networks also exist. One example for cellular telephony is the TEMS product family (TEMS, 2001). Examples of tools to analyse WLAN are WildPackets AiroPeek (WildPackets, 2002) and Sniffer Wireless (Sniffer, 2002). Exactly how to analyse the Traffic Patterns of EI systems is a topic for further stud-ies, for example using a prototype EIS.

Another use for traffic patterns is to analyse and verify if Quality of Service requirements are fulfilled and correctly implemented in the system, or if such mechanisms are needed or not.

48 Interaction

7. Interaction

Interaction can be analysed with conversation as a model: a cyclic process in which two or more participants alternately listen, think and speak. The quality of the interaction depends on the quality of each of those subtasks (listening, thinking, speaking) (Crawford, 2000). If we apply this definition to the HITI model, the partic-ipants are the Humans (actors), the Things and the Information/Ideas. Of course, the conversation model has its limitations, particu-larly in the context of EIS: interactions are not limited to turn-taking but can be continuous and going on in both directions simultane-ously; interactions will commonly not be neat two-party direct inter-changes, but be asymmetric with regard to input and output, involve a third party as middle hand, etc.

In the interaction part of an EIS we will focus on the parts that directly involve the actors. The main issues of this section are how to:

• collect input to the system

• feed back the processed information

• make the actors’ experience of the EI application enjoyable

Input collection from the actors and their surroundingsAs primary input to an EIS, different physical parameters can be utilised, such

as temperature, pressure, gas, position, speed, acceleration, light and sound. There are roughly three categories of input that are applicable in this case. First, sensors for unobtrusive data input to the EIS. Second, the PID itself can be used for data input by for example pressing a certain key at the right time, usually requiring certain awareness on the actor’s part - but note that the actor may be less aware or even unaware of it as being input to the EIS. Third, video cameras - located on the actor, in the PID or in the environment - can be used to measure move-ment in a group, how crowded a place is, in what mood people are, and so on, without the participants knowing of it. The video image can also be used as input to an Augmented Reality system (AR), when the visual properties of the shared phenomenon are registered from the individual actor’s point of view.

Sensor inputWhen choosing sensors one would have to consider what to measure, size, stand-ards, economy, accuracy or resolution, linearity and time aspects, power consump-tion, saturation, range or field of view, sample rate and noise filtering. Depending on the situation of use one may consider putting sensors on the actor’s body or to have them in the environment. Sensors on the body should be small and not disturb the actors in their interactions with the real world. Having the sensors in the environment on the other hand may generally be expected to give less data and lower resolution, by making it harder to focus on individual actors.

In general, there are two types of sensors: active and passive. Active sensors can produce, transmit and receive data (e.g. infrared motion detectors), passive sen-sors can only produce data (e.g. thermistors). Sensors can be divided into types and the kind of stimulus energy from the environment that these types can con-vert to electrical signals to the EIS (Barfield & Caudell, 2001).

Interaction 49

• Mechanical sensors can sense position, acceleration, force, shape, mass and displacement. Can be used to detect actor’s or object’s position, weight and movement.

• Biological sensors can sense heart rate, body temperature, neural activity, respiration rate, skin resistance, etc. Can be used to detect the actor’s emo-tional and physical state.

• Acoustic sensors can sense sound volume, pitch, frequency, phase and changes. Can be used to detect sound and for interpreting speech.

• Optical sensors can sense light emission, refraction, light wave frequency, brightness and luminance. Can be used for computer vision detection, IR motion/presence detection.

• Environmental sensors can sense temperature, humidity, carbon dioxide level, etc. Can be used for monitoring the conditions of the environment that the actors are in.

PID inputThe Personal Interaction Device, PID, is a fundamental vehicle for input in the EIS. It can be a cellular phone, a PDA or some kind of wearable computer (see Emergent Interaction Systems, page 17). (It might even be based entirely on remote sensing equipment, without any material parts carried by the actor, but we will not explore this alternative in the following.)

The PID can be used as a carrier of several types of input (and output) devices. The interaction via the PID might not be as hands-free and unobtrusive for the actor as sensor or video input. Hand-manoeuvred PIDs are mostly suitable when the actor has one or both hands free to interact with the PID. If true hands-free is a requirement for the application, other types of input technology should be considered.

The most obvious type of PID input device consists of some kind of tangible components that the actor manipulates mechanically. When designing physical interaction artefacts it is important to incorporate some kind of affordance (Norman, 1990) – perceived properties that help the user determine how it pos-sibly can be used. This kind of physical input devices can be divided into two groups:

• Tactile devices. Designated buttons, compact keyboards, scroll wheels or track balls are some examples. These devices give the user instant feedback via the mechanism inside the product.

• Touch-only devices. Touch pad, touch screen and data gloves are some examples of this kind of apparatus. If this kind of input device is selected, it is important to give the actor instant feedback when data has been put in: for example a clicking sound, a blinking light or alternated graphics.

Video inputVideo input can give a lot of information to the system about the actors and the shared phenomenon to the system. This information can be processed and interpreted in a broad range of ways. This creates a great degree of freedom when designing the application, but it will also restrict the system due to its demands for high bandwidth and extensive computational power. We can divide the video inputs into two groups:

• Sensory video, which is video used for detection, monitoring and recogni-tion only. The images themselves are not presented to the actors. The video can be registering other kinds of input than light; it can register chemical emissions and heat radiation and so on.

• Display video is used for real time presentation for the actor of the real environment, often with superimposed 2-D or 3-D virtual objects (called Augmented Reality, AR). Unlike Virtual Reality ( VR), AR supplements reality, rather than completely replacing it.

50 Interaction

Feeding back processed information Feedback is essential; without it there is no dynamics in the system. The content of the feedback is closely connected to what the goal of the EIS is. In this section we are concerned with the form of the feedback: what forms of feedback are possi-ble and what forms of feedback will be able to effectively reach the actors, eventu-ally affecting their behaviour? Considering that all the usual five senses of human beings - vision, hearing, sensing, tasting and smelling - can be engaged in the feedback, singly or in any combination, and that many senses allow for several dimensions, complex feature detection, learned abilities, and more - the design space for the feedback is truly enormous. Many other questions arise in design-ing the form of the feedback for a particular EIS. Should all actors be aware that there is a feedback? What the form of the feedback is? What the content of the feedback is? Should the feedback (normally) be conscious or unconscious? Close to the actors or in the background? Should the feedback be direct or indirect? If everyone is to be exposed to more or less the same feedback, we might for instance consider using:

• Big displays- can everybody see it? Will people care to look at it?

• Loudspeakers - you cannot close your ears as easily as your eyes (but closing your eyes usually makes you more helpless than holding your ears).

• Changing the temperature - blow cold (or warm) air at the group.

• Vibrations in the floor (ground)

• Air/gas, could smell be used as feedback?

If on the other hand we decide to deliver the feedback individually we might do it via the PID. The technical problem here is to distribute the same (or an individu-ally differentiated) signal, to a lot of PIDs. Traffic problems and communication problems have to be solved. Examples of output devices that might be used:

• Small, individual displays - but who will look at their PID instead of watching the big event?

• Tactile feedback, by using systems like Braille pads or dynamic materials whose surface structure can be digitally controlled.

• Earphones, ranging from small ear plugs to headphones

• Force feedback, haptic and physical feedback, which could also be delivered via physical things in the environment, such as tools, chairs, etc.

• Augmented Reality displays, like optical see-through Head Mounted Display, HMD, or video see-through HMD (see Figure 13).

Figure 13. Basic principles of HMD AR systems. Left: optical see-through system, Right: video see-through system - adapted from Barfield and Caudell. (Barfield & Caudell, 2001).

Real worldphenomenon

Scenegenerator

Optical combiner(s)

Monitor(s)

Position trackerHead

positionGraphicimages

Headposition

Combinedvideo

Graphicimages

Video ofreal world

Real worldphenomenonScene

generator

Videocompositor

Monitor(s)

Video camera(s)

Position tracker

Interaction 51

Augmented Reality HMDs still has their technical drawbacks. That might dis-courage us from using it for actors in a real-world context (Barfield & Caudell, 2001):

• Size, the present systems are very bulky and awkward to use. Neither the size of the hardware nor the connections (cable or wireless) has been developed very much. They are custom made or commercialised proto-types.

• Complexity, especially video see-through systems are structurally complex and hard to maintain in operable condition, in other words vulnerable.

• Safety, the video see-through HMD makes the actor effectively “blind” in case of power cut-off. In that case the actor should not be driving a vehicle, or be engaged in any kind of critical activity requiring vision.

• Resolution, the present micro displays on the market can produce images in the range of 640x480 to 1024x768 pixels. This resolution is far less than the resolving power of the human fovea (the high-resolution spot of the retina). This is most apparent in optical see-through AR systems, where a computer-generated digital image is superimposed on the real world image. The computer image is jagged while the real world looks as usual, smooth and - well - real.

• System delay, even with state-of-the-art technology there will be a delay. Video streams have inherent delays in the tens of milliseconds. The monitor also needs time to update an image; a monitor that completely refreshes the screen at 60Hz has a frame time of 16.67 ms.

Adjusting the output to the actor’s abilitiesIn the design of interaction using a PID appliance, one should consider all human modalities, and let the actor interact with the system both consciously and uncon-sciously. In some cases the actor’s feelings and affects might be of interest (Picard, 1997). Our knowledge of the world is initially sensational; stimulus over a certain intensity level activates sensory receptors; vision, hearing, tactility, olfactory and taste are our senses that can receive the system output. In practical terms vision and hearing are preferred when dealing with large quantities of information being transferred over a shorter time period; see the table below:

By combining two or more of these cognitive input channels it may be possible get through more information per second to the actor. In the case of EIS we must consider how fast we want our system to be. If we have a slow system, there might be a waste of bandwidth to use a fast channel to transfer the information and vice versa. In this early stage of EIS theory there are no rules or guidelines to follow, except to say that it depends on the context of use.

Visualising the informationWhen visual feedback is used, the best way to visualise the information is highly application dependent. Still, there are some useful guidelines (regrettably not completely consistent with each other) on the general principle of form following content.Norman (Norman, 1991), argues for the importance of minimising the cogni-tive gap between a representation and what it represents in order to get a natural interaction with artefacts. For example, distinguishing between additive and sub-stitutive dimensions (see Figure 12), it is important that additive dimensions are represented by some additive representation, substitutive dimensions by some

Sense: Vision

10 000 K

Tactile*

1 000 K

Olfaction#

100 K

Hearing

100 K

Taste

1 KSensory bandwidth factor:

(bits/second)

Table 10. The human peripheral sensory capacity as approximate values. Note that these figures describe the information load before conscious information processing. Since our conscious-ness’ computational capacity is about 7 (±2) bits per second, an autonomous filtering process (Zimmerman, 1989) reduces most of the sensory information.

* Coldness, heat, pain, touch etc.

# Smell

52 Interaction

substitutive representation. A mix-up is very frustrating for the user, causing unnecessary cognitive load.

• If the information is quantitative, one should use delicate combinations of points, lines, a coordinate system, numbers, symbols, words, shadings and colour ( Tufte, 1983).

• If the information regards maps, colouring, layering and symbols will be utilised ( Tufte, 1990).

• If the information is in a substitutive dimension, e.g. different groups of individuals, use a substitutive representation.

• If the information is in an additive dimension, e.g. a percentage of a popula-tion, use an additive representation.

All visual distinctions should be made as subtle as possible while remaining clear and effective for the purpose. Sometimes parallel information streams can be uti-lised to bring about clarity, efficiency, forcefulness, rhythm and balance. Multi-ple images can also be used to reveal repetition and change, pattern and surprise (Tufte, 1997).

Asynchronous awareness Feedback in an EIS will commonly be continuous and real-time but in some applications the actors might be able to choose when they want to take part of the feedback. In a pause, a break, or an uneventful period of the phenomenon the actor can take time to study the information available so far. The data collection from the actor can be continuous, but the extent and characteristics could change when the actor explicitly takes part of the feedback.

Timing is certainly an issue that needs further exploration, e.g. the effects of different feedback delay, jitter, and update frequency as it relates to the type of application.

Some EISs might be running continuously, others could be active just when an actor is present in a certain area (spatial restriction), or during certain time intervals (chronological restriction), or on request from an actor (personal restric-tion) or when a critical mass of actors are present (emergence restriction).

Building the actors’ experienceAn EIS is a combination of objects, events, and services, populated by a number of actors existing and acting in a social environment, a physical environment, and an information environment. The complexities implied by EISs obviously relates to two of the trendier areas of social science: Large Technical Systems, LTS (Mayntz & Huges, 1988) and Social Construction of Technology, SCOT (Bijker, Huges & Pinch, 1987) which both concern complex systems that often are beyond the actors’ (or anyone’s) comprehension. Donald Norman maintains, in The Invisible Computer that computers are too difficult to use, and that simple information appliances are needed (Bergman, 2000). The use of a PID, considered as an infor-mation appliance, may have the effect of keeping down complexity from the indi-vidual actor’s point of view, even though the actor with that same device enters an EIS with complex transactions and processes beyond comprehension. One may

An Additive Dimension

A B C D

A Substitutive Dimension

A B C D

Figure 12. Substitutive and additive dimension. Each of the ovals rep-resents a value along the dimen-sion from A to D. In the substitutive case, the representations replace one another. In the additive case, each successive representation includes the previous. Examples of additive dimensions are loudness and brightness. Examples of sub-stitutive dimensions are pitch and hue - adapted from Norman (Norman, 1991).

Interaction 53

wonder whether (digital) EISs may have a general effect of shifting the complexity distribution to make the individual’s role less complex, but the system more com-plex. If so, how far do we want to go in that direction? (We would probably want to stop short of the architecture of an ant society.) If the shift is relative rather than absolute, we may rather see the introduction of EISs as a way of making a more complex society possible.

Within the area of Industrial Design research, theorists talk of the trinity of good design; industrial design is the process of producing useful, usable, and desirable products (Weed, 1996). The usefulness (purpose) of an EIS system can of course be debated from case to case. Obviously, if the actors think that the EIS is just a waste of time, their motivation and attitude towards the system might drop dramatically. Usability concerns ergonomic and cognitive issues; e.g. we should not have PIDs that are difficult to hold or with illogical arrangements of buttons. Desirability is considered to be the most unpredictable part of the design craft. What makes a product desirable? In most cases it is closely connected to context and culture; designing a PID for youths in Norway differs a lot from a PID for senior Singaporeans. When designing a product, all three parts of the trinity must be taken into consideration.

One way to improve the quality of design is to conduct ethnographical user studies to determine which kind of prototype to design, an approach successfully adopted by e.g. Nokia. The look and feel of the EIS is very important for success, since the human actors are the prime movers of the system.

Open issuesWe have only been able to begin to answer a few of the questions about EIS inter-action and user interface. Much depends on the application; the high-level model is too unspecified and abstract to give any leads towards a successful user interface. In this kind of novel interfaces, all the problems and possibilities will not surface until a prototype has been built and tested in a reality-like environment. When the application prototype has been tested by the actors, an iterative process can start with ethnographical user studies, concept design, early user tests, evaluation, redesign, detail design, user tests, evaluation, and redesign - which will result in the final user interface.

To sum up, EIS interaction promises to be a very interesting research area, even though many of the problems are in common with other types of applications. When specific EIS concepts has been chosen, we can go deeper into detail on how the feedback can be presented to the actors in the best way. At this point, we believe that a flexible system where the individual actor can choose type of presentation is preferable. Flexibility is also desirable in choosing type of input sensors or devices, especially in ad-hoc systems where the actors bring their own, personal devices.

It is also important to create useful applications; otherwise negative attitudes might bias the actors’ experience of the EIS. Much of the current research in interaction technology concerns future concepts, which are mostly unavailable in the near future. This suggests that one should start out with fairly uncomplicated concepts and successively add on features as the EIS research program evolves.

54 Sociological Aspects

8. Sociological Aspects

How does a collective “think”? What do we mean with thinking? Can there be different kind of groups and how do they react? How does social delusion (mass “hysteria”) happen? What kind of feed-back will different kinds of people react to? How can intentions and emotions of a group of people be estimated? Can there be sys-tems where the users believe that they interact but actually do not (placebo effects)? These are some questions concerning emergent interaction systems for which there could be answers in the social sci-ences. The purpose with this section is to emphasise the importance of the social aspects when studying, designing, implementing, and running emergent interaction systems.

Simply speaking a group of people with something in common, which is in focus of their communication, could be called a community. One important char-acteristics of a community is the communication, without which it would be just a number of people focused on the same interest. There are two main reasons why communities are of interest for emergent interaction system research. First, emergent interaction systems can be used to support the emergence, maintenance, and the phase-out of communities. Second, a lot of research concerning different aspects of communities has been done, from studies covering quite technical aspects to studies covering more sociological and psychological aspects of com-munities, and studies in between (Stolterman, 2001). A community can be char-acterised by a number of characteristics (Croon & Ågren, 1998; Jacobsson, 2000; Valtersson, 1996), see the list below. An open question is which of them are rel-evant for emergent interaction systems.

• virtual and/or physical

• kind of medium

• time aspects: fast, slow

• openness, private sphere

• activity level, history

The structures of a community will also set the tone for the behaviour of its mem-bers; it will serve as a context for behaviour. The structures could be religious or cultural, and depend on age and gender. For instance it is not enough for an indi-vidual to have strong religious beliefs, it is also necessary that the beliefs occur in a community with such beliefs and that the community structure reinforces the individual beliefs. This is the influence of the community structure, as described in (Stark, 2001).

There are groups of actors in almost all forms of communities; actors with a similar way to act, behave, think, etc. Such groups can be characterized by several parameters such as: how well known the group is, what it is that makes it a group, how active the group is, how easy it is to identify a group or members of a group, the self-awareness of membership, the dynamics of the group, etc. Some issues that concern emergent interaction systems have to do with mechanisms to iden-tify groups and group members and mechanisms to control the behaviour of dif-ferent kinds of groups. There is a need to identify relevant ways to characterise groups (interesting parameters).

There will be no emergent interaction if the actors are very few; so one ques-tion is how many users are needed. What is the critical mass required, for a certain emergent interaction system, before we can even talk about emergent interaction? Given a collective that is big enough the next question is how does a collective “think” and what do we mean by “thinking”? That leads us to reflect on how

Sociological Aspects 55

social delusion can happen - how does something like “the Seattle Windshield Pit-ting Epidemic” emerge (Bartholomew, 1998)?

Several key factors on mass or collective delusion could be found in this partic-ular story. From the presence of ambiguity and anxiety, to the spread of rumours and false plausible beliefs over to a redefinition of the potential threat from general and distant to specific and imminent. Interesting factors include human percep-tual fallibility, mass media influence in spreading the fears, recent geo-political events, and reinforcement of the false belief by authority figures and people in institutions of social control.

If a specific emergent interaction system would have an external observer that could predict something like a riot, what would be the best way to stop it? – Go in and manually change the feedback or the time constant for the feedback, or maybe something else? What could be the impact of having outside observers or control persons? Would the users care? Is there a risk for a 1984 scenario or will people just not use the system? Considering that people in a crowd have a ten-dency to be easy to lead we might build the emergent interaction system with or without external control, depending on the purpose of the system.

Emergent interaction systems are a kind of applications where security, integ-rity, and privacy issues are important – people want to be alone, they are afraid of being logged, and they worry about the data being transmitted in a secure way. There is a risk for losing the control over the system with an escalation of unwanted effects. There is another aspect of security, the risk that an unauthorized person takes the control over the system. That definitely could cause unwanted effects.

An emergent interaction system collects data from actors in the system. If the system treats data from all actors equally the system is a democratic system. If the system treats the actors unequally it is an undemocratic system. It is also possible to imagine systems that dynamically change their democratic/undemocratic behav-iour. For example, in the normal case the system is a democratic system, but under some circumstances the system turns into an undemocratic system, giving one or several actors a more prominent role in the feedback loop. Some of the interesting issues are social: how and when is it considered appropriate to use a democratic or an undemocratic system?

Wrange’s Average Citizen project plays with the normal structures in society (Wrange, 2001). A real person (Monika) that best matches the average statistics of the society (in a number of parameters: age, number of children, etc.) repre-sents the community. By giving her the same conditions for communicating her (and thus - in some sense - the community’s) opinions that politicians and other prominent citizens have, the normal structures are sidestepped. The idea with the projects is to study how Monika’s opinion affects the society.

Should the actors be aware of participating in the emergent interaction system, of the existence of a group, of the manner in which they may affect the system, and to what extent? Could we have placebo effects – would it be possible to fake (parts of ) the feedback loop and still give the actors the same experience of really being a part of the event? It seems important to identify and characterise levels of user awareness, maybe create a taxonomy.

Open IssuesIn this section more questions than answers have been created; it is an open field with a lot of interesting research to do. Building an emergent interaction system, the sociological aspects would have to be considered in advance and measured during a period of testing to make sure that no really bad effects will emerge. Potentially relevant scientific disciplines include psychology, sociology, pedagogy, cognitive science, artificial life, theology, economy, and more.

The Seattle Windshield Pitting Epidemic

Suddenly a lot of people started to report that the windshield of their car has been damaged, they reported tiny pit marks in the glass. The episode started on March 23, 1954. On April 14th the police had logged over 3000 vehi-cles with pit marks. On April 16th the police logged 64 pitting com-plaints, and 10 on the 17th, but after that date, not a single further report was received. The police ini-tially suspected vandals, but as the number increased, it soon became evident that this was not the right explanation. Speculations concern-ing sandflea eggs and atomic fall-out from hydrogen bomb tests were spread. However an inves-tigation a few years later deter-mined that the pits had always existed. Influenced by the rumours and spurred by a few initial cases amplified by mass media, people started looking at instead of through their windshields.

56 Data, Information, and Knowledge

9. Data, Information, and Knowledge

What can be computed? What data are needed to make intelligent computations? What are data, system data, personal data, and user input? What kinds of representations of data, information, and knowledge are suitable for the kind of computations that EI appli-cations fall back on? Answers to these questions and similar can be found in the areas of computing science and information science.

The purpose with this section is to bring out the computational aspects of emergent interaction - the processing of data, informa-tion, and knowledge transformation, with a focus on the Compile and Compute Unit in the model and the computation of the feed-back function. The shared feedback loop in an EIS consists of a number of logical components (see the model description on page 17), where the compile and com-pute function plays a central role. Many of the other components are discussed in previous sections. The Interaction section and the Technical System Aspects sec-tion discuss the technical aspects of the data collection and presentation parts. The Sociological Aspects section discusses these aspects from a human perspective.

One of the most prominent facts of computing science is that not all math-ematical functions are computable, in terms of theoretical as well as practical limits of time and memory resources - even for the fastest computers (top500.org, 2001). For example, to solve the problem of the travelling salesman* with 40 cities requires O(39!) elementary calculations. If we have a very powerful computer that is able to test 1015 routes per second#, solving this problem will still take many times longer than the lifetime of the universe. Since quick feedback and is impor-tant in emergent interaction, the computational time and memory requirements need careful analysis.

The compile and compute functionality can be designed for different ‘levels of ambition’:

• Basic feedback, i.e. compile data into feedback information. For example, compute max, min, and mean values, display distributions, etc. That means that the system leaves the high-level interpretations of the information to the actors.

• Analysed feedback, i.e. analyse and identify the interactions and behaviour in the system. In this case, the computation performs some of the high-level interpretations of the information in the system. For, example identi-fying categories of behaviours, matching individual data against collective data, etc.

• Controlled feedback. Equipped with a context model (which must be contin-uously updated) of the actors and other parts of the environment external to the technical system, the technical part of the EIS (the CCU in particular) can be made to have some idea of what is going on in the system as a whole. Given that the compile and compute unit has instructions about what is considered desired system behaviour, and has some methods for controlling the behaviour, feedback could be dynamically engineered to take the EIS in the desired direction.

The functional model of EIS that was introduced in section, Emergent Interac-tion Systems, leaves many design issues open, such as the question of the level of decentralisation of the compile and compute functionality. Hence, it is relevant to study different computing paradigms and how well they suit different parts of EISs, different system architectures, etc.

The data collection plays an important role for the compile and compute func-tionality. First, which information is picked up from the users and the environ-ment sets definite limits for what can be computed. Second, what we want to achieve with the EIS bears on what information is appropriate to deliver as feed-

* A salesman have to visit all cities but want to minimise the distance to travel, the salesman is forbidden to visit a city more then one time. This problem seems to be trivial.

# The fastest computers today, which are clusters of many com-puters, have a peak performance on 1012 elementary operations per second.

Data, Information, and Knowledge 57

back, and must also guide the design of the data collection system. Third, there are now sensors for almost any basic physical variable, but the problem is how to take care of the data and give it a reasonable interpretation. Generally, the compu-tational issues of EIS can be expected to be harder to deal with than the measuring issues.

There are groups of actors in almost all forms of communities, actors with similar way to act, behave, think, etc. (see section Sociological Aspects). There are also recurring behaviour patterns without any obvious connection to groups or established (sub) communities. From that perspective, if we wish to use analysed feedback, there is a need to develop relevant methods to characterise and recognise groups and behaviours.

There are many clustering algorithms, such as Latent Semantic Analysing/indexing LSA/I (Dumais, Furnas, Landauer, Deerwester & Harsman, 1988; Foltz, 1990; Landauer & Dumais, 1997; Rehder et al.; Soto, 1998), Self Organisation Maps (SOM) (Kohonen, 1988),K-nearest neighbours (Belew, 2000), Supported Vector Machines (SVM) (Hearst, 1998), Bayesian Networks (Belew, 2000), Markov based methods (Belew, 2000), etc., that are of interest for the CCU. Many of these algorithms are used in the fields of data mining and information retrieval, as well as in the fields of user modelling, collaborative filtering, social navigation, and context awareness (Grouplens, 2002). One important research issue is to identify properties of EISs and EI applications that make it easier to choose techniques for user modelling and the design of the feedback; e.g. the timing aspects of an application are important in this process. The techniques themselves are also of interest to study, e.g. to make comparisons between them in order to get a better idea of pros and cons with them.

Simulation of complex systems (such as ant societies, traffic situations, urbani-sation, etc) is also a relatively active research area in computing science and related areas (Resnick, 1994; Ropella, 1999), Simulations of that kind can be used as a tool to study and develop emergent interaction systems, and test and analyse how they work. For example, in EIS simulation different techniques, architectures and application areas can be demonstrated, analysed, tested, or evaluated. Another interesting use of simulation is to let it complement a real system in order to build up system awareness. This can be used to decrease data traffic in parts of the system, or to control the overall behaviour in the system. What to simulate and how to do it needs to be investigated. Another issue to resolve is how to handle the results from the simulations. Identifying new research topics could also be an output from this work. One such idea is to see if it is possible to develop methods for identifying the level of interaction and dependences between actors, behav-iours, and knowledge structures.

Open IssuesTo summarise, some of the open issues in this area are:

• Study the different approaches to the level of centralisation of the “intel-ligence”.

• Define the important characteristics of EISs from a data mining/clustering perspective.

• Development of fast and “memory greedy” algorithms for the processing of the collected data.

• Study different techniques for modelling knowledge structures.

• Study how to mix the real and the simulated worlds for the system aware-ness issues.

• How simulation environments like Swarm can be used to study the EI con-cept, etc.

• Develop methods for identifying the level of interaction, especially between ideas or knowledge structures

58 Pre-study Outcome

10. Pre-study Outcome

The pre-study started with a tentative definition of emergent interaction. One of the objectives was to test, refine, and deepen the initial notion and see if a stable (project group) consensus on the emergent interaction concept would form. Another objective was to pave the way for future, comprehensive studies of emer-gent interaction. This report can be seen as an umbrella spanning and defining the area of emergent interaction. The purpose with this section of the report is to summarise the results of the pre-study and suggest a general direction for the future research in the emergent interaction area. The Designing for Emergence sub-section summarises our reflections on the problems and possibilities on design-ing EI systems. The Emergent Interaction in a Context subsection is an overview of the context in which EI exists. The subsection Possible Application Areas section shows the large variety of proposals for EI applications, which have been identified during the pre-study. The Prototype Requirements subsection lists suggested general requirements for prototype EIS. The Open Issues subsection is a listing of proposal for research within the EI area. The Suggestions subsection contains recommenda-tions and suggestions for further work in the emergent interaction area.

Designing for EmergenceThis pre-study has started an examination of the conditions for a type of applica-tions supporting and exploiting interaction between the individual and the collec-tive, theoretically inspired by the notion of emergence. It is appropriate that the first four sections have dealt with Emergent Behaviour, Emergent Interaction, Emer-gent Architecture, and Emergent Interaction Systems. The novelty factor implied by the emergence concept can be seen in a number of new applications building on new technology plus the individual-collective relation, with sometimes quite unexpected result. E.g. the GPS (Global Position System) technology has origi-nated several novel applications, like GeoCaching and Digital Angel, and the HTTP (Hyper Text Transfer Protocol) has resulted in a number of new and unex-pected web applications such as MovieLens (MovieLens, 2001). Babelfish (Babelf-ish, 1999), etc.

Is it really possible to design emergence, is it possible to design the unexpected? It may seem that “designed emergence” is a contradiction in terms. And of course, if we believe that, then the idea of designing emergent interaction systems comes to an immediate halt. Unpredictable outcome is inherent in the concept of emer-gence, but what we might be able to do is to design for emergence. A basic meth-odology for the standard EIS project of the future might go like this. 1. Think about and decide on a very high level what kind of result, in terms of this or that value or parameter the EIS should have. 2. Design for emergence, that is, design a system that satisfies some identifiable (believed to be) necessary requirements for such effects, and which might (we believe) produce them if we are lucky. 3. Implement and run a prototype (possibly as a simulation). 4. Evaluate how far the system goals are satisfied. 5. If the system is a failure then try to analyse why it was unproductive or counterproductive and start over. If the system is a partial suc-cess, analyse the emergent system and try to understand what happened and try to identify crucial parameters and design decisions. Then redesign (modify, adjust), evaluate and iterate until the result is satisfying.

The first point is that, even though we cannot anticipate how a planned emer-gent system will behave and thus, if we are lucky, how it will deliver the wanted effects, we may yet be able to put together a system of which we have reason to believe that it may produce the wanted effects. The second point is that, once we have an existing emergent system, good or not so good or even counterproductive, then we have something that can be analysed, explained and understood, after the fact.

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This approach to the design of emergent interaction systems – let us call it emer-gent design – can be compared with standard methods of designing information systems, building on a number of well-known design principles (Margolin & Buchanan, 1995). Emergent design is a type of iterative design and also has some resemblance with the participatory design approach (Hackos, 1997), where the interplay between the users and the designers of the (technical) system is an important part of the development process. Participatory design, however, is a traditional development methodology in that it focuses on how the task is per-formed. Emergent design, by contrast, has its focus on the wanted overall effect and purpose of the application, and the conditions for emergence, and it works by iterative redesign guided by emergent system analysis, after the fact.

In a more advanced form of emergent design, we may even make the design activity part of the system being developed. The designers are on-line actors in the system. They try to control and guide the system behaviour into what they deem to be the right direction, serendipitously taking advantage of lucky but unforeseen effects, opportunistically changing their minds about what the right direction is – based on the system’s actual development in time. According to Stolterman infor-mation technology is the perfect technology to create what he calls tectonic sys-tems.

“A system can, of course, be designed without organization principles, or only with locally (regionally) organization principles. We can label such system as tectonic.”

(Stolterman, 2000)

The emergent design approach opens new dimensions for design, e.g. simulations of complex systems may play a central role. Simulations of complex systems fall back on an analysis of the basic primitives for the interaction, resulting in a well-structured model of the ongoing phenomena. Simulation and the possibilities to control the direction of behaviour development and analyse how different control apparatus affect the system, are powerful tools in the design of emergence. Mixing the simulated world (virtual) with the real world is another interesting option. One example is discussed below.

This pre-study has identified several problems and open issues with emergent interaction, which are necessary to have in mind for designers and implementers of EISs. Reaching a critical mass is one of these potential problems. Simulation can prove to be a generally useful tool. Simulation models offer the possibility to start the system in simulated mode, phase it out and phase-in real mode. In the beginning the system includes a number of simulated actors and a simulation of the technical framework, with the designers acting in it. As more real actors join the application and real EI-units are up and running, the designers and simulated actors can be phased out.

Emergent Interaction in a ContextEmergent interaction is not an isolated concept. Due to the generality of the concept and its connections to a broad variety of topics, there is much ongoing research, development, a number of interesting applications, organisations, etc, that can help to set emergent interaction into a context. The purpose with this part is to give a view of this context in which emergent interaction has emerged and exists.


ApplicationsWith regard to applications, the Arena/IT-Hockey and eStreet, developed at Mäkitalo Research Center in Luleå, are related and of obvious interest. Arena is a future-oriented programme for the development of new services, applications and technologies that support different types of public events and enhance spectator experiences, mainly in sports (like IT-Hockey), cultural and trade-fair contexts. eStreet is a collaborative programme for developing and testing new mobile-telephony services and technologies for commercial and public-service informa-tion in a real-life setting (MRC, 2001b).

Telia Mobile’s application FriendFinder is another application of interest where the mobile phone users can share their own position with their friends (Telia, 2001). Digital Angels is an application that combines location monitoring and monitoring of selected biological functions. That makes it possible to find a person, animal or object anywhere in the world, anytime, and to do medical monitoring over distance (Digital Angel, 2001).

Other examples in the area of positioning are Geocaching, which is an adven-ture game for GPS users. Participating in a cache hunt is a good way to take advantage of the features and capability of a GPS unit. The basic idea is to have individuals and organisations set up caches all over the world and share the loca-tions of these caches on the Internet. GPS users can then use the location coordi-nates to find the caches. Once found, a cache may provide the visitor with a wide variety of rewards. All the visitor is asked to do is if they get something they should try to leave something for the cache (Inc, 2001).

GPS-art, drawing pictures by tracing your own walking path, is another existing example of how positioning systems can be used in a new emergent way, a way that the system designers did not intend when they developed and launched the GPS in 1989 (Pryor & Wood, 2001). The sport of orienteering has also started to use GPS to visualise the participators’ positions, for example superimposing the runners’ results as if they started at the same time.

The web and the manifold of web-application have emerged from a very simple application protocol (http) on the TCP/IP stack. Hence, the web it self is an interesting EIS to study. In addition MovieLens (MovieLens, 2001) and Amazon.com (Amazon.com, 2002) are two web-applications that we use in this report as examples of EISs with a focus on the virtual room, to this two applica-tion the concept of web-portals can be added to exemplify this kind of emergent interaction systems.

Technical FrameworksWith regard to technology we have found the Russian wireless peer-to-peer com-municator Cybiko Xtreme to be an interesting appliance that can be useful as a test bed for future applications and prototypes (Inc, 2002). Wireless Local Area

Figure 14. Example of GPS art from a walk in Brighton (Melin, 2001).


Networks, WLAN (Armenta, 2002), and the Bluetooth (Bluetooth, 2001; Miller & Bisikian, 2000) technology for mid-range and short-range communication are basic technologies ready to use, as well as cellular phone technologies, especially 2.5 and 3 G platforms, GPS,and GIS. The Ericsson Erisoft’s Event System (Erics-son, 2002) and similar platforms such as Digital Angel systems makes it possible to start implementing EISs and applications without putting lots of resources on implementing the infrastructure for EIS.

Microvision’s display based on the virtual retina display technique from HITL is a very interesting technique for presenting visual feedback on personal level in EISs (Microvision, 2002; Tidwell, Johnston, Melville, & Thomas A. Furness III, 1995).

Orad is a company with many interesting technologies aimed to enhance the experience of a broadcasted arena event. For example techniques for: tracking posi-tions of the actors in real time and visual effects on broadcasted events (Orad, 2002).

Wiki is a collection of web pages, which can be edited by anyone, at anytime, from anywhere. Ward Cunningham created the concept in 1994 (Cunningham, 2002). The name originates from wiki-wiki, which is the Hawaiian word for quick. This technology could be used to create virtual EI systems on the Internet.

Research and Development ProjectsEven if the field of emergent interaction research is new, there are many organisa-tions running research and development projects relevant for the further under-standing and development of the emergent interaction field. It is hardly possible to give or to have a total view of all ongoing projects with relevance for emergent interaction. Thefollowing brief walkthrough is an attempt to giveat least some idea of the ongoing research relevant for emergent interaction.

MIT Media Lab, at Massachusetts Institute of Technology, Cambridge, is doing research in many areas relevant to EI. One example is the Things That Think consortium (MediaLab, 2001) with a focus on making things that inter-act with their surroundings. Another important example is the affective comput-ing research group (MIT, 2002) with their focus on “measuring emotions” with biosensors, and using these techniques for various applications.

The Center for Lifelong Learning and Design (L3D) at the University of Colo-rado at Boulder is another important research organisation (L3D, 2002) for the EI view. They also do research in a very broad field with many exciting ideas and applications in which the LSA algorithm is used to analyse knowledge structure and development of knowledge structures (Berry & Dummais, 1994; Deewester, Dumais, Furnas, Harshman, & Laundauer, 1990; Dumais, Furnas, Landauer, Deerwester, & Harshman, 1988; Foltz, 1996; Foltz & Dumais, 1992; Knowl-edge Analysis Technologies, 1999; Landauer & Dumais, 1997). The GroupLens Research at the University of Minnesota in the field of collaborative filtering, (Riedl & Konstan, 2001) is also interesting for the research on EI. Mainly for the kind of system they study, but also because they attempt to utilise the LSA algorithm in their applications (Konstant, 2001).

Humle lab at the Swedish Institute of Computer Science (SICS) (SICS, 2002b) at Kista, has a number of relevant projects, especially the projects with a focus on social computing (SICS, 2001b) and social navigation projects like PERSONA (SICS, 2002c). Geonotes (SICS, 2001a) is another project at Humle lab with rel-evance for the EI view. It is relevant for the attempt to merge the physical and vir-tual worlds with an application that makes it possible to make notes connected to geographical objects (like Post-It notes on physical objects). Chalmers Media Lab, Gothenburg, with their Digital Senses project goes in the same direction and does research related to problems in the borderland between the physical and virtual worlds (Chalmers, 2001)


The Department of Informatics at Umeå University, Sweden is doing research in several fields with relevance for the research on EI – IT-democracy and design are two areas – the Net-Life group with their focus on virtual communities may be the most relevant for the research view on EI (Stolterman, 2002), for their studies on various aspects on being on the net.

The research field that has made emergence a fundamental concept, is referred to with a multitude of different names, e.g. Complexity, Complex Adaptive Sys-tems, Emergence, Self Organization, Artificial Life (Alife), etc. This is problem-atic, since it makes it very hard to overview the field. Three Nobel Prize laureates founded the Santa Fé Institute (SFI), with the ambition to bring researchers together in order to explorethe science of complexity (Goldberg, 2002). The SFI pioneered the field and is still an important centre for this type of research. Many other groups have also come to specialize in the field, for example the MIT AI-lab (Brooks, 2002), the Center for the study of complex systems at the University of Michigan (Simon, 2002), the People, computers and design group at Stanford (Winograd, 2002), and the Complex systems group at Chalmers in Gothenburg (Lindgren, Mehlig, & Nordin, 2002).

The Computer and Network Architectures laboratory (CNS) at SICS conduct research in the context of ad-hoc networks and communication for appliances. Especially the research about ad-hoc networks is relevant to EI (SICS, 2002a).

The Mäkitalo Research Centre (MRC) at Luleå Technical University, Sweden, is a research centre for wireless technology and applications. At MRC new mobile Internet products and services are created and developed. Examples of research programmes are Arena and eStreet (MRC, 2001a, 2001b, 2001c).

There are many interesting projects in the arts and humanities with relevance for emergent interaction, especially for the ideas concerning the design for emer-gence and how to communicate ideas to individuals and groups in new ways. Måns Wrange’s Average citizen project (Wrange, 2001) and Kenneth E. Rinaldo’s art project Emergent systems (Rinaldo, 2001) exemplify the latter; in Wrange’s case by representing the society’s opinion with one physical person, and in Rinal-do’s case with a manyfold of interesting forms of presentation of the system status. Animationshuset with their focus on the use of the animations as a form for inter-action is another institution conducting research with relevance for EI systems (Löwgren, 2002).

Research concerning the language that art directors and movie directors use in their communication with the actors is interesting from a design perspective on EIS. HUMlab at Umeå University is an interdisciplinary laboratory for human informatics, digital culture and art, and a meeting place for humanities, culture, information and media technology. Some examples of HUMlab projects are Eng-lish language in transformation, Virtual weddings, Streamed media in language education, Magic Touch, Cultural simulation, Aggressive behaviour in online com-munities, and Virtual ice (Svensson, 2002).

Journals and ConferencesJournals and conferences are both natural channels for spreading ideas in the research community. The list below is a selection; of course there are many more that are relevant for spreading ideas concerning the concept of emergent interac-tion.

Journals

· Personal and Ubiquitous Computing, (Springer, 2002).

· Mobile Networks and Applications (ACM, 2002b).

· Wireless Networks - The Journal of Mobile Communication, Computa-tion and Information (Kluver, 2002)


· International Journal Universal Access in the Information Society UAIS (Stephanidis, 2002).

· Human-Computer Interaction, (Moran, 2002).

· Complexity International is a refereed journal for scientific papers deal-ing with any area of complex systems research. (Green, 2002).

Conferences and Workshops

· 2nd International Symposium on Smart Graphics (Butz, Krueger, Olivier, Schlechtweg, & Zhou, 2002).

· Complex Systems (CS02) - Complexity with Agent-based Modelling (Namatame, Green, & Aruka 2002).

· DIS2002 Designing Interactive Systems. A venue for serious reflection on the practice of designing interactive systems, exploring the aes-thetic, social and cultural dimensions of new technologies (Sutcliffe & Verplank, 2002).

· Artificial Life VIII The 8th International Conference on the Simulation and Synthesis of Living Systems (Standish, 2002).

· The annual ACM CHI conference (ACM, 2002a).

· ACM International Symposium on Mobile ad hoc networking & comput-ing (Hubaux, 2002).

Possible application areasA large number of ideas for EI applications have been proposed and organised in nine categories. A complete listing of all the application ideas for EI can be found in Appendix A. The nine categories are:

1) Knowledge Society Events, supporting the emergence of knowledge both on the individual level and in society/community level.

2) Exercising Events, enhancing the experience of collective athletic activities like spinning, aerobics, dancing or swimming.

3) Home Activity Events, with the idea to support the emergence of communi-ties focused on home activities; make it easier, more fun, feel affinity when eating dinner, watching TV, sleeping, etc.

4) Professional Events, applications to feel presence over a large distance, like meeting, factory floor, problem solving and design and empathy training.

5) Public Space Events, for rational reasons like making shopping more effec-tive or looking up bus timetables, or for pleasure, like visiting restaurants, pubs or exhibitions.

6) Political Events, supporting the democratic process, both communication activities and the control apparatus, in terms of demonstrations, elections, avoid/track crime activities or even war.

7) Communication Events, for shortening and bridging of distance, car-pooling, tourist guidance, implicit traffic control, extended videophone conferenc-ing, etc.

8) Arena Events, to extend and enhance the concept of arena events, for exam-ple make the audience more active or participating, or widening the arena into the virtual world for theatre, track and field, or collaborative art.

9) Community Events, supporting activities intended to increase the engage-ment, attendance, kinship or similar qualities in applications like hunting, instant communities, gardening, social life, or to create new contacts.


Positive and negative aspectsThe general discussion has resulted in a group consensus that the concept of emergent interaction could and should be used to increase and enhance social life with participation and engagement in some sense, and with new ways of forming acquaintances. Prototype systems of this kind would be an attraction and would make the concept more concrete and visible, it might also create a general feeling that EI applications are cool and trendy.

The identified associations between emergent interaction applications and Orwell’s 1984 scenario put the finger on the main weakness of the EI concept - the secrecy issues, the integrity issues, and the risk for promoting also negative tendencies in the society. The general discussion has also identified a number of other worries.

Prototype RequirementsThe activities for finding application ideas resulted in about seventy suggestions. These systems are all more or less realistic to implement, especially as a prototype serving to test ideas and to use as a research platform for studying different aspects of EI. In order to evaluate and rank the ideas, it is necessary to set up prototype requirements. The requirements cover technical aspects such as system platform, time and cost, as well as utility aspects and some more pragmatic aspects. It is hard to put the requirements in priority order, so they are simply arranged into four categories: Cost Requirements, Technical Requirements, Effect Requirements, and Security Requirements. This categorisation is ambiguous, (i.e. some of the requirements fall into more than one category) and each requirement is described under just one category.

Cost RequirementsOne important aspect of the feasibility of a project is the cost. We have focused on the costs for implementation of a system platform, travels, personal resources and regular tests. In order to cut the travel costs, the event must be located in the near region or be more or less a virtual event. It is also good to have something to demonstrate in the near region. To implement an EIS system from scratch is costly. One solution is to build a prototype upon existing platforms, such as Erics-son Erisoft’s Event System platform (Ericsson, 2002) and Digital Angel platform (Digital Angel, 2001) based on GPS and WLAN technologies. This is also a tech-nical requirement and a motive requirement; it is interesting from Ericsson’s point of view to evaluate their platform in a research system, to find pros and cons with their technology.

One way to cut the costs for the implementation of a prototype is to associate master thesis projects to the implementation project. In order to minimise the cost of evaluation of different aspects of the EIS it is necessary that the event occurs repeatedly or constantly. A one-time event will not do. This is also an effect requirement; interesting effects may not emerge in a short run. E.g. a community is normally a relatively slowly developing phenomenon. Another way to reduce the cost for development is to define open source projects where volunteers par-ticipate in the development of the application. The projects can be presented on the Internet, for example (McGovern, 2002) and thereby be available for partici-pants all over the world.

Technical RequirementsEmergent interaction covers a very broad spectrum of research issues ranging from technical to sociological. The technological view is focused on studying data com-munication, with traffic patterns, possible WLAN technologies, and evaluating new technologies in large. This has lead to four technical requirements on a pro-totype implementation. First, the potential for the use of 2,5G or 3G mobile


telephony terminals and/or variants as PIDs is an important factor for a future market evaluation of the system and therefore an important technical require-ment on a EI prototype. Second, a similar requirement is the support for different communication interfaces and PIDs. Third, the potential usage of new technol-ogy, such as wireless (HiperLan2, 802.11a, etc), for the communication is also a valuable factor, it is necessary to minimise the bottlenecks in the communica-tion and one way to that is to use as good as possible techniques for communica-tion. Fourth, data must be easy to access and compile, technologically as well as cognitively. From a technical point of view is necessary to use existing and stable techniques for data collection, and to utilise standard interfaces for the commu-nication with sensors.

Effect RequirementsEven if it is hard to predict the impact of an implementation, one of the strong-est requirements on a prototype is that the prototype should create an emergent interaction effect (obviously). One research issue associated with that requirement is how to identify or prove such effects. Another requirement is that it must be an interesting effect for all kinds of actors in the system; otherwise we risk ending up with a system without any actors. Also, the prototype must allow variations in the feedback so that their effects on the actors’ experience of the system can be studied, e.g. feedback with a natural, statistical, or more surrealistic character. A basic requirement on a prototype implementation is that it must be easy to follow up and evaluate. This puts some restrictions on the system, how distributed it is, the size of the test groups, the kind of event, how long it will take before interest-ing things begin to happen, etc.

Security RequirementsEffort must be laid on security aspects even for the EI prototypes. At least a first draft of a security policy should be specified and used when designing prototype systems. In the early projects the policy can specify both which security measures that shall be implemented and those who will not.

Summary of the requirementsTo summarise, the following requirements are the result from a discussion about how to implement prototypes. These can be seen more as a set of recommenda-tions to chose from than mandatory requirements.

• 2,5G or 3G potential and/or variants

• Built upon the Event System platform

• Easy access and compilation of the data

• Interesting effect on the participants/users

• Interesting for a proposed owner of such a system

• Interesting parts for UCIT, the project and the project parts

• It should use an event that occurs repeatedly and/or always

• Master thesis task should be possible associate with the development

• Possible to do follow up and evaluation of the result

• Possible to implement during the spring 2002

• Support for different communication interfaces and PIDs

• The event shall be in the Umeå (campus?) region

• The prototype should create an Emergent Interaction effect (obviously)

• Usage of new technology, such as WLAN (HiperLan2, 802.11a), etc.

• The uniqueness of the application is important for the choice of prototype system

• Security aspects must be considered


Open IssuesMany of the sections in this report have listed issues for further studies. The intention with this section is to draw attention to some of them, and the wide scope of research tasks for EI identified in this pre-study. Each area can be stud-ied separately in small research projects or together with others in larger research projects.

One example of issues to resolve is the largely hypothesized positive and nega-tive aspects of EI that originated from the brainstorming activities. Many of these statements lack verification, and it is important to find out of which of them cor-rectly describe the actual situation.

Another interesting area to study is which architectures to use when designing different EI applications. Many research topics can be found within this area, such as how communication protocols and mechanisms should be specified and implemented. Different technologies and topologies can be designed and evalu-ated. Other examples are how to design the systems to ensure that they are scal-able and able to handle large numbers of actors. Related to this are issues of how to satisfy the requirements on data communication, network capacity and serv-ices. Different technologies and standards and their use in different situations to handle the need for communication in the EIS can be studied.

The design of user interfaces for EISs is another important area for research and development, to ensure that the interaction works well. This is essential to create the wanted effect and to make the EIS attractive to use. By creating sce-narios, interesting ideas and concepts for the interaction between users and the different EIS applications can be tested and evaluated.

There are many unresolved issues about computation. Which existing algo-rithms can be used and is there a need to develop new algorithms? Can computa-tion be distributed between actors in the system and what mechanisms are needed to ensure that this works as intended?

The different aspects of security, integrity, authentication, authorisation, (loca-tion) privacy, etc for emergent interaction systems, make up a large research area. Methods for implementing security and integrity policies could also be studied. This is at least partly related to the sociological aspects and effects that EI applica-tions could create. How should systems be designed to avoid unwanted effects? Many open issues remain to be resolved in the sociological area.

A more technical issue is which sensors can be used in EI systems and how they communicate with other actors in the EIS. What data can be collected from the participants and what can we get directly from the shared phenomenon? Is there a need to develop new sensors for EIS applications?

What EI aspects and applications can be tested and evaluated by simulations? One example is evaluation of protocols. Another use is to simulate parts of an EIS. For example, by simulating a large number of users one could test and evaluate applications without a large number of test pilots.

This is only a few of the areas for research and development that have been mentioned in this report. It is an extensive field to cover. The next section there-fore presents more specific ideas about how to proceed with our study of emergent interaction.

SuggestionsHow should the result from our pre-study be used? This report can be seen as a framework for emergent interaction research, serving both as background infor-mation and as an outline of the possible research directions for the near future. Emergent interaction systems have potential to generate a market for commercial products. Emergent interaction research and development has a great potential to


engage both universities and the industry. There is much ongoing research and development activities around the world with relevance for emergent interaction. The fact that all participants in the pre-study (Cognitive Computing Lab, Digital Media Lab, Interaction Design Lab, Virtual Reality Lab, and Ericsson Erissoft) were able to identify relevant issues to study and interesting ideas to develop shows that the UCIT research program on emergent interaction is well anchored.

The following activities have been identified as high-priority issues in develop-ing the field of emergent interaction, and should be included in the research pro-gram.

ImplementationsA small number of EIS prototypes need to be built. The main motive is the neces-sity to have some test environment that can provide empirical support and feed-back, in order to develop the emergent interaction concept further. In choosing prototype we obviously want it to be of interest for all participants in the projects, i.e. all UCIT labs, other academic participants, and industrial participants such as Ericsson. Another requirement is the potential interest of a proposed owner of such a system. The time aspect of the implementation also constrains the choice of prototype system, e.g. kind of available techniques, the complexity of the system, etc. The prototype must be possible to implement in the near future, for two rea-sons. First, we need it to continue our studies. Second, it is important to be able to illustrate and demonstrate the concept; it will be much easier to establish the concept with one or two implementations to point to. The choice of prototypes also depends on the focus and goal of the research program and what the partici-pants think are important for them.

Besides the prototypes, simulators would also be helpful. Simulators are needed for the further development of the emergent architecture concept and the emer-gent design approach, but are also useful for basic research on emergence in com-plex systems.

When developing EI prototype systems it is probably best to start in a small scale, with already proved techniques, to cut the costs and get a smooth start of the project. We also recommend reducing the complexity of the first systems. One way to do this is to skip the mobility issues in the first versions. By involving students doing their master theses the EI concept is introduced on a broad front, especially to the system developers of tomorrow, and we get much of the work done at a low cost. Another recommendation is to perform as much of the proto-typing in the near region. It is also wise to select a recurring or continuous event for the application, to be able to perform detailed tests and verifications.

To quickly establish the concept of emergent interaction systems, we want pro-totype systems that are quick and simple to implement. Use as simple technology as possible and make sure the solution is scalable. The design space of EISs is very complex and so is the research area. Therefore, we think it is necessary to develop two or three different and complementary prototype systems, to cover some of the variety previously identified for EISs. For example, an application in a closed environment (exercise events) with a relatively small number of users could be complemented with a large open space application for a large number of users, and a virtual system based on web technology.

Applications and prototypes implemented in the early phase of the research program, must be designed in such a way that they easily can work as research platforms, i.e. easy to make changes in, test new concepts, log data, etc. Three candidates have been selected: spinning, collaborative art, and campus communi-ties. The prototype requirements discussed above have been the basis for the dis-cussion and selection of those three ideas.


• Spinning – the main idea is to study the possibilities to enhance the expe-rience of collective exercising with an EIS. This is an exercising event, in a closed room (IKSU, 2001). Generally, there is a wish in this group of actors to measure their performance. The kind of basic set-up that this prototype requires is relatively simple. The actors sit on their bikes, which makes it possible to start with a mostly wired infrastructure. There are many inter-esting research issues concerning social interaction, data, information, and knowledge, data communication, etc., in this prototype. Its greatest advan-tage is that it allows starting in a small scale with a potential to extend the project in many directions. A closed-room event is mostly associated with a classic system design approach, but it also allows us to test ad-hoc/temporary infrastructure solutions, which means that emergent design may be relevant in a longer time perspective.

• Collaborative art – the main idea with this application is to create an event in which a collective produce art by acting and interacting. Appendix C- Collaborative Art, describes one example, which is a combination of an arena event and a public space event. This prototype makes it possible to test an open-room application. The application could be used to test an ad-hoc/temporary network, develop emergent architecture, and try emergent design. Hence, development of simulations and an Emergent Interaction Protocol (EAP) would be two important research issues for the implementa-tion of this application. This prototype has the potential to involve both humanistic parties such as artists and social psychologists as well as tech-nical parties like mobile phone operators and developers of infrastructure solutions.

• Campus communities – the main idea with the prototype is to support the emergence of knowledge, on the individual level and on the community level; i.e. a knowledge society event. This can be done by feeding back information about knowledge activities in the campus area - in the short-term perspective but also in a long-term perspective. The early focus for this prototype is on the virtual room, but it has potential to be an applica-tion in between the physical and the virtual. E.g. analysing informal meet-ings, connect cafeterias, etc. are things that can be implemented in the prototype. Its virtual character will be mirrored in the design of the feed-back loop and the technological choices. Web technology will be the base. This application, too, can be used to test the emergent design approach and ad-hoc/temporary infrastructure solutions. Umeå University is one pos-sible partner, together with providers of infrastructure solutions.

From an Umeå perspective, the proposed applications cover three characteristics of the city and its citizens. First, there are many people in Umeå doing some sort of physical exercise, individually or in groups. Second, the cultural life of Umeå is flowering, with many cultural events. Third, Umeå University and other educa-tion institutes play a central role in the everyday life in Umeå.

All proposals fulfil the requirement of being something to show. In that respect the collaborative art prototype may have the greatest impact, both as a research object and as a commercial object. The character of the room is different in each proposal (closed, open, and virtual). Assuming that the closed and the virtual room prototypes rely on well-known technical frameworks, they may be the easi-est to implement. The campus prototype may be the one that would be most valu-able for the society in large, in particular the near region.

Research and DevelopmentThe pre-study has identified many interesting basic research issues that need to be studied, gathered into 21 focus areas. The four sections Technical System Aspects, Interaction, Sociological Aspects, and Data, Information, and Knowledge cover the focus areas and discuss the related open issues - summarised in the Open Issues subsection above. Emergent design and emergent architecture are two ideas pro-duced by the pre-study, which need to be further developed. We should:


• develop a set of Emergent Architecture Protocols (EAP), which is a communi-cation protocol stack for the emergent architecture

• identify and develop new artefacts (emergent interaction units), based on the EAP, that makes it possible to start implementing emergent interaction systems based on ad-hoc/temporary infrastructures

• develop the use of simulations in the design of emergent interaction appli-cations, involving studies of the basic concept of emergence, and how to characterise the basic primitives for interaction.

Establishing the Emergent Interaction ConceptFor the success of emergent interaction the concept needs to be anchored in the scientific community as well as in the commercial world. There are connections between these two, and the process of concept establishing must include work on the local, national, and international level. This work should have high priority and the pre-study suggests that a special project should be launched to work with these issues. Of course each of the sub projects of the research program must take their own responsibility for establishing the emergent interaction concept. The EI establishing project can work with:

• seminars, locally to establish connections and find potential partners

• presenting the ideas on courses at university level, e.g. design exercises and in master thesis projects where different aspects of EI are in focus

• arranging international workshops

• making use of personal contacts to spread the idea and information about results and plans

• presenting scientific results at conferences, workshops, etc. and in scientific journals

• a web site for the emergent interaction research program. Internet publish-ing encourages people to give comments and ideas for further studies and applications

The Emergent Interaction in a Context section above, discusses some of the chan-nels that this project has access to.

Market studiesEmergent interaction has a potential to generate a market for commercial prod-ucts on many levels. There are signs already today that such a market is beginning to form. Here are some examples of what the pre-study has identified as potential products or markets:

• the EAP has potential to generate ideas for applications, similar to how the development of HTTP has generated ideas for web applications

• given EAP, there is a potential market for EIUs

• emergent architecture has the potential to generate commercial products at the infrastructure level, for example infrastructure for content providers of the 2.5 and 3G mobile phone platforms

If one believes that EI has the potential for this, market studies must be done to study the conditions for commercialising EI-related products.


External financingThe research program obviously needs funding. It is of vital importance to apply for financing from different research councils and funds, and the industrial/commercial establishment. Part of this process and necessary for success is to have good partners from the academia and the industry. The pre-study has formed a basis for this work, and this report will be a useful reference for funding applica-tions.

SummaryWhen this pre-study project was conceived in early 2001, a number of expected outcomes and purposes with the project were discussed. Creating a better idea of emergent interaction and preparing for a larger study was defined as the main purpose of the pre-study. The readers of this report and the people that gave us the assignment should judge if we have succeeded. Of course, we in the project group do believe we have!

Independent of the concrete outcome, the project has been valuable in many ways, on an individual level as well as on an organisation level. We have done our work in an open-minded and creative atmosphere. The way that the group has worked with the twentyone relevant areas (each with one responsible person) has been quite successful. For example, the focus areas have guided the choice of topics for the seminars, and have been used in the organisation of the report (see - Focus area map). The experiences from the project can be summarised as: fun, lots of new experiences, broad, exciting, demanding, and creative.

Appendix A – Ideas on EI-applications 71

Knowledge Society Events

Classroom, learning

Appendix A - Ideas on EI- Applications

+ Involvement

+ Wake up the students

+ Knowledge is an emerging and social phenomena

+ Can make the classroom more appealing

+ Possibilities to prohibiting harassment bullying (mobbing)

– Privacy issues

– Dangerous to loose the control of the system

– Brainwashing

Feedback from a class + Effectiveness of resources

+ Quality

+ Experience/ event

+ New phenomenon /behavior

– Integrity·

– Stress

Keeping track of a research society

Examinations

Finding Information

Self Knowledge

Exercising Events

Dance, disco music + Cool

+ Distributed

+ Atmosphere

+ Dynamic·

+ Profiles

+ Categories

– Removes tradition

– Hard to start

– Risk for self oscillation

Exercise event - IKSU + Nearness

+ Repeated

+ Receptive persons

– Hard to replace the human leader

Training, work out + Distributed

+ More effective

+ Pepping

+ Feedback to the leader

– One-track

– What to measure

– How give feedback

– Unpredictable

Spinning/Team cycling + Easy to measure

+ Closed community

+ Clear border

+ Easy to enhance the experience

+ Good for your health

+ High acceptance for data collection

+ Often regular activity

+ Social activity

– Not everyone is doing it

– Short-term

– Too focused activity

72 Appendix A – Ideas on EI-applications

Home Activity Events

Home economy administration/buy-sell

Eating dinner + Everyday activity for everyone

+ Common experience

+ Social activity

+ You could have a positive influence on what people are eating

+ Not yet IT-polluted

+ Many social navigation possibilities

– Somewhat hard to measure (taste)· Privacy issue

– 9 a clock a front of TV

Cleaning

Reading

Dressing

Sleeping

Public Space Events

Atmosphere detection, estimation/ evaluations/feedback of queues, res-taurants, etc

+ Spread load

+ Quality effects

– Segregation

– No spontaneity

– Less development and considerations

– Unwanted effects

– Standardisation

Down town - cheapest prices, choice of place to go, e.g. restaurant

+ Winning time

+ Individualised

+ Effective use of resources

+ Quality effects

+ Experience/Event

– PDA is needed

– Unwanted effects

– Standardisation

Auctions + Faster

+ Better price

– Destroys the event

– Too fast

– Whom does it benefit?

Campus/Nursing home/Offices + Effective use of resources

+ Experience/Event

+ Safety

+ Humanity

– Integrity

– Stress

Public spaces/exhibitions/waiting room/squares

+ Big diversity of people

+ Opportunity to make new acquaint-ances

+ Neutral (nobody owns the territory)

+ Not common intention

– Privacy

– The will to carry the sensors

– Dangerous to loose the control of the system

– Unclear who should have the control

Shopping areas

Pubs

Libraries


Parties + Common intentional – Drunk people might ruin the equipment (puke warning)

Parking place + Effective use of resources

+ Environmental

– Boring

– Wrong target group

– Small target group

– Competition problems

Professional Events

Dissertation

Meetings

Farming

Problem solving

Product development/design/factory floor

+ Effective use of resources

+ Experience/event

+ Safety

+ Humanity

– Integrity

– Stress

AL forest simulation (distributed VR) + Creates knowledge about forests

+ Easy to collect data

+ Possible to speed up time

– No emergent interaction, or?

Empathy training/development + Solves the lack of empathy in society

+ Focused on human needs

+ Easy to measure data

+ Easy to create adrenalin shocks

– Privacy issues

– Hard to interpret data

Political Events

War

Riots

Gangs, Groups + Control

+ Surveillance

+ Less violence

+ Organisation

– Mobbing

– Crowds

– Injustice

Demonstrations, Police actions + Democracy

+ Economy

+ Right equipment

– More violence

– Escalation

– No surprises

Avoid/track/see crime

Demonstrations

Love-peace-and-understanding

IT-democracy + Easy to measure

+ Everybody

– Security problems

– Does it work, is the result a better democracy?

Simulation of economical systems

74 Appendix A – Ideas on EI-applications

Communication Events

Car-pooling + Effective use of resources

+ Environmental

+ Humanity

– Computational problems·

– Stress

– Security

Subway - Where should I stand to find a place to sit

+ Huge population – Not in Umeå

Tourism/Round trips/guiding

Charter

Silent communities/bus/ train + Repeatable

+ Possible to compare groups in similar situation

+ Nothing else happening

+ No way to escape

– People wants to be alone

– Interference

Traffic -Affect other drivers, traffic flows, emergency vehicle

+ ...of advantage to society

+ Easy to find money for

– Too big

Extended telephone/video conference - Move to a new context, virtual environments/meetings, meet new people

+ Democracy

+ Commitment

+ Distance spanning

+ Economy

– Boring, grey

– Demands capacity

– Not something that is new.

Arena Events

Theatre + Closed area/local

+ The public (as paying) allows to decide more

+ Can create more interaction

– Demands that you engage in the play

– Does it have any positive effect?

– Huge demands on the actors

Sports arenas/ Track and field/down hill/orienteering

Formula 1 + Money

+ It would help the drivers, the teams at the pit stops

+ Few events

– Expensive system

– Secrecy

– Does it already exist?

Festival - Where is it best?Where is it most crowded?

+ Individualised – Very few events

Concerts + Enhances experience

+ The right songs

+ Fewer extra tracks

+ Less tomatoes thrown

– We don’t want to choose

– No new songs

– Dependent of the audience

Survivor like programs/shows. (Docy soap) - Switch context, connects exist-ing shows

+ Commercial interests

+ Hot

+ Everyone is a participants, 24-7.

+ Collaboration with (Strix)

– Difficult industry ( TV )

– Expensive prototype

– Loads of technique.

Film/Movie

Net Work Games

Top charts


“City battle”++ + Already based on a on interaction – Once a year event

– Whom profits?

Create Collaborative art games/competitions

+ Experience/event

+ Fun

+ Humanity

– Just a game

Pokemon, DigiMon, DigiBird applica-tions.

+ Enormous market

+ Easy to find test pilots

+ Playfulness

+ Easy to please

+ Many existing concepts to expand

+ Tokyo as a lab (DoCoMo)

+ Brainstorm with children/youths

– Difficult to understand the market

– How to charge?

– What is new?

Sound/Voice controlled cameras moni-toring audience reactions. The sound level is collected among the audience and a server calculate where there is most activity and the camera is directed towards those people. The image can be presented on a big screen TV.

Art happenings - Exploring new ways to express art and to make art happen in real time

+ Maybe a growing area ·

Community Events

Communities, what’s happening + Individualised – Where to find the people

– What do they have in common?

Religion/ New age

Social life flirt, etc

Instant Communities - create groups/communities (instantly) whenever people get together

+ The group could later on be used to send out information, requests, and reminders.

+ Groups could be created afterwards from logged data (who was there and personal profiles) when a need is iden-tified.

What/Who is hot? (ICQ, Communities) - Non-pleasure situations (Is there a doctor present?), search criteria’s, opposite attraction

+ New

+ Cool

+ Business and pleasure

+ Young market

+ Scalable

– Hard to get started

– Critical mass

– What terminals to use?

– Uniform

Community building/hobbies

Hunting

Agriculture

Gardening

76 Appendix B – Focus Area Map

Application areas

Main topic

Categories and characteristics

Communities

Communication

Computability Aspects

Data Collection

Democratic and Undemocratic systems

Feedback

Model

Physical-Virtual Systems

Presentation and Interaction

Sociological Aspects

Control Systems

Scenarios

Security

Simulation

System Architecture

Timing

Traffic Patterns

User Awareness

Section in the report

Focus Areas Ap

pli

cati

on

s

Em

erg

en

t In

tera

ctio

n

EI

Sce

nar

ios

Inte

ract

ion

Tech

nic

al S

yste

m A

spe

cts

So

cio

lgic

al A

spe

cts

Dat

a, I

nfo

an

d K

no

wle

dg

e

System Awareness

High relevance

Relevant

Unclear

Appendix B - Focus Area Map

Appendix C – Collaborative Art 77

Appendix C - Collaborative Art

The basic idea with this application is that a collective (which may be distributed over a wide area) will produce art together. The work of art, for instance a picture, will emerge from the behaviour of the members of the collective, using the work of art itself as the feedback. (Alternatively, one might choose to see the behaviours and behaviour patterns as the real work of art, like a performance or a happening.) Different groups control different aspects or parts of the work of art. The group concept is very broad. For instance, groups can be defined spatially, logically, etc.

Figure 15. A naive example of col-lective art, with some graphics and message displays.

Figure 15 exemplifies the idea of collaborative art. Four major commercial areas of the city of Umeå define the groups. Each group controls one part of an image of a human body. The behaviour in each group (perhaps normalised with respect to group size, etc.) can determine which part of the body a group controls and the shape of that part. The size of the controlled parts can also be determined in a similar way. Another variant is to randomly associate a part of the body to a particular group.

This work of art also includes some “text displays”. These can be used for com-mercial purposes, to control the flow, or to generate collaborative poetry or stories compiled and edited from SMS messages contributed by the actors.

It is possible to play with the democracy dimension in this application by varying the actor’s impact on the feedback, triggered by quite specific individual actions. E.g., a person that has just bought a pair of shoes could be rewarded with total control over the shoe-part for the nearest fifteen minutes. One could have a lottery as part of this application, where the participants “get” lottery tickets simply by being in the region for the application.

Technical aspects: the application could use large public displays in each area; given 3G mobile phones, they could be used as personal displays; also RDS-radio messages could be used to distribute feedback. The application is scalable, both in the number of participant, and from a technical perspective with the introduc-tion of new technologies. Genetic algorithms can be used to generate the appear-ance of each part of the image. Shannon’s theories (Shannon, 1948) can be used to generate poetry and stories from the SMS flow, similar to how the “Shannon-izer” (Shannonteam, 2002) transforms a given text into a “Shakespeare text”. Also other algorithms could be used to compute textual feedback, e.g. genetic algo-rithms, or LSA together with text summary algorithms.

UMEÅ

DOWN-TOWN

OBS

STRÖMPILEN

BJÖRNVÄGEN

JFIE JREFJ OS JGIJG AOPE AÄIOJ AG

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