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Coordination and emergence in designKaterina Alexiou aa The Open University, Design Group, Milton Keynes, UKPublished online: 22 Jul 2010.

To cite this article: Katerina Alexiou (2010) Coordination and emergence in design,CoDesign: International Journal of CoCreation in Design and the Arts, 6:2, 75-97, DOI:10.1080/15710882.2010.493942

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Coordination and emergence in design

Katerina Alexiou*

The Open University, Design Group, Milton Keynes, UK

(Received 25 January 2010; final version received 10 May 2010)

Over the last few decades there has been a growing interest in the concept ofemergence in design research. Despite this interest, the meaning and scopeof emergence in design is not clear. Indeed, there are two different views ofemergence in design literature, representing different types of theories aboutdesign. The first is focused on individual cognition or perception, and the secondis focused on social aspects of design activity. This paper grapples with thequestion of how we can reconcile the two perspectives in a theory of design as anemergent phenomenon. More specifically, the paper builds a model of design as adistributed process that links together cognitive and social dimensions of designactivity, and uses this model in order to elucidate the meaning and role ofemergence in design. Overall, the paper explicates the relation between emergence,complexity and coordination as a vehicle for linking individual and socialconceptions of design.

Keywords: coordination; collaboration; emergence; creativity; complexity

1. Introduction

Over the last few decades there has been a growing interest in the concept ofemergence as a characterisation of design phenomena. But although we canintuitively connect emergence with design thinking or design activity, the status orrole of emergence in design is not clear. In design research, emergence is morecommonly discussed in relation to the visual discovery of previously unrecognisedshapes or forms in designers’ sketches and drawings (e.g. Soufi and Edmonds 1996,Suwa et al. 2000). In this context, the concept of emergence, associated withspontaneous or unexpected discovery and innovation, has been increasingly used inorder to describe the creative design process. This research is often related to shapegrammars, a mathematical/computational language which studies the generation ofspatial configurations based on rules of shape replacement and transformation (e.g.Stiny 1994, Oxman 2002, Knight 2003). However, in design, emergence is aphenomenon that does not only apply to the recognition of shapes, but also appliesto the recognition of functions and behaviours of artefacts (Cross 2006). Theconception of emergence as discovery of structures, behaviours and functionsassumes the existence of a cognitive agent that is able to observe, recognise andre-interpret what he or she sees.

*Email: a.k.alexiou@open.ac.uk

CoDesign

Vol. 6, No. 2, June 2010, 75–97

ISSN 1571-0882 print/ISSN 1745-3755 online

� 2010 Taylor & Francis

DOI: 10.1080/15710882.2010.493942

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Another way in which the concept of emergence has been introduced into therealm of design is through the adoption of methodologies and theories comingfrom distributed artificial intelligence, artificial life and multi-agent technologyapplied to the development of computational models and systems for generatingnovel design solutions (e.g. Saunders 2001, Hensel et al. 2004). Looking at multi-agent systems in particular, the notion of emergence has a more technical meaning,and refers to a process of generating new structures, behaviours and functions‘from the bottom-up’. The term bottom-up is used to indicate that solutions arenot obtained through direct modelling of the desired outcome (‘top-down’modelling), but they are formulated as an aggregate effect of distributed processescarried by a number of individual entities (agents) that interact with each other.This notion of emergence does not necessarily assume the existence of a cognitivefacility, but rather places emphasis on interaction as the basis of the creation ofglobal emergent phenomena.

In these two roughly defined views of emergence we can immediately detect theexistence of different foci and different conceptualisations of design. The first type ofresearch places emphasis on visual recognition (and particularly on how individualsperceive shapes, behaviours and functions), to a great degree ignoring the dynamicsof the interactions developed between individuals in a social context. The secondtype of research places emphasis on interaction and distributed processes of creation,but largely seems to ignore the role of individual cognition (although see Sosa 2005).Both views, however, capture important aspects of emergence, the first referring tothe ability to observe and recognise design solutions and the second referring to theprocess of creation through distributed action.

So the different views of emergence in fact represent different types of theoriesabout design: the first focused on individual cognition or perception, and the secondon social aspects of design activity. However, the reconciliation of these two types ofdesign theories is an open question in the design literature. In this paper we grapplewith the question of how we can reconcile the two in a theory of design as emergentphenomenon. The contribution of such a theory is that it links the individual andsocial level of design activity and offers a novel understanding of how cognitive andsocial creative processes co-exist.

The paper aims to develop the basis for such a theory of design by adopting aconstructive methodology. That is, instead of analysing a specific design situation,the paper starts with the identification of critical principles and dimensions of designin order to synthesise them together in a coherent way. The paper does not aim tomodel a particular system or process, but it seeks to construct a theoretical model (orframework) that can in turn be used in order to observe reality and eventually guidethe development of appropriate tools to support the design process.

More specifically, this paper is divided into three sections. The first section buildson existing conceptions of design as a distributed process in order to formulate amodel that links together cognitive and social dimensions of design activity. Inparticular, this section introduces the concept of coordination as a vehicle formodelling design. In the second section we present and discuss some fundamentalaspects of emergence as have been elaborated in other disciplines, predominantly inphilosophy, artificial intelligence and complexity science. This review is used in orderto derive important insights on how emergence has been and should be looked at indesign. The third section brings insights from the two previous sections together.Specifically, this section analyses and explicates the link between emergence,

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complexity and coordination in order to establish an understanding of how theseconcepts can serve as a glue for linking individual and social conceptions of design.

2. Design as a multi-agent coordination process

Design is normally regarded as a purposeful activity aiming to generate descriptionsof artefacts that will satisfy some perceived idea, need, or goal. Design is alsoperceived as a complex social process, which involves multiple agents, such asdomain experts, clients, stakeholders and users. These distributed agents need tocoordinate themselves and their knowledge, resources, goals and requirements inorder to produce a design object, process or artefact. The distributed exploration,generation and evaluation of creative design solutions towards a coordinatedoutcome, is the quintessence of design as a social process. Both the dynamics ofagent interaction and the final design solution or product are emergent, as theycannot be well defined or planned in advance. This paper puts forward the idea thatthe emergent phenomenon of design can be more explicitly described as acoordination process. Coordination is in a sense the ‘problem’ or ‘objective’ ofdesign activity, but also the underlying mechanism that makes this distributedprocess work.

A very helpful and comprehensive definition of coordination is given by Maloneand Crowston (1990) as ‘the act of managing interdependencies between activities toachieve a goal’ (p. 361). The problem of managing interdependencies is common inall design domains; examples include establishing translatable relationships betweendifferent representations (for instance, between structural and architecturaldrawings), synchronising the exchange of information, ordering activities, establish-ing roles and delegation structures in organisations, and many others.

Defined in this way, the concept of coordination encompasses conflict andcooperation and can be broadly applied to a variety of goal-oriented activities.However, the subject of investigation here is not a general area of management, butthe activity of design very specifically. Although there are studies of coordination indesign, these are predominantly focussed on management of activities, conflictresolution, synchronisation or communication (e.g. Duffy et al. 1993, Klein 1995,Perry and Sanderson 1998, Cumming 2002, Petre 2004). So there is a need forunderstanding coordination also in relation to questions of exploration andgeneration of novel or creative design solutions.

The concept of coordination has also been very important in the field ofdistributed artificial intelligence (DAI) and multi-agent systems (MAS). DAI andMAS approaches in general aim to develop systems composed of individual(artificial) agents that have limited knowledge and resources, information processingabilities, and viewpoints, and thus need to engage in some kind of collective activityor interaction in order to improve their problem-solving and goal-attainmentcapabilities. The term distribution here is critical: it suggests existence of incompleteand dispersed information, interdependency of actions and decisions, and lack ofglobal control mechanisms or rules to dictate global behaviour. Coordinationtherefore becomes instrumental and might refer to effective ordering, synchronisa-tion, planning, and adaptation of actions, decisions and goals (Ferber 1999). It isinteresting to note that from the inception of the field there has been a dividebetween approaches focused on the development of appropriate ontologies, rulesand shared conventions as a basis of coordination, and approaches focused on local

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agent interactions and the idea of building coordinated group behaviour ‘from thebottom up’ (for an overview see O’Hare and Jennings 1996, Sycara 1998, Ossowski1999). The latter have strong links with research in artificial life, as well asbehaviour-based robotics (e.g. Steels and Brooks 1993). These approaches havegrown to be more dominant in recent years, and together with dynamical approachesto the emergence of coherent global behaviour (e.g. Atay and Jost 2004, Jirsa andKelso 2004) have become seamlessly incorporated in the core curriculum of complexsystems science.

2.1. Basic principles of coordination as an abstraction of multi-agent design

Following this line of thought, the present paper considers that design should beapproached by focusing on how distributed goals, knowledge and activities areorganised together in order to produce a consistent whole. Coordination can be usedto express the complex interplay between individual agents that leads to thegeneration and reconciliation of goals and design solutions. What seems to beparticularly needed is a concept of coordination that incorporates three character-istic features of design.

First, in multi-agent design the knowledge necessary for carrying out a task isfragmented and distributed among local agents (see e.g. Fischer 1999). In this sense,distributed design agents need to be able to learn (acquire and collectively construct)the knowledge necessary for carrying out the design task. An appropriate notion ofcoordination for design thus should assimilate such a learning process.

Additionally, in distributed design, each agent is driven from individual goalsand requirements. Decisions are taken at a local level without any external,centralised source of guidance or control. In line with the view coming from complexsystems science and MAS that we saw above, coordination should incorporate theidea that design solutions emerge from a process of decentralised, local control.

In contemporary theories and models of design the exploration and articulationof goals alongside the exploration and generation of solutions is considered to be ahallmark of creative design. This approach is often labelled co-evolution (Maher2000, Dorst and Cross 2001). In distributed design, because the different agentsexpress diverse and conflicting goals, the notion of identifying, creating, and re-creating goals is a necessary part of the collective design process. Coordinationshould then be understood as a process of parallel exploration, generation, andreformulation of problem and solution spaces.

2.2. A conceptual model of design as coordination

Let us now formulate a conceptual model of design as coordination based on thethree characteristic features identified above. The focus will be on explicating thegeneral mechanisms, or principles that underlie the coordination process.

2.2.1. Modelling co-evolution as iterative transformations between functions,behaviours and structures

Descriptive and prescriptive models of the design process typically include the tasksof analysis, synthesis and evaluation. More specifically, the design process iscustomarily modelled as an iterative process where analysis, synthesis and evaluation

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are structured in a series of loops (that can be visualised as a kind of spiral) whichmoves from the more abstract to the more specific. This process begins with abstractfunctions or needs, and ends with a satisfying form or structure for the designartefact. Although other components may also be added in this general model (forexample, communication) and although different pictorial representations may beused, the core of the idea remains the same – compare, for example, Pahl and Beitz(1984), French (1992), Roozenburg and Eekels (1995). Existing models of the designprocess also generally assume that design is essentially concerned with devisingspecifications or descriptions of artefacts (physical or not) that satisfy some purposeor function. In fact, it is typically held that what distinguishes design artefacts fromnatural objects (and organisms) is that artefacts are intentionally constructed toserve specific functions. In design, it is essential to be able to represent, manipulateand reason about functional, as well as behavioural and structural informationembedded in artefacts. But what do we mean by structure, behaviour and function?

The Function–Behaviour–Structure (FBS) framework proposed by Gero (1990,2000) offers a way to explicate and understand the meaning and role of functions,behaviours and structures within the iterative design process. For this reason, in thispaper we utilise and adapt FBS as a basis for modelling coordination andexemplifying its dimensions. The framework also offers a way to discuss design in adomain-independent way. Let us explore the framework in a bit more detail.

In Gero’s FBS framework, function is generally tied to the purpose of anartefact; structure relates to the form and organisation of the components of anartefact; and behaviour refers to the operation of an artefact through which aparticular function is satisfied, or to some observation or measurement applied overa particular structure. Gero distinguishes two types of behaviour: structuralbehaviour which is directly derived from structure, and expected behaviour whichis derived from function. Gero identifies a set of processes by which a designer movesfrom function to structure and thereby to an appropriate design description.

More analytically, these processes are:

(a) analysis, where the behaviour of a structure, Bs, is deduced from thestructure, S;

(b) formulation, where function, F, is mapped to expected behaviour, Be;(c) synthesis, where the expected behaviour, Be, is used to produce a structure, S,

based on knowledge of the behaviours, Bs;(d) evaluation, where the structural behaviour Bs is compared with the expected

behaviour, Be, to determine whether the synthesised structure can satisfy thedesired function;

(e) reformulation, where the range of expected behaviours, Be, can be changed,and through them the function; and

(f) design description, where the structure is developed into a description of theartefact to be constructed.

In later papers (e.g. Gero and Kannengiesser 2002), two other types ofreformulation are also added: one which refers to changes in terms of structurevariables or ranges of values for them, and one which refers to changes in terms offunction variables or ranges of values of them. For a summary see Table 1.

From this presentation, analysis and synthesis seem to be processes concernedwith the development of structures or solutions that will satisfy certain requirements,

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whereas formulation and reformulation are processes concerned with the definitionof the requirements for the design artefact. Hence, the processes of analysis andsynthesis can be taken to represent operations that concern the solution space,whereas formulation and reformulation concern the problem space. This way ofinterpreting the FBS framework gives a means to understand and model how co-evolution of problem and solution spaces may be realised. This is another importantreason for utilising and adapting the FBS framework. The conceptual model ofcoordination introduced here develops this idea and recasts the processes of analysis,synthesis, formulation, reformulation and evaluation within the context ofdistributed learning control. Before we elaborate this idea further, let us makesome critical observations about FBS.

It is important to note that the FBS framework is often criticised for itsconception of function. Coming from an engineering perspective, Gero considers thesatisfaction of an intended function to be the driving force behind design. However,many design processes start from ill-defined purposes, or even abstract ideas andvisions, in which case the concept of function may be seen as problematic. Let usdwell on this concept for a while.

At the beginning of this subsection, it was stated that artefacts are distinguishedfrom natural objects because they are intentionally constructed to serve specificfunctions. A notion of purpose or intentionality seems to be intrinsic in the notion offunction. An artefact, of course, may have unintended functions, so there is adifference between function seen as an activity (what an artefact does) and functionseen as utility (what use an artefact is supposed to serve). Some reserve the term‘function’ to refer to the second case only, but function does not necessarily implyutility. On the other hand, function can also be perceived as an assignment of ameaning, a name, or a label over a particular structure; in this sense, function is aninterpretation of structure that characterises what the structure is, rather than whatit does. In this sense again, function may be an abstract concept, a need, an abstractactivity or an abstract idea, which is both the driver and the final destination ofdesign. For an interesting and comprehensive discussion about the meaning and roleof function in biological and artificial (designed) systems, see Buller (1999).

The position adopted in the paper is that function is indeed both the driver anddestination of design, but it is not seen strictly as a description of what an artefactdoes. So behaviour is a kind of mediator between structure and function: thestructure impinges on the behaviour of an artefact, the behaviour achieves thefunction, and on the other hand function entails behaviour and drives the generationand selection of structures. A full critique of the FBS framework is beyond the scope

Table 1. Summary of the processes involved in design as discussed by Gero andKannengiesser (2002).

Analysis: S ! Bs

Formulation: F ! BeSynthesis: Be ! SEvaluation: Be $ BsReformulation: S ! Be

S ! SS ! F

Design description: S ! D

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of the present paper. For more discussions about function in FBS and othercriticisms see (Vermaas and Dorst 2007, Galle 2009).

Another issue with FBS that is particularly important for the present paper isthat the framework was basically developed as a model of individual or single-agentdesign. Although an attempt has been made to account for social aspects (Gero andKannengiesser 2003) the discussion offered is quite limited since the specifics ofagent-to-agent interaction are not explained or modelled in detail. The coordinationmodel proposed in this paper uses the idea and mechanics of distributed learningcontrol in order to account both for individual activity and the social interactions,conflicts and interdependencies developed between agents.

2.2.2. Coordination as distributed learning control

To explicate the notion of distributed learning control and how it can be used tomodel multi-agent design, we use some insights from cybernetics, the science ofcommunication and control (Wiener 1948).

2.2.2.1. Distributed learning control: some basic concepts. The notion of control wasused in the early days of design research to model design activity. For instance,Archer (1970) approached designing as an iterative process of generating andcontrolling a set of (decision) variables in order to optimally fulfil a given set ofobjectives and performance criteria.

Control refers to actions taken by a system with the purpose of achieving ormaintaining a target state, despite disturbances or perturbations coming from itsenvironment. Control then necessarily assumes a relation, or interaction, between acontrolling system and a controlled environment. So, control systems are systems inwhich a controller interacts, by way of one or more controlling variables, so as toinfluence the state of a controlled object. At the heart of control theory lie twoimportant complementary notions: controllability and observability. Briefly,controllability is a measurement of the ability of a system to manipulate the statesor outputs of the controlled object, while observability is a measurement of theability of a system to predict all the states of the controlled object on the basis ofobservations of its outputs. A simple control system can be illustrated as shown inFigure 1, where boxes represent components that carry out different operations andarrows represent inputs and outputs.

When we have a situation where the parameters of the system to be controlledvary in time, a control strategy is also needed that adapts itself to the changingconditions in order to be effective. This is the domain of adaptive control. In general,the efficiency of the controller can be improved by learning – gaining knowledgeabout environment behaviour and the available control variables. In this case,another box (operation) is added to the control diagram to represent this learningmechanism. The learning mechanism generally aims to replicate the dynamics of thecontrolled system using a measurement of the ‘errors’ or ‘distance’ between theactual behaviour of the controlled system and the behaviour produced by the model.

A cybernetic view of design is useful for capturing the goal-oriented nature ofdesign activity, specifically when design goals vary in time and design problems arecharacterized by complexity, uncertainty and non-linearity. However, the earlyformulations of design as control failed to capture the idea that the goals of thedesign process are not defined in advance. As discussed earlier, this is particularly

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important in distributed design, where goals are distributed and indeed cannot bedefined in advance. For this reason we use here the notion of distributed ordecentralised learning control. The main difference between the traditional view ofcontrol in design and the one proposed here is the idea of distributing the designprocess into different agents that have their own goals and learning capabilities. Inother words, it is assumed that there is no central entity able to control the overallprocess or impose goals a priori. Additionally, goals and performance criteria areconsidered to be endogenous to the design process (i.e. they are not provided by asource external to the agents).

2.2.2.2. The model of design as distributed learning control. More analytically, adistributed design task involves a number of agents that act on a common worldwhilst trying to achieve their individual visions about this world. Thus, there is adirect interaction between an agent and its environment, which includes otheragents. As each agent has limited control over the whole state of the world/designspace, any individual activity is influenced by the activity of others.

To illustrate this (Figure 2), let us consider a simple example where we have anumber of agents acting on a collective space. In this example, the collective spacecontains objects (represented as cuboids) that the agents generate and modify. Eachobject and the overall composition of objects in the collective space can be describedin terms of functions, behaviours and structures.

Figure 1. A simple control system.

Figure 2. A simple illustration of a multi-agent design situation. We have a collective designspace composed by cuboids. Each agent controls a single cuboid by manipulating itsfunctional, behavioural and structural dimensions. Each agent has individual goals and noagent can control the overall configuration.

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Each object has a different function; consider for example that each cuboidencompasses a different activity or land use which can be represented by a differentcolour (medium grey for retail, light grey for housing, dark grey for open space).Structural information describes the physical characteristics of the objects and theirtopological relations. For instance, for each object, structural information mayinclude location, volume dimensions and relations with other objects, such asdistance from other facilities. Behavioural information specifies the way each objectreacts to changes of its state and its environment in order to reach its intendedfunction. For instance, behaviours may describe land use attractiveness (tendency ofland uses to be attracted to – or repelled by – other facilities) and change of distancebetween objects. Behaviour may also refer to observations or measurements appliedover structures, for example cost (derived in relation to land value and floor area).Each agent is able to change the function, location and dimensions of a cuboid, thusaffecting the functional, geometrical, and spatial relationships expressed in theoverall configuration.1

The crux of the idea of distributed learning control is that each agent hasindividual goals about the overall configuration but is able to control only partsof the overall design (by manipulating functional, behavioural and structuralvariables). In this sense, the notion of distributed control encompasses the ideathat each agent is also to a certain extent ‘controlled’ (constrained as well asenabled) by the actions of others. The controlling activity of each agent isfacilitated and augmented by a continuous process of observation and learning.This process is crucial for the generation and reformulation of design solutions aswell as goals.

More specifically, each agent is considered to carry-out two combined control-based activities. The first control activity alludes to an analysis–synthesis–evaluationroute and the second alludes to a formulation–reformulation–evaluation route. Wecan illustrate these two control activities with diagrams such as those typically usedin cybernetics (Figures 3a and 3b). In these diagrams, boxes represent componentsthat carry out different operations (transformations or associations between FBSvariables) and arrows represent inputs and outputs. The circles denote a comparisonor measurement of ‘error’.

In the diagrams, the box labelled ‘Control’ represents the controlling activity ofeach agent and the box labelled ‘World/Design Space’ represents the space whereactions and decisions of agents are expressed. The boxes labelled ‘World Model’ and‘Reference Model’ represent learning mechanisms, processes of capturing inter-dependencies between design (FBS) variables. The world and reference models areboth learning mechanisms but they have different functionality. The world model isused to inform the generation of control actions, whereas the reference model is usedto derive control targets (acts at a meta-level providing a reference target for thecontrol process). To reflect this difference, we need here to distinguish two differenttypes of functional and structural behaviours. Expected behaviours (Be) arefunctional and structural behaviours that are derived from the world model, sothey express an agent’s beliefs about what is the state of the world and how otheragents operate within it. Because expected behaviours are not accurate representa-tions of the real world (we have limited capacity to learn and understand what goeson around us), we can detect errors between expected behaviours and actualbehaviours (i.e. what ‘really’ happens in the world). Desired behaviours (Br) arefunctional and structural behaviours derived from the reference model and represent

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an agent’s desires about the state of the world, i.e. what the agent wants the world tobe like.

Let us examine this model in more detail by explicating the dual controlactivities.

The first control activity alludes to an analysis–synthesis–evaluation route(Figure 3a). The objective of each agent in this case, is to synthesise a suitable path orclass of structures, S (control actions), that can lead the behaviours, Bs, to follow thetarget behaviour, Br (derived by a target function F), despite uncertainties and

Figure 3a. The model of coordination as distributed learning control: the Analysis–Synthesis–Evaluation control activity.

Figure 3b. The model of coordination as distributed learning control: the Formulation–Reformulation–Evaluation control activity.

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despite exogenous disturbances, d, produced by other agents’ decisions. The worldmodel represents an agent’s ability to learn and capture associations betweenstructures, S, and behaviours, Bs (analysis). There are two ‘errors’, denoted by E,that are used in order to evaluate the analysis–synthesis process. The first is acomparison between expected and actual structural behaviours (Be–Bs) and it is usedto inform the construction of the world model and the generation of control actions,S. The second is a comparison between the reference behaviours, Br, and the actualbehaviours, Bs, and it is used in order to guide the control process (the selection ofappropriate control actions S).

The second activity alludes to a formulation–reformulation–evaluation route(Figure 3b). The objective of each agent in this case is to find a suitable function, F,leading to such behaviour, Br, that can satisfy a given structural configuration, S(reformulation), despite uncertainties and despite exogenous disturbances, d (i.e.other agents’ goals). In this case each agent learns associations between proposedfunctions, F, and the behaviour, Bf, which are modelled by the world model asassociations between F and Be) (formulation). Again, there is a double evaluationprocess: the error, E, between Be and Bf is used for the construction of the worldmodel and the generation of suitable control actions, F. The error between referencebehaviours, Br, and the actual behaviours, Bf, is used in order to guide the controlprocess (the selection of appropriate control actions, F).

Although perhaps not immediately evident from the figures, the two controlprocesses are intimately linked as they provide targets and constraints for oneanother: the desired performance of the analysis–synthesis process is evaluatedthrough the formulation–reformulation process and vice-versa. In other words, theworld model constructed in analysis–synthesis process is used as reference for theformulation–reformulation and the world model constructed in the formulation–reformulation process is used as reference for the analysis–synthesis.

It is important to note the different roles of the world model and the referencemodel. The world model essentially forms expectations or beliefs about the futurestates of the world on the basis of observations of current and past states of thisworld. The acquired knowledge then is used to guide the generation and selection ofcontrol actions able to satisfy given goals or desires. The reference model is alsocreated on the basis of knowledge about the world and observations about theconsequences or effectiveness of actions. Its role, however, is to provide new goals,targets or desires that guide the design process. As discussed, the creation of goals, inparallel to the creation of design solutions, is a very vital requirement for creativedesign in general, and coordination in particular, where these processes aredistributed. We should keep in mind that goals in this model are formed both forthe synthesis (solution formation) and the reformulation (problem formation)processes.

It is also important to note that expected behaviours in the original FBSframework are strictly considered to be behaviours derived from function. There isno account for expected behaviour as behaviour derived from a model of the world,or knowledge about the effects of other agents’ actions. Here we make a distinctionbetween expected, desired and actual behaviours.

An alternative representation of the idea of distributed learning control is givenin Figure 4.

According to this figure, each agent observes activity and changes in the world(by capturing associations between design variables) and uses this knowledge to

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generate actions that will lead to desired solutions. Hence, each agent constructs aworld model. Each agent also reformulates goals and desires based on observationsin the world (reference model). In this way the problem space and the solution spacecan be said to co-evolve together.

This conceptualisation also recognises that each agent’s goals and solutions may beconflicting and explicitly makes this part of the design process. Each agent therefore isable to control only a part of the overall design. But because of the fact that agents’goals and actions are interdependent, each agent is also to a certain extent ‘controlled’(constrained as well as enabled) by the actions of others. It is important to note thatdisturbances are not only considered as a source of conflict here; they also constitute asource of variation and hence an opportunity for novelty for individual agents.

2.2.2.3. Discussion. In the proposed model, the distribution of the design process indifferent agents, that hold individual knowledge and targets, implies the possibilityfor global phenomena to emerge through the interaction and self-adaptation ofagents at the local scale. There is no explicit collective goal, but coordinatedsolutions are thought to emerge from the distributed learning control process.

As discussed previously, emergence is not only about the phenomenon of seeingor recognising shapes and forms, but more generally it can be associated with thegeneration and recognition of some new attribute (structure, behaviour or function)of the design description or artefact, which has not been initially expected oranticipated.

At this point it is important to reflect more deeply on the concept of emergence,explore its meaning in various domains, and gain a more thorough understanding ofhow it becomes materialised in the proposed theoretical model of design.

3. Understanding emergence

We have already seen some ways in which the concept of emergence has entered thedesign literature. However, while there are links between design and emergence, as

Figure 4. An abstract representation of the activity and relationship between agents in thedistributed control model.

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they both embody creativity and unexpected discovery, there are also antitheses;while emergence is a term used to characterise unplanned, self-organisingphenomena, design denotes purposive exploration and thoughtful planning. Inpart, the ambiguity of the relationship between design and emergence is due to theambiguity of the concept of emergence itself. The concept is open to many differentinterpretations and has been critically debated in many disciplines, from psychologyand cognitive science, to biology and physics, to social science and philosophy. It hasbeen central in great scientific and philosophical discussions about the mind–bodyproblem, holism, irreducibility and the independence of sciences, with no clearconsensus. The purpose of the following brief review is not only to introduce themain aspects of the debate on the meaning of emergence, but also to explicate inwhat sense design can be treated as an emergent phenomenon.

3.1. The concept of emergence and its relation to design

The concept of emergence is customarily associated with the dictum ‘the whole ismore than the sum of its parts’. The dictum was originally articulated by Aristotle(Metaphysics 10f–1045a) and later adopted by general systems theorists (Bertalanffy1968). In reality, almost every single word in this statement has been, and continuesto be, the subject of rigorous debate and dispute (even the word ‘sum’: see Kubik2003). More recently, the concept of emergence has become the centre of attentionfor the science of complex systems. In fact, emergence has come to be regarded as theepitome of complex systems, so definitions of emergence are very closely linked todefinitions of complexity. More specifically, in this context, emergence has beenpredominantly associated with phenomena where micro behaviours and interactionsbetween ‘parts’ of a system lead to qualitative changes at the macro level of thesystem functioning. This new state of the system at a macro level is often perceived asa new ‘order’ that can be defined or measured in relation to complexity (for example,using measurements such as entropy or information). For more comprehensivereviews, see O’Connor and Wong (2002) (which focuses on philosophical issues), aswell as Bonabeau et al. (1995) and Corning (2002) (which focus on artificial life andcomplexity). The appeal of the concept of emergence comes from the demarcation ofa particular distinction (or relation) between wholes and parts (or between the macroand the micro); this is also where all the difficulty with the concept comes from. Thestatement considers that parts and wholes must be somehow different (summing upthe parts doesn’t simply produce the whole, hence the whole is qualitatively differentfrom the parts), yet there must be a relation between them such that the partssomehow make up the whole (they are ‘parts of’ it).

At a first level, the idea that a whole may be more than the sum of its partsdefinitely resonates with what we know about the nature of design activity. Designactivity can readily be seen as a process of bringing together diverse tasks, cognitivefunctions, people, goals and competences to produce a new whole with newproperties that could not have been uniquely prescribed given the properties of theseindividual ‘parts’. To put it differently, design activity can be considered as anemergent phenomenon and the product of design activity – a design object ordescription of a design object – can be understood as an emergent entity.

At a second level, the view of a design object as an emergent entity often assumesthe existence of an agent that perceives this entity and its properties, and soemergence is defined as a concept relative to an observer/designer. In this context,

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design is not necessarily an emergent phenomenon itself, but rather emergence is aphenomenon that typically occurs within design activity, when novel propertiesspontaneously appear in the eyes of designer as he or she reflects with that task inhand.

In this paper we will take into account both aspects of the relationship betweenemergence and design and look both at design as an emergent phenomenon (wheredesign objects are emergent entities) and emergence as part of design activity.

3.2. The nature of emergent phenomena

A number of views of emergence are based on the idea that certain macro states of asystem cannot be reduced, deduced or predicted from (knowledge of) the propertiesof its parts. Basing the argument on the grounds of knowledge raises the issue ofwhether emergent phenomena are ‘in principle’ irreducible or simply unpredictable.To put it differently, would a complete knowledge of parts and their propertieseliminate emergent phenomena? Or could emergent phenomena be explained ordescribed a posteriori even if they could not have been predicted a priori?

Such questions led to a discussion around the epistemological or ontologicalstatus of emergent phenomena (Peacocke 2003, pp. 189–190). An epistemologicalview of emergence considers that emergence materialises as a characteristic of thelimitations of human knowledge or the descriptive apparatus employed tounderstand reality. Ontological emergentism by contrast denies this assertion andattributes genuine novelty and distinct characteristics at the macroscopic level. Thereare various subtle arguments against the ‘ontological’ version of emergence (see, forexample, Kim 1999), but we can roughly distinguish two main difficulties. First, if noextra distinct characteristics can be shown to exist at the macro level, then emergentphenomena become epiphenomena. On the other hand, considering that emergentphenomena cannot be derived from their constituents makes the argument somehowextra-scientific.

In design we are faced with a similar conundrum. We can consider thatemergence is a characteristic of design either in principle, because of the open-endedand creative nature of design activity, or because of the distributed nature of designknowledge and the limitations or ‘bounded rationality’ of individuals. In some sensethe question about the nature of emergence is unsolvable, but if we want to make theconcept have an operational and explanatory value, then we need to come up first,with a consistent definition, and second, with a definition that can account for orinclude the different nuances of the concept.

For this purpose, Bedau (1997) proposes the distinction between weak and strongemergence. His definition (for a system, S) states that a ‘macro-state, P, of S withmicro-dynamic, D, is weakly emergent iff P can be derived from D and S’s externalconditions but only with simulation’ (p. 378). This version of emergence is weakbecause it applies to a wide range of phenomena and considers that emergentproperties are in principle predictable. The main idea is that although macro-statesare fully and solely constituted from micro-states and their dynamics, they cannot bederived in any other way except from simulating them directly (i.e. they arecomputationally irreducible). On the other hand, understanding general principlesand laws of macro level patterns and phenomena requires empirical observation atthe macro-level and therefore macro-states are considered to be somehowautonomous from underlying processes.

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Bedau’s idea also brings to the fore the question of observation and descriptionof emergence. In fact there is a considerable body of studies that define emergence inrelation to the creation, or the necessity to create, and use, new observational anddescriptive categories. For the purpose of this paper, we briefly review three corecontributions to the definition of emergence. These will be used in order to definedifferent types of emergence in design and analyse the model of design proposed inthe previous section.

3.3. Core definitions of emergence

3.3.1. Emergence relative to a model

The first contribution is related to Rosen’s (1985, 1991) elaboration of the conceptsof complexity and emergence within the framework of the modelling relation.Modelling relation as a scientific endeavour refers to the establishment of relationsbetween a natural system (an aspect, member, or element of the external world wewish to study) and a formal system (a system we create in order to represent, modeland draw inferences about the natural system). The endeavour of modelling relationrefers to the consistent encoding of a natural system into a formal one so that theinferences developed within the formal system become predictions about the naturalworld. The crux of the idea is that the natural world is constituted by a set ofperceivable qualities, and linkages between qualities, which we call observables: ‘assuch, then, a natural system from the outset embodies a mental construct (i.e. arelation established by the mind between percepts) which comprises a hypothesis ormodel pertaining to the organization of the external world’ (Rosen 1985, p. 47).Rosen associated complexity with the concept of error (the discrepancy between asystem and its model) and related the appearance of bifurcation (emergentphenomena) with our ability to produce enough independent encodings to fullydescribe a given natural system.

3.3.2. Emergence and hierarchical organisation

Another view which associates emergence with the creation of new descriptive andobservational categories is provided by Baas (1994). Baas sees emergence in relationto hierarchical organisation, as the creation of higher-level structures through themediation of observational mechanisms. Central to his argument is the study ofemergence by considering three basic notions: structures (as the primitive entities),observational mechanisms for evaluating, observing and describing structures, andinteractions among entities. He offers a definition of emergence which can briefly bedescribed as follows: a property, P, is emergent at a certain level (S2) – which isconstructed from the set of primitive entities and interactions among them – if theproperty can be observed (and described) at this level but not at the level below it(S1) using the same observational mechanisms. The definition captures the idea thatalthough the higher-level structure is constructed by the interaction between entitiesat the lower level, new observational mechanisms are needed in order to describe theproperty, P. Baas further distinguishes deducible or computable emergence, wherethe observational mechanism is an algorithm or deductive process, from observa-tional (non-deducible) emergence where the observational mechanism is a semanticmeaning function or a truth function. It is also interesting to note that Baas

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considers observational mechanisms to play the role of some type of selectionprocess that guides evolution towards higher order structures and he explicitly linksthis process to the subject of design.

3.3.3. Emergence and relative complexity

A unification of the emergence-relative-to-a-model view and the view that bindsemergence with the creation of high level structures is suggested by Bonabeau andDessalles (1997) via a concept of relative complexity. The definition does not take aposition about the ontological status of emergence, but aims to provide an inclusivegeneralised view that relates emergence with complexity. The work builds onalgorithmic definitions of complexity. In general, algorithmic complexity is definedin relation to the effort needed to describe an observed system. This effort is typicallymeasured as a function of the computational resources needed to complete adescription – for example, in terms of time and space needed, or in terms of the sizeof the description. Bonabeau and Dessalles use a version of algorithmic complexitythat associates complexity with the length of the shortest algorithm required todescribe a system (which can be measured in relation to the number of axioms andrules needed to produce a description). The existence of a minimal description(realised as a program, algorithm, or formal system) is often perceived as ananalogue of the most economical hypothesis that explains a phenomenon. In orderto capture syntactic and semantic aspects of emergence, Bonabeau and Dessallesoffer a definition of relative complexity which includes a clear distinction betweenobservational and descriptive tools. Relative complexity, C, denoted by C(S/D,T), isdefined with respect to a set of observational tools (observables or detectors), D, anda set of descriptive tools (relations between observables), T, used to compute adescription of the detected structures, S. Complexity is then a relative conceptdefined as the difficulty of decomposing (or describing) a system, S, when certaindetectors, D, are employed together with a theory, T, about the interdependencebetween the observables. In this context, emergence is linked with a decrease ofrelative complexity. This decrease reflects a shortening of the overall descriptioncaused by the activation of a higher-level detector (a new observable at a higher levelof abstraction, or a more general model) which substitutes lower-level observations.

3.4. Identifying types of emergence in design

In this brief review we saw that emergence can be seen both as a characterisation ofdesign activity and as a process within design activity.

Drawing on all these discussions we can construct a classification of differenttypes of emergence that can be applicable in design. To do this we start from theconcept of relative complexity proposed by Bonabeau and Dessalles (1997), which asmentioned is able to unify the different perspectives and definitions of emergence. Inparticular, we propose that the notion of relative complexity in design can bespecified by considering that in a distributed design setting, each agent has anobservational capacity (a capacity to observe FBS variables), and a descriptivecapacity (a capacity to describe and generate relationships between these variables).The observational capacity of each agent depends on the variety of the designvariables; so the set of observables available to an agent (which Bonabeau andDessalles call detectors) is increased or decreased depending on the number (the

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variety) of the variables. The descriptive capacity of each agent is related to thecapacity to model relationships between variables and in effect generate designsolutions. The definition of relative complexity given by Bonabeau and Dessalles(1997) is translated as follows C ¼ (W/FBSObservations, FBSDescriptions).

Emergence type 1: generation of unexpected design solutions/Generativeemergence.

Given certain FBS observations and a set of ‘rules’ associating FBS variables,each agent generates certain design solutions. These solutions are completelydescribable and deducible, and can only be considered emergent from the point ofview of an external agent, who does not have knowledge of the observations anddescriptive rules that generated them. Thus emergence type 1 occurs when anexternal agent makes a new observation.

Emergence type 2: generation of descriptive rules / Descriptive emergence.

When the design activity involves creation of new associations (or ‘rules’ ofassociation) between FBS variables, then this reflects a change in the descriptivecapacity of agents, which impinges on the relative complexity of the design space.Simple generation of new descriptive rules does not guarantee emergence type 2, butin the contrary can increase complexity. Emergence type 2 occurs when a new moresuccinct or more comprehensive description is produced and therefore relativecomplexity is decreased. This can be considered from the point of view of anindividual agent, but also from the point of view of a collective of agents.

Emergence type 3: generation of observables/Observational emergence.

When the design activity involves the creation of FBS observables then thisreflects a change in the observational capacity of agents, which again impinges on therelative complexity of the design space. Simple generation of new observationalcategories does not guarantee emergence type 3, but in the contrary can increasecomplexity. Emergence type 3 occurs when the new observable substitutes a set ofexisting observables and therefore relative complexity is decreased. This can beconsidered from the point of view of an individual agent, but also from the point ofview of a collective of agents.

In the following section we revisit the conceptual model of design developed inthe previous section in order to illustrate how and whether the different types ofemergence are materialised. To do this we focus on underlying processes and unpackthe interactions between components at different levels of observation.

4. A view of coordination and emergence in design

To better understand the properties of the model elaborated in section 2, and theway in which emergence is realised, we need to consider the different components itincorporates and the way they are organised together.

In the model of coordination as distributed learning control the capacity togenerate designs is attributed to the distributed control and learning functions. Theproblem for each agent-controller is to generate appropriate configurations in order

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to steer the overall system towards individual temporal targets. The controllingability is tightly linked to the ability of each agent to learn by observing the action ofother agents, that is, to construct new knowledge about interdependencies betweendesign variables (i.e. dimensions, location, spatial relationships, functional attri-butes, etc.). In this regard, creativity can be associated with the ability of agents todiscover new values and new relationships among design variables and thereforeenhance the problem and solution spaces. Another way to express creativity, whichwould more closely reflect the notion of emergence, is to consider how agents mayextend the original definitions of the design variables (construct new variables andnew goals). Such an action requires agents to be able to modify the definition andcomplexity of the objects they manipulate. This could be achieved, for instance, byincorporating new function variables (mixed housing, recreation, etc.), or by addingnew objects or even by sub-dividing the initial objects. This would also introducenovel interdependencies and constraints for the multi-agent design process.

To better understand emergence in this context, we the concept of relativecomplexity we defined above C ¼ (W/FBSObservations, FBSDescriptions). Herethe notion of relative complexity can be specified by considering that each agent hasan observational and descriptive capacity which is tied to the existence of thedifferent components or functions of the distributed learning control model. Morespecifically, observational and descriptive mechanisms are guaranteed in thecoordination model through the incorporation of learning and control operations.Let us examine the model in more detail, by considering different parts or operationsin isolation.

Let us first take the component that was labelled World in Figure 4, whichrepresents the space of design changes produced by agents. Given a set ofobservations (structures and functions) about the current state of the design objects,and a set of ‘rules’ (associating FBS variables), the overall network of agents deducesstructural and functional behaviours. In logic terms, this constitutes a form ofdeductive reasoning and can be represented as follows:

Generation of behaviours:

In deduction, the FBS variables are given, the causes or initial conditions (F andS) are given, and the rules associating FBS are given. This process isindispensable as it is responsible for exploring and developing the design space.However, as such it does not contribute to any increase or decrease of complexity(it does not have an effect on the descriptive capacity of the system) and hence itcannot lead (on its own) to emergence. In a sense, the complexity of the space isalready entailed in the rules that guide the process and the initial conditions.Emergence type 1 is possible to be associated with this process, but only relativeto an observer that is outside the network of agents that take part in thedistributed design process.

The second functional part of this system covers the learning components, whichin general take the resulting configurations (observations of the actual world) and

S FS ! Bs F ! Bf

Bs Bf

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produce a generalised rule, which associates initial conditions with expected results.This process of law extraction from examples can be represented as follows:

Creation of world and reference models:

By generating new laws, this process affects the descriptive capacity of the system. Ifthe gained knowledge enlarges the amount of information available to the system,then it effectively leads to an increase of the relative complexity; if it helps producemore succinct descriptions of the world then it leads to a decrease of relativecomplexity (and hence emergence type 2). In computational design studies, neuralnetworks have been extensively used to realise such an inductive process: neuralnetworks are able to form hypotheses about the laws governing the world/designspace. These laws can then be utilised for the generation of design alternatives(Coyne and Newton 1989, Coyne 1990, Newton and Coyne 1991).

The third part of the system concerns the controller functions. The controllingactions, which are derived through learning, are launched as possible causes of thedesired consequence (a goal, an intended change in the configuration). This processof acquiring the control action affects the descriptive capacity of the system as itdefines the number of causes or premises that lead to the desired descriptions. Notethat the control action itself aims to reduce complexity by restricting or conditioningthe space of possible configurations.

Inference of the control action:

In reality, the picture becomes a bit more complicated if we look at the synergybetween the two learning and control components. The combination of knowledgeabout structural and functional change and about the relation between structuresand functions leads to the creation of new hypotheses. This function can berepresented as follows:

Production of new hypotheses:

In other words, each agent generates new observables, which are used to guide thedevelopment of new knowledge about FBS interdependencies and new descriptions.This process of production of new hypotheses therefore may increase or decrease therelative complexity of the system by adding or reducing existing FBS variables.

S FBs Bf

S ! Bs F ! Bf

S ! Bs F ! BfBs Bf

S F

Bs Bf

S, S ! Bs F, F ! Bf

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When the process leads to the generation of observables that substitute a set oflower-level observables then emergence type 3 occurs.

Looking at the overall picture, the model implies that coordination in designis not a process which can be easily realised by simple bottom-up modelling ofagents with local rules of interaction. It is a process that tries to achieve abalance between increase and decrease of complexity and crucially involveslearning both as construction of new categories and as re-construction, re-organisation of knowledge.

It is important to note that emergence types 2 and 3 not only depend on eachagent’s individual capacity to learn and to create new descriptive and observationalcategories, but also depend on the interaction of agents over a common designspace.

In the proposed model, the distribution of the design process in different agents,that hold individual knowledge and targets, implies the possibility for globalphenomena to emerge through the interaction and self-adaptation of agents at thelocal scale.

The above treatment also has interesting implications for our understanding ofdesign creativity. Following Fischer’s (1999) argument, the fact that knowledge isdistributed among agents offers opportunities for creativity. The notion ofcoordination here additionally implies that emergence and creativity are expressedat two different levels: at the level of the individual agents, who are able to generateand explore new problem and solution spaces, and at the level of the distributedsystem as a whole, where new collective problem and solution spaces are created.There is an increasing number of studies focused on social or collaborative creativityin design (e.g. Edmonds et al. 1999, Liu 2000, Gero and Sosa 2002, Mamykina et al.2002, Fischer et al. 2005, Warr and O’Neill 2005, Johnson and Carruthers 2006,Brickwood et al. 2007). The present treatment offers some insights to helpunderstand creative multi-agent (or collaborative) design in relation to the conceptsof co-evolution and emergence.

5. Summary and conclusions

The paper started from the observation that although the issue of emergence ispertinent to understanding design, the current literature has paid little attention tothe development of a general theory of design as an emergent phenomenon thatintegrates issues of individual perception which issues of distributed (social)interaction.

To respond to this challenge, the paper examined how the concept ofcoordination can enrich our understanding of design both as an emergentphenomenon and as a process that leads to emergent design entities. The paperspecifically proposed a model of design able to explain emergent phenomena byassociation to the mechanisms of control, learning, and co-evolution of problemsolution that occur at the individual and social level of a design team.

To further explore the meaning and nature of emergence, the paper drawstogether studies from different fields. The review showed that emergence is not alabel that we assign to anything that may come about through a generative process.In effect, emergent design solutions are not those solutions which we casually noticeand feel surprised about. To characterise a phenomenon as emergent we need to beable to explain the causal relationships and interactions that lead to it by

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appreciating the context within which this phenomenon is developed. Emergencemarks a transition from one level of description and observation to another, moreconcise one: a transition associated with a decrease in relative complexity.

Detailed examination of the proposed conceptual model using this notion ofrelative complexity showed that design becomes a process that tries to achieve abalance between increase and decrease of complexity and crucially involves learningas construction of new (observational and descriptive) categories and as re-construction, re-organisation of knowledge.

This view can have interesting interpretations about the way we understand andsupport creative design, but also the way we model design within computationalsystems. The analysis offered here suggests that in order to model design as a multi-agent coordination process we need to incorporate mechanisms for observation andlearning. Such mechanisms are needed so as to equip the multi-agent design systemwith the ability to re-organise itself and its goals.

Acknowledgements

The model presented in section 2 was originally developed in collaboration with TheodoreZamenopoulos. I also owe him thanks for some very constructive feedback that helped meimprove the paper.

Note

1. Although the example considers a kind of urban configuration problem, the setting can begeneralised to other domains. For example, we can think of the cuboids as rooms in abuilding, as components in an engine, as sound bits, as programs, etc.

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