[IA] Week 09. Resilience
Transcript of [IA] Week 09. Resilience
Lecture 9
Resilience
Information Architecture / IID 2016 Fall Class hours : Tuesday 3pm – 7pm Lecture room : International Campus Veritas Hall B306 1st November
Looking for that special wine
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FIGURE 6.1 Photo: Umbria Lovers. Source: Flickr.
Known-item seeking strategy
Seeking strategies are basic behavioral patterns we use when looking for information. Known-item is one of them and implies that the user knows what she is searching for and how to describe it. For an in-depth discussion in connection to information architecture, see Rosenfeld and Morville (2006) or Spencer (2006a).
Human–Information Interaction
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A synchronic society generates trillions of catalogable,
searchable, trackable trajectories: patterns of design,
manufacturing, distribution and recycling that are
maintained in fine-grained detail. These are the
microhistories of people with objects.
(Sterling 2005, p. 45)
Human–Information Interaction
• Seeking strategies
– Cognitive, cultural, and social models have a strong impact on behavior; as a
result, we all are different individuals who browse and search differently
because we all have different goals and possess different reference models.
• Two characteristics actively shape this process of human– information
interaction and impact either positively or negatively:
– the capability (or incapability) of an information space to adapt itself to the
needs of its users
– the capability (or incapability) of an information space to support multiple
information seeking strategies
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Human–Information Interaction
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Resilience - The capability of a pervasive information architecture model to shape and adapt itself to specific users, needs, and seeking strategies.
An Integrated Model of Information Seeking
• Toward an Integrated Model of Information Seeking and Searching (Bates,
2002)
– vertical axis (consciousness)
• directed: individuals can specify to some degree what they are looking for undirected:
individuals cannot articulate their need; they expose themselves to information randomly
– horizontal axis (voluntariness)
• active: individuals acquire information actively passive: individuals absorb information
passively (from family, friends, colleagues) and do not enact any active seeking behavior.
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Table 6.1 Fundamental Strategies of Information Seeking (Bates 2002)
Active Passive
Directed Searching Monitoring
Undirected Browsing Being aware
An Integrated Model of Information Seeking
• The resulting matrix illustrates the four fundamental strategies we can adopt while
engaged in information seeking behaviors.
1. Searching: directed and active information seeking. We are conscious we need a
certain piece of information that we are able to articulate and we work actively to find it.
2. Monitoring: directed and passive information seeking. We are conscious we need a
certain piece of information that we are able to articulate, but we do not search actively.
This modality identifies a propensity to absorb pieces of information from the context
without engaging in a direct search, which relates to serendipity.
3. Browsing: undirected and active information seeking. We have no specific interest or
need or we cannot articulate it, but we acquire new information actively.
4. Being aware (or awareness): undirected and passive information seeking. We have no
specific interest or need or we cannot articulate it, and we do not acquire information
actively. We rather absorb it from the context.
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An Integrated Model of Information Seeking
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FIGURE 6.2 Percentage relevance of the different information seeking strategies in everyday life.
An Integrated Model of Information Seeking
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FIGURE 6.3 Kirsty Williamson’s integrated approach to information seeking behavior.
The Principle of Least Effort
• The principle of least effort
– As humans, and throughout our evolution from cave dwellers to video
game players, we have always been keen in gathering information
passively from the context, be it the clan, tribe, family, or environment, as
that provides the best almost-free meal we can get, so to speak. We have
simply maintained such a propensity and passed it on to our children,
resorting to active seekingonly when passive seeking fails.
• Satisficing
– meaning some degree of satisfaction obtained with minimal effort.
Simon’s point is that often we don’t make optimal choices, we satisfice.
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Resilience in Pervasive Information Architecture
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FIGURE 6.5 Tracking and reusing patterns of use and communication in a dance project, part of Synchronous Objects. Source: Synchronous.osu.edu.
Resilience in Pervasive Information Architecture
• You can imagine a down-to-earth, real-life situation if you think of your
visits to a convenience store or a supermarket.
– refind paths
– customize your shopping and save time, money, or mileage
– receive meaningful, personalized push suggestions and correlations
– share your histories and profiles with family or friends
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Resilience in Pervasive Information Architecture
• places as mnemonic palimpsests (Bruno, 2007, “Public Intimacy”)
– Preserving a history of the flow and actions of people inside places transforms
them effectively into a text, into architectures, emotional landscapes, and
mobile maps where the environment is complemented by the interactions of
people with them.
– In a way, in Bruno’s vision people are like pens: they write the stories of their
interactions with places inside those very places, and hence inevitably change
them. As we said when introducing place-making, places have a spatial
component and an existential, emotional, personal, and social part that
stretches back and forth into the past and into the future. Exploiting these
sediments, these narratives, help make them resilient.
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Resilience in Pervasive Information Architecture
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FIGURE 6.6 Desire paths are often opened even to avoid a simple deviation from the least effort. Source: Flickr.
Resilience in Pervasive Information Architecture
• desire lines or desire paths,
– a concept borrowed from urban design: “trails worn into a landscape that
demonstrate the paths people want to take, not those that were laid down
by the designer”
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Table 6.2 Correspondences between Information Seeking Strategies in Digital and Physical Contexts
Digital Physical Information Seeking Strategies (Bates)
Search Specific places, objects, people having unique IDs or coordinates
Monitoring
A–Z index Alphabetical list of items and related coordinates Searching
Main and local navigation
Departments, aisles, shelves, and similar Browsing
What’s new New items, hot topics, promotions, or highlights Browsing
RSS, newsletters Push alerts Monitoring
Shortcuts Custom paths for returning users or specific targets/needs Monitoring/being aware
Social navigation Popular items or paths Being aware
Contextual navigation Related items Being aware
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FIGURE 6.6 Real Time Copenhagen, one of the seminal applications built by MIT (this one in collaboration with the City of Copenhagen and Aalborg University, Denmark). Source: Senseable .mit.edu.
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Table 6.3 How, What, Why: How We Save Interactions, What This Provides Us, Why It Is Important
How What Why
Geotagging, unique IDs via RFId or similar code systems
Every item may be localized and is directly findable
Enables direct search for objects in the physical world Allows customized paths
Recording paths via smart cards, mobile devices, and similar Cross-referencing recorded paths
Usual or custom paths may be refound for personal or social use Amazon-like correlation strategies such as “if you like x maybe you also like y” or “people who saw this also saw”
Refinding frequently used paths allows optimization Custom paths can be shared Receiving suggestions in push mode
Tagging enabled by RFId or similar technologies and by mobile devices
Tagging and collaborative tagging for improved metadata on items
Used for both personal purposes (refinding items; creating wish lists . . .) and social purposes (receiving suggestions or discovering related items by people with similar profiles; sharing paths or lists . . .)
All of the above All of the above Monitoring interactions and flows help the corporate to improve the information architecture of the entire ecosystem
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FIGURE 6.6 The Coliseum, Rome, in a Photosynth 3D reconstruction from usergenerated photos. Source: Photosynth.net
Lessons learned
• Know
Resilience makes an information space able to adapt itself to the
changing needs of its users in different contexts of use, different places,
and different times.
Resilience makes an information space capable of supporting multiple
information seeking strategies, either active or passive, directed or
indirected, conscious or latent.
Places are texts. Places are palimpsests where people write and rewrite
their interactions with the environment, with other people, or with objects.
Most objects leave traces and project shadows in information space.
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Lessons learned
• Do
Integrate bottom-up, user-created patterns with top-down, built-in
structures to improve the resilience of an information space.
Make these two levels communicate: allow fast but consolidated
usercreated patterns to seep down to the foundations and allow slow
structures to be moved, changed, and flexed when needed.
Collect, filter, and reuse the traces and shadows objects and people leave
in information space to allow users to satisfy their natural propensity for
harvesting pieces of information passively and to elicit latent needs.
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Case Studies : The Resilient Museum
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FIGURE 6.9 How museums used to be.
Case Studies : The Resilient Museum
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FIGURE 6.10 How museums are. The NEMO Science Center in Amsterdam, The Netherlands. Photo: J. Nieuwland.
Case Studies : The Resilient Museum
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FIGURE 6.11 The Apartheid Museum in Johannesburg Web site.
Case Studies : The Resilient Museum
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FIGURE 6.12 Home page for the Guggenheim Museum in New York, showing a mixture of facet-like static navigation (on the left) and dynamic navigation (in the body).
Case Studies : The Resilient Museum
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Table 6.4 Simple Scheme of Integrated Classification Layers in a Museum
Layers Classification System Enabling Tools and Technologies
Top down Faceted classification system Thematic paths
Analogical and/or digital signage employing alphanumeric and chromatic codes. Searchable digital catalogs
Bottom up Social tagging and navigation: personal/social paths created by people using facets, tags, and their own competencies
PDAs, smartphones, GPS, RFIds or similar systems. Augmented reality systems
Up and down (service layer)
General monitoring and controlled absorption into the top-down layer of the paths and stories created by the visitors
Monitoring and measurement systems logging and reporting repeating paths and behaviors
Case Studies : The BBC and the Metadata Threshold
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FIGURE 6.11 The metadata threshold: Combining folksonomies and controlled vocabularies at the BBC. Poster presented at the 2nd European Information Architecture Summit (Berlin 2006). Photo: K. Loasby.
DIGITAL INFORMATION
UNDERSTANDING CONTEXT: ENVIRONMENT, LANGUAGE, AND
INFORMATION ARCHITECTURE, Part IV
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The Pervasive Influence of Code
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FIGURE IV.1 Digital information
Digital Learning and Agency
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FIGURE 12.2 Humans and digital agents learn in different directions
Everyday Digital Agents
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FIGURE 12.5 Recipes from IFTTT.com
Ontologies
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FIGURE 12.7 The “programme” ontology from the BBC*
Semantic Function of Simulated Objects
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FIGURE 13.3 Magritte’s The Treachery of Images (© Herscovici, Brussels/Artists Rights Society [ARS], New York)
Semantic Function of Simulated Objects
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FIGURE 13.4 The Shazam mobile app’s primary control is a big simulated button that, when touched, scans the environment for musical patterns
Semantic Function of Simulated Objects
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FIGURE 13.6 Animoog simulates the controls of a small Moog synthesizer
Digital Environment
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FIGURE 14.1 When in the Shopping tab, search results are driven by different rules, which you can see by clicking the “Why these products?” link
Foraging for Information
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FIGURE 14.2 Bates’ “berrypicking” model
Inhabiting Two Worlds at Once
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FIGURE 14.3 Refresh podcasts by geolocation in the Downcast podcast app
Inhabiting Two Worlds at Once
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FIGURE 14.4 The user begins shopping on the Web—a place without location—but must be placed in a “store” to see necessary information about products
Ambient Agents
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FIGURE 14.8 Detail portion of an infographic about smart cities, from Internet-of-Things platform provider, Libelium‡
Our cities and towns are becoming sensor-studded, agent-suffused environments that can improve our lives immensely, such as the cityscape of systems depicted in Figure 14-8. But they also require careful design and translation into people’s everyday, tacitly aware understanding. As inhabitants of these places satisfice their way through each day, they need the environment to let them know when objects aren’t going to behave the way natural objects do. Retail store shelves can track body movements, age, and gender.* Safety-aware, cooperatively smart chemical drums can track how well their handlers are following safe-handling policies.† These are creatures of a sort that are not especially legible to us.
Ambient Agents
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The vision of an Internet of Things built from smart objects raises several important research questions in terms of system architecture, design and development, and human involvement. For example, what is the right balance for the distribution of functionality between smart objects and the supporting infrastructure? How do we model and represent smart objects’ intelligence? What are appropriate programming models? And how can people make sense of and interact with smart physical objects?
Kortuem, Gerd et al.“Smart objects as building blocks for the Internet of things.” Internet Computing, IEEE.2010;14(1):44–51.
Ambient Agents
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FIGURE 14.9 A model describing smart objects across several dimensions
The model, presented in Figure 14-9, describes smart objects across several dimensions, one of which the authors describe as “fundamental design and architectural principles: activity-aware objects, policy-aware objects, and process-aware objects.” In their book Code/Space: Software and Everyday Life, a particularly important idea explored by Kitchin and Dodge is that, not only do hardware and software objects have this kind of agency, but the “space” (in our terms, the places) we inhabit can have agency, as well—a quasi-sentience that’s woven into the surfaces and layouts around us. Robots aren’t only in the form of objects that behave like people or animals; entire buildings and cities can be “robots” of a sort.
Exercise 9.1
• Think about what information seeking strategy will be used in your
products/services
– Draw out 5 critical navigation scenarios of your system.
– Think about the matrix of the navigations in physical, and digital contexts
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Table 6.2 Correspondences between Information Seeking Strategies in Digital and Physical Contexts
Digital Physical Information Seeking Strategies (Bates)
Search Specific places, objects, people having unique IDs or coordinates
Monitoring
A–Z index Alphabetical list of items and related coordinates Searching
Main and local navigation
Departments, aisles, shelves, and similar Browsing
What’s new New items, hot topics, promotions, or highlights Browsing
RSS, newsletters Push alerts Monitoring
Shortcuts Custom paths for returning users or specific targets/needs Monitoring/being aware
Social navigation Popular items or paths Being aware
Contextual navigation Related items Being aware
Exercise 9.2
• Find ambient agents for your system
– Think about possible ambient agents.
– Think about the functions/data
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Ready for Unity Workshops
• Download and Samples
– https://developers.google.com/cardboard/unity/download
• Download Unity
– http://unity3d.com/get-unity/download
• Download Android Studio
– http://developer.android.com/sdk/index.html
IxD Studio IID, 2016 Spring 48
Homework
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Technology Case Studies
(Individual Assignment)
Make a personal pinterest board,
“Resilience”
Ready for the team
presentation
1 2 3
Find technology set relating your team project - IoT/Sensor/GPS/LBS - VR/AR/MR - NUI(Gestures/Voice) - Research on
example projects - Post the report on
your personal blog.
Personal Homework - Just upload until the due
Group Homework - Team leaders should send me an email after they post the presentation on team blog.
Submission Due : 11: 59 pm Sun. 6th November