[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

RESILIENCE

Chapter 6

Lecture #9 IID_Information Architecture 2

Looking for that special wine


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


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)


• 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




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.


Table 6.1 Fundamental Strategies of Information Seeking (Bates 2002)

Active Passive

Directed Searching Monitoring

Undirected Browsing Being aware


• 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.




FIGURE 6.2 Percentage relevance of the different information seeking strategies in everyday life.



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.


Resilience in Pervasive Information Architecture


FIGURE 6.5 Tracking and reusing patterns of use and communication in a dance project, part of Synchronous Objects. Source: Synchronous.osu.edu.


• 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



• 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.




FIGURE 6.6 Desire paths are often opened even to avoid a simple deviation from the least effort. Source: Flickr.


• 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”


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



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.



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



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.


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.


Case Studies : The Resilient Museum


FIGURE 6.9 How museums used to be.



FIGURE 6.10 How museums are. The NEMO Science Center in Amsterdam, The Netherlands. Photo: J. Nieuwland.



FIGURE 6.11 The Apartheid Museum in Johannesburg Web site.



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).



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


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


The Pervasive Influence of Code


FIGURE IV.1 Digital information

Digital Learning and Agency


FIGURE 12.2 Humans and digital agents learn in different directions

Everyday Digital Agents


FIGURE 12.5 Recipes from IFTTT.com

Ontologies


FIGURE 12.7 The “programme” ontology from the BBC*



FIGURE 13.4 The Shazam mobile app’s primary control is a big simulated button that, when touched, scans the environment for musical patterns



FIGURE 13.6 Animoog simulates the controls of a small Moog synthesizer

Digital Environment


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


FIGURE 14.2 Bates’ “berrypicking” model

Inhabiting Two Worlds at Once


FIGURE 14.3 Refresh podcasts by geolocation in the Downcast podcast app

Inhabiting Two Worlds at Once


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


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


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


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


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 9.1


FIGURE 14.2 Bates’ “berrypicking” model

Exercise 9.2

• Find ambient agents for your system

– Think about possible ambient agents.

– Think about the functions/data


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

https://developers.google.com/cardboard/unity/download

http://unity3d.com/get-unity/download

http://developer.android.com/sdk/index.html

Homework


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

[ia] week 09. resilience

Education