Course Syllabus

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CogSci 190, 3 units, Spring 2019
Tuesday & Thursday 12:30-2pm, Wheeler 104

TEACHING TEAM

Professor Robert J. Glushko
glushko@berkeley.edu, OFFICE: Evans 557

Lauren Stoops
lstoops@berkeley.edu

When something "makes sense” or "is organized” we are imposing or discovering order in the arrangement of concepts, events, or resources of some kind.    Sensemaking and organizing are fundamental human activities that raise many multi- or trans-disciplinary questions about perception, knowledge, decision making, and interaction with things and with other people, values and value creation.

We can analyze sensemaking and organizing from four interrelated perspectives. The most fundamental one is provided by language and culture, which shapes the perspectives one takes as an individual, in institutional contexts governed by business or legal processes, or in data-intensive or scientific contexts.

COURSE FORMAT and GRADING

The course is primarily lecture-based, with as much classroom discussion and peer learning as possible during the three hours of lecture each week.  During the first thirty or last thirty minutes of many of the class meetings, students will engage in small group discussions.

Expected workload should average 10 hours a week; 3 for the course meetings and 7 for preparation and deliverables.   The load will be lighter at the beginning of the semester and heavier toward the end when students are researching and writing their case study, the most important work product for the course.

  • 10 weekly assignments (50%)
  • Take home timed final exam, administered by bCourses (20%)
  • Case study (20%)
    • Purpose: To reinforce the generality of the organizing system framework.  After reading ten or more short case studies in the first few weeks of the course, students write one of their own. The best student cases end up in future syllabi
  • Class preparation and participation in lectures and sections (10%)

SCHEDULE AND READINGS

The primary text is The Discipline of Organizing, Core Concepts (4th edition, 2016). This text has been extensively revised since it was first published and it can be found in multiple versions, and in both print and digital formats. Make sure you have the recommended one!

Readings from this text are indicated with “TDO” and a chapter or section number, a * after a number (like 4.1*) means the identified section and all of its subsections

Many readings are short case studies, some from the textbook and some written by students taking this or similar courses. Their diversity demonstrates the breadth of the organizing system concept.

Introduction to Sensemaking and Organizing

[1 - Jan 22] Course introduction; the concept of the "Organizing System”

An Organizing System is an intentionally arranged collection of resources and the interactions they support.

The Organizing System concept transcends the traditional view that information organization is a human activity and information retrieval is a machine activity, or that information organization is a topic for library and information science and information retrieval is one for computer science. Instead, we readily see that computers now assist people in organizing and that people contribute much of the information used when computers analyze and organize resources

Because the Organizing System perspective can be applied to any kind of resource, it enables more nuanced discussion of how the economic, social, and cognitive costs and benefits of organizing are allocated among different stakeholders and over time.

  • Foundations for Organizing Systems, TDO Chapter 1

Case Studies:

  • TDO 12.5 Organizing a kitchen (Hardman 2013)
  • Time Zones (Volkov 2015)

[2 - Jan 24] Design Decisions for Organizing Systems

This lecture introduces six broad design questions or dimensions whose intertwined answers define an Organizing System: What, why, how much, when, how, and where. This framework for describing and comparing Organizing Systems overcomes the biases and conservatism built into familiar categories like libraries and museums while enabling us to describe them as design patterns. We can then use these patterns to support inter-disciplinary work that cuts across categories and applies knowledge about familiar domains to unfamiliar ones.

  • Design Decisions for Organizing Systems, TDO Chapter 2

Case Studies:

  • TDO 12.1 Multi-generational photo collection
  • TDO 12.3 Smarter farming in Japan
  • Medical Emergencies (Delzompo 2018)

[3 - Jan 29] More Organizing System Case Studies

  • TDO 12.4 Single-source textbook publishing
  • TDO 12.6 Earth orbiting satellites (Brenners 2014)
  • TDO 12.18 Neuroscience lab (Gerber 2013)
  • Cognitive Neuroscience (Griffith 2018)
  • Organized Crime (Sondhi 2015)

[4 - Jan 31]The activities of sensemaking and organizing

A perspective that brings together how we organize as individuals or as members of groups with how libraries, museums, governments, research institutions, and businesses create Organizing Systems requires that we generalize the organizing concepts and methods from these different domains. This lecture surveys a wide variety of Organizing Systems and describes four activities or functions shared by all of them: selecting resources, organizing resources, designing resource-based interactions, and maintaining the interactions and resources over time.

 [5 – Feb 5] Resources

The design of an Organizing System is strongly shaped by what is being organized, the first of the six design decisions we introduced in the second lecture. To enable a broad perspective on this fundamental issue we use resource to refer to anything being organized, an abstraction that we can apply to physical things, people, digital things, information about either of them, or web-based services or objects.

Two separate aspects cut across the "thingness" distinctions:
 - Granularity – do we think of the resource as unitary, or as consisting of parts?
 - Abstraction – “thing” or “type of thing” – are we thinking of the resource as a single instance or as a member of a bigger category?

  • Resources in Organizing Systems,  TDO 4.1*, 4.2*

[ 6 – Feb 7] Names and Identifiers

A NAME is a label for some thing or some category that is used to distinguish one from another. If a name is used to refer to some specific thing and is unique in some context it is an IDENTIFIER. Choosing good names and identifiers is essential, difficult, and often contentious.

  • Resource Identity, TDO 4.3*
  • Naming Resources, TDO 4.4*
  • Resources Over Time, TDO 4.5*

 [7 – Feb 12] The lifecycle of organizing; architectural thinking

The Organizing System concept is an architectural and conceptual view that is distinct from the physical arrangement of resources that might embody it, and also distinct from the person, enterprise, or institution that implements and operates it. These distinctions are sometimes hard to maintain in ordinary language; for example, we might describe some set of resource descriptions, organizing principles, and supported interactions as a “library” Organizing System. However, we also need at times to refer to a “library” as the institution in which this Organizing System operates, and of course the idea of a “library” as a physical facility is well established in language and culture.

This lecture also analyzes the design choices and tradeoffs that must be made in different phases in an Organizing System’s life cycle.

  • The Organizing System Lifecycle, TDO 11.1, 11.2, 11.3*
  • FLASHBACK: The Concept of “Organizing Principle”, TDO 1.6
  • Case Study: Universal Vending Machine (Potluri, 2015)

Sensemaking and Organizing in Language,
Social, Cultural Groups

[8 – Feb 14] Introduction to Categorization; Cultural categories and linguistic relativity

Categories are sets or groups of resources or abstract entities that are treated the same.  Categories are involved whenever we perceive, communicate, analyze, predict, classify – or otherwise attempt to make sense of our experiences. All human languages and cultures divide the physical and experiential “worlds” into categories. Many categories have a perceptual or sensorimotor origin based on natural boundaries or discontinuities in perception and experience.  When properties or behaviors co-occur in predictable ways, it is useful to have category words that name them.

  • Categorization: Describing Resource Classes and Types, TDO 7.1, 7.2
  • Cultural Categories and Linguistic Relativity, TDO 7.2.1
  • Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive psychology, 8(3), 382-387 (Introduction), 428-431 (General Discussion)

[9 – Feb 19]Resource Description

Categories can be created in many different ways according to many different types of principles and processes.  The principles by which resources are organized and the interactions that can be supported for them largely depend on the nature and explicitness of the resource descriptions. This “how much description” design question was introduced in the second lecture.

  • Resource Description and Metadata, TDO Chapter 5 (5.1, 5.2 through 5.2.2.3, 5.3 through 5.3.2.4, 5.3.4*, 5.3.7*)

[10 – Feb 21]Describing Non-text Resources

  • Describing Non-Text Resources TDO 5.4*
  • Case Study: What3Words (Donofrio, 2016)
  • Case study: TDO 12.16 Dabbawalas of Mumbai (Rathore 2014)
  • Music as an Organizing System: An Approach for Non-Musicians (Glushko and Freeman, under review)

  [11 – Feb 26]Cognitive Universals? Folk biology. Bilingualism.

Two fundamental cognitive processes are categorization (how do we decide what objects are the same kind of thing) and inductive reasoning (given that one object or class exhibits a property, how do we decide whether other related objects or classes do).

Are these and other cognitive processes universal? Do all people have the same “cognitive toolkit” to use?

“Folk biology” (studying ordinary people’s relationships with plants and animals) suggests that there are universal principles for perceiving, classifying, and naming biological kinds. Studies of bilinguals suggest that cognitive and cultural advantages emerge from knowing more than one language.

  • Medin, D. L., & Atran, S. (2004). The native mind: biological categorization and reasoning in development and across cultures. EXCERPT
  • Bailenson, J. N., Shum, M. S., Atran, S., Medin, D. L., & Coley, J. D. (2002). A bird's eye view: Biological categorization and reasoning within and across cultures. Cognition, 84(1), 1-53. EXCERPT
  • Ramírez-Esparza, N., & García-Sierra, A. (2014). The bilingual brain: language, culture and identity. Handbook of Multicultural Identity: Basic and Applied Perspectives,  (pages 35-37 and all the "Conclusion" sections)
  • Cooperrider, K., and Gentner, D. "Where do measurement units come from?" Proceedings of the 2018 Conference of the Cognitive Science Society.

  [ 12 – Feb 28] Describing Relationships and Structures, Part 1

An important aspect of organizing a collection of resources is describing the relationships between them. This lecture introduces the specialized vocabulary used to describe semantic relationships between resources and between the concepts and words used in resource descriptions.

  [ 13 – March 5] Describing Relationships and Structures, Part 2; Social network analysis

The structural perspective analyzes the patterns of association, arrangement, proximity, or connection between resources (and often ignores the reasons for them).

We can apply graph theory to understanding relationships from a structural perspective

  • Describing Relationships and Structures, TDO Chapter 6 - Sections 6.5-6.7
  • Hansen, D. L., & Smith, M. A. (2014). Social network analysis in HCI. In Ways of Knowing in HCI (read pages 432-440 carefully; skim the rest)

  [ 14 – March 7] Social thinking: wisdom or delusion of crowds, filter bubbles, misinformation, fake news

How do the groups we explicitly or implicitly belong to influence how and what we think? When does social thinking create better decisions and outcomes? When does social thinking create worse decisions and outcomes?

  • O'Reilly T. (2017). WTF - What's the Future and Why It's Up to Us, Chapter 10, "Media in the Age of Algorithms."
  • Sunstein, C. R. (2004). Mobbed Up. Review of “The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations” by James Surowiecki. NEW REPUBLIC, June 2004.
  • Manjoo, F. (2017). How Twitter is being gamed to feed misinformation, The New York Times, 31 May 2017.
  • Susskind, J. (2018). Chatbots are a danger to democracy. The New York Times, 4 December 2018.
  • Lapowsky, I. (2018). NewsGuard wants to fight fake news with humans, not algorithms. Wired, 23 August 2018.
  • Schwartz, O. (2018). You thought fake news was bad? Deep fakes are where truth goes to die. The Guardian, 12 November 2018.

Sensemaking and Organizing as Individuals

  [ 15 – March 12] Causality and Explanation

People have a cognitive bias to see order and purpose in the world. The vast majority of our decisions are influenced—at least in part—by our beliefs about the causal structure of the world.  It is essential for survival to understand and predict the properties and behaviors of physical objects and substances (Physics), plants and animals (Biology), and other people (Psychology).

But what makes some sequences of events causal, so we make inferences about similar events, and other sequences non-causal? Causes are not deducible, and are not always explicitly visible in sensory input

  • Kleinberg, S. (2015). WHY: A Guide to Finding and Using Causes. Chapters 1, 2, 4
  • Banerjee, K., & Bloom, P. (2014). Why did this happen to me? Religious believers’ and non-believers’ teleological reasoning about life events. Cognition, 277-280 (section 1), 299-301 (General discussion)
  • Thagard, P. (2012). The cognitive science of science: Explanation, discovery, and conceptual change.  Chapter 13 Conceptual Change in the History of Science: Life, Mind, and Disease

  [ 16 – March 14] Categories made by individuals; cognitive overload and offloading; distributed cognition and taskonomy

Individual categories are created to satisfy ad hoc requirements that emerge from an individual’s unique experiences, preferences, and resource collections.  They are created intentionally in response to specific organizing requirements, often short-term ones

  • Individual Categories, TDO 7.2.2
  • Bush, V. (1945). As We May Think.
  • Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction (TOCHI) [Read pages 174-178 and Section 3.6 Intelligent Use of Space]
  • Classification by Activity Structure. TDO Section 8.5

  [ 17 – March 19] "Automatic organizing" using perceptual processes, Gestalt principles, affordances

Physical resources are often organized according to intrinsic physical properties like size, color, or shape, because the human visual system quickly and automatically pays a lot of attention to them. Likewise, because people have limited attentional capacity, we ignore a lot of the ongoing complexity of visual (and auditory) stimulation, making us perceive our sensory world as simpler than it really is

  • Organizing with Properties of Physical Resources, TDO 3.3.1.1
  • Affordance and Capability, TDO 3.4.1
  • Chater, N., & Loewenstein, G. (2016). The under-appreciated drive for sense-making. Journal of Economic Behavior & Organization, 137-141 (sections 1 and 2)

  [ 18 – March 21] Applied Behavioral Economics; the Narrative Fallacy

Organizing systems control the behavior of their users. The emerging field of applied behavioral economics explains how subtle differences in resource arrangement, the number and framing of choices, and default values can have substantial effects on the decisions people make.

  • The Narrative Fallacy and What You Can Do About It
  • Thaler, R. (2015) “Unless You Are Spock, Irrelevant Things Matter in Economic Behavior” New York Times
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.

Sensemaking and Organizing in Defined/Institutional Contexts

  [ 19 – April 2] Category Structure; Institutional Categories

Institutional categories are explicitly constructed semantic models of a domain to enable more control, robustness, and interoperability than is possible with just the cultural category system.  They are essential in abstract, information-intensive domains where semantic precision is essential for processes and transactions (especially automated ones). They are usually developed via rigorous and formal processes (e.g., in standards organizations or legislative bodies) and require ongoing governance and maintenance

  • Institutional Categories, TDO 7.2.3
  • Principles for Creating Categories, TDO 7.3* (skip 7.3.6 "Similarity" for now)

  [ 20 – April 4] Classification; Standardization

The terms categorization and classification are often used interchangeably but they are not the same. Classification is applied categorization—the assignment of resources to a system of categories, called classes, using a pre­determined set of principles. These include enumerative classification, faceted classification, activity-based classification, and computational classification. Because classification and standardization are closely related, the lecture also analyzes standards and standards-making as they apply to Organizing Systems.

  • Classification and Standardization, TDO 8.1.5
  • Understanding Classification, TDO 8.2*
  • Case Study: Occupational Classification (Suzuki, 2015)

  [ 21 – April 9] Vocabulary control, interoperability

The words people use to describe things or concepts are "embodied" in their context and experiences... so they are often different or even "bad" with respect to the words used by others.  These naturally-occurring words are an "uncontrolled vocabulary” that people use when they describe and search for resources.  A controlled vocabulary is an artificial language to be used in place of the "natural" ones that people would otherwise use

  • Controlled Vocabularies, TDO 4.4.3.2
  • Wheatley, M. (2004). Operation Clean Data.

  [ 22 – April 11] Organizing and Interaction Design

Classifications arrange resources into categories. So every classification increases the capability and efficiency of interactions with resources that are “co-located” or “within-category” and makes “between-category” interactions less efficient. Choices about descriptions and organizing principles involve tradeoffs that determine which interactions are efficient, possible, difficult, or impossible

  • Faceted Classification, TDO 8.4
  • Interactions with Resources, TDO 10.1, 10.2
  • Johnson, Eric J., et al. "Beyond nudges: Tools of a choice architecture." Marketing Letters 23.2 (2012): 487-504.

Sensemaking and Organizing with Data

  [ 23 – April 16] Scientific Methods and Systems Thinking

TOPIC CANCELLED. GROUP WORK AND COURSE REVIEW INSTEAD

  [ 24 – April 18] Exploratory Data Analysis and Information Visualization

TOPIC CANCELLED. GROUP WORK AND COURSE REVIEW INSTEAD

  [ 25 – April 23] Organizing with “Interaction Traces” or “Usage Metadata”

Interactions with physical resources sometimes leave traces or other evidence. Many of these traces are unintentional, like fingerprints, a coffee cup stain on a newspaper, or the erosion on a shortcut path across a lawn. However, when Organizing Systems contain digital resources, or physical resources that have sensing, recording, or communication capabilities, interaction traces can be made predictable, persistent, and consistent. Each record of a user choice in accessing, browsing, buying, highlighting, linking, and other interactions then becomes an “interaction resource” that can be analyzed to reorganize the resource collection or otherwise influence subsequent interactions with the primary resources

  • The Concept of “Interaction Resource,” TDO 1.9
  • Active or Operant Resources, TDO 4.2.3.2
  • Voida, S., Patterson, D. J., & Patel, S. N. (2014). Sensor data streams. In Ways of Knowing in HCI (pp. 291-321). Springer, New York, NY.
  • Carr, Nicholas.  (2018). I am a data factory (and so are you).  http://www.roughtype.com/?p=8394

  [ 26 – April 25]Computational description and categorization

Categories are typically created computationally when a collection of resources or resource descriptions is too large for people to think about effectively.  The simplest computational categories are the implicit ones created by descriptive statistics that identify typical or frequent and atypical or infrequent items

  • Automated and Computational Resource Description, TDO 5.3.6.4
  • Computational categories, TDO 7.2.5
  • Similarity TDO 7.3.6*

[27 – April 30] Computational classification and its limitations

Some classification tasks are hard because the categories are “close together” and the resource properties or features involved are not easy to understand and use.  Other classification tasks are conceptually simpler, but can't be done by people at the needed scale or speed

In “data science,” a classifier is a system whose input is a vector of discrete or continuous feature (or attribute) values and whose output is the name of the class. This vector might be the description of a document, an image, a person, a molecule, just about anything

  • Implementing Categories Defined by Probability and Similarity, TDO 7.5.3*
  • Computational classification, TDO 8.6

[28 – May 2] Course Review

Take Home Exam available now

[29 – May 7] Case Study Presentations

[30 – May 9] Case Study Presentations

Course Summary:

Date Details Due