Course Syllabus
Sensemaking & Organizing
CogSci 150, 3 units, Spring 2024
Wheeler Room 30, Tu-Th 2:00-3:30
TEACHING TEAM
Professor Robert J. Glushko
https://linguistics.berkeley.edu/~glushko/
glushko@berkeley.edu
Cynthia Chen
https://www.cynchen.me/
cynthia_chen@berkeley.edu
When something "makes sense” or "is organized” we are discovering or imposing 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, language, knowledge, decision making, and interaction with things and with other people.
Organizing is an intentional activity, while sensemaking is often automatic or pre-attentive, especially in our perceptual systems. But this contrast isn't sharp; we often organize things using pre-attentive properties like color and shape to facilitate our interactions with them. "Scientific sensemaking" requires iterative observation, analysis, and introspection that can take years or centuries.
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, but to enable more discussion, breakout sessions, and peer learning we will try to schedule occasional discussion sections on Friday (time and place TBD, most likely online)
The expected workload should average 10 hours a week; 4 for the course meetings and 6 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.
- 12 assignments, most of which can be completed in an hour (25%)
- 2 timed exams, administered by bCourses (35%; 15% for #1, 20% for #2)
- Case study (25%)
- 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 as their course capstone. The best student cases end up in future syllabi
- Class preparation and participation (15%)
SCHEDULE AND READINGS
The primary text is The Discipline of Organizing, currently being revised to create a 5th edition. This text has been extensively revised since it was first published in 2013.
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.
Part 1: Introduction to Sensemaking and Organizing
[1 - Jan 16] Course introduction; the concept of the "Organizing System”
An Organizing System is an intentionally arranged collection of resources and the interactions they support. This definition doesn't specifically mention books, people, datasets, shoes, animals, or any other type of resource. This definition also doesn't mention any specific ways in which resources are used. Finally, this definition allows for any arrangement or structure that is needed to enable the interaction.
Because the Organizing System perspective can be applied to any kind of resource, it enables a more nuanced discussion of how the economic, social, and cognitive costs and benefits of organizing are allocated among different stakeholders and over time.
Case Studies:
- TDO 12.5 Organizing a kitchen (Hardman 2013)
- Time Zones (Volkov 2015)
- Orchestra Seating Arrangements (Hsu 2018)
[2 - Jan 18] Design Dimensions for Organizing Systems
This lecture introduces several broad design questions or dimensions whose intertwined answers define an Organizing System, including: What, why, how, for whom, when, by whom or what, 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 Dimensions for Organizing Systems, TDO v5 Chapter 2
- Grossman, M. (2022). When is a bumblebee a fish? When a California court says so. Wall Street Journal, 7 June 2022.
Case Studies:
- Medical Emergencies (Delzompo 2018)
- From the fields to your plate: The Organizing System of lettuce production (Munoz 2023)
[3 - Jan 23] Structure and Representations of It; More Case Studies
Sensemaking and Organizing are about discovering or imposing structure. This lecture starts by contrasting six categories of structure. The most important contrast is between structure that is created and structure that is discovered, and a second contrast is between structure that is created by intentional action and that which is self-organized by collective action or physical forces.
We'll then see how different academic fields – management, operations research, informatics, etc. – provide models or representations for describing structures and organizing systems. These different depictions, many from case studies you've read, distinguish and highlight different aspects of structure and organization.
Case Studies:
- Guide Dogs for the Blind (Cho 2016)
- Organized Crime (Sondhi 2015)
- Intentional Communities (Sauter 2016)
- The Spectrum of Pottery Production (Bar-Or 2023)
[4 - Jan 25] The Life Cycle of Organizing Activities
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 five activities or functions shared by all of them: domain scoping and resource selection, identifying requirements for interaction and organizing, organizing resources, designing resource-based interactions, and maintaining the interactions and resources over time.
[5 - Jan 30] Resources
The design of an Organizing System is strongly shaped by what is being organized, the first of the design decisions 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?
Case Studies:
[6 - Feb 1] 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.
- Economist (2019), Countryside Catches Up With Napoleon
- Kamiya. G. (2021). The Holier-Than-Thou Crusade in San Francisco. The Atlantic
- Wheatley, M. (2004). Operation Clean Data, CIO Magazine.
Case Study:
[7 - Feb 6] Resource Description
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.
[8 - Feb 8] Resource Properties Framework; Describing Non-text Resources
Two important dimensions for understanding and contrasting resource properties used in descriptions and organizing principles are: property essence—whether the properties are intrinsically or extrinsically associated with the resource, and; property persistence—whether the properties are static or dynamic. Taken together these two dimensions yield four categories of properties.
Non-text resources raise interesting questions about resource description and there are novel approaches to answering them.
- Revisit previously assigned TDO 5.2.1.2, 6.2.3, 6.4.5
Case Studies
[9 - Feb 13] Describing Relationships and Structures, Part 1; Social Network Analysis Using Graph Theory
An organizing system can use existing relationships among resources, or it can create relationships by applying organizing principles to arrange the resources. We need a vocabulary for talking about these relationships precisely and consistently so that we can analyze, design, reason about them, and implement them in organizing systems. In fact, we need 4 different vocabularies because there are 4 different perspectives on relationships
This lecture introduces the structural perspective, which 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.
- Resource Relationships TDO v5 7.1 and 7.2
- 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)
[10 - Feb 15] Describing Relationships and Structures, Part 2
This lecture introduces the specialized vocabulary used to describe semantic relationships between resources and between the concepts and words used in resource descriptions.
Part 2: Sensemaking and Organizing in Language and Culture
[11 - Feb 20] Introduction to Categorization; Category Structure
Categories are sets or groups of resources or abstract entities that are treated the same. Categories are cognitive and linguistic models for applying prior knowledge; creating and using categories are essential human activities. Categories are involved whenever we perceive, communicate, analyze, predict, classify – or otherwise attempt to make sense of our experiences.
Category structure refers to the principles by which a category is defined. Categories can be defined by enumerating their members, on the basis of one or more shared properties, or by less rigid principles like similarity.
[12 - Feb 22] Astronomical Sensemaking and Other "Natural" Categories
The universality of temporal concepts for "day," "month," "year," and "season" reflects their basis in natural astronomical events that have been systematically observed and recorded for well over ten thousand years. Likewise, all cultures impose patterns on stars, often associated with myths. Studying how these constellation-embodied myths evolve and disperse over time using techniques of evolutionary biologists creates "the tree of myths."
- Breasted, J. (1935). The beginnings of time-measurement and the origins of our calendar. The Scientific Monthly, 41(4), 289-304.
- d'Huy, J. (2016). The evolution of myths. Scientific American, 315(6), 62-69.
- Boxer, A. (2020) Selection from A Scheme of Heaven
[13- Feb 27] Universal Cognition? Folk Biology; Linguistic Relativity; Bi-lingualism & Bi-culturalism
All human languages and cultures divide the physical and experiential “worlds” into categories; there are substantial similarities but interesting differences. Analyzing these differences makes us realize that language is much more a cultural construction than a biological one.
- Cultural Categories, Language and Thought, TDO v5 8.2.1
- Boraditsky, Lera. (2017) How Language Shapes the Way We Think. TED Talk
- Atran, S., and Medin, D. L., & (2008). The Native Mind and the Cultural Construction of Nature. Chapter 1, Introduction.
- Christiansen. M., and Chater, N. (2022). 7000 natural experiments in cultural evolution, (selection from Chapter 7), The Language Game: How Improvisation Created Language and Changed the World.
[14 -Feb 29] Social thinking; Dis-/Mis-/Mal- information
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?
Social thinking is a critical factor in the problem of "fake news" -- which we will unpack into more precise categories so we can better understand its causes, mechanisms, and remedies.
- Wardle. C., and Derakhshan (2018). Thinking about "information disorder" - formats of misinformation, disinformation, and mal-information.
- Louf, T., et al. (2022) American cultural regions mapped through the lexical analysis of social media. arXiv:2208.07649
[15 - March 5] EXAM
Part 3: Sensemaking and Organizing As Individuals
[16 - March 7] "Automatic organizing" using perceptual processes, Gestalt principles, statistical learning
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. Infants have a remarkable ability to extract patterns in auditory and visual inputs so that they can learn their native language and cope with the complexity of their physical environments.
- Cameron, C. Exploring the Gestalt Principles of Design
- Pearl, L. (2018). Acquisition of Language 2. Saffran et al 1996
- Chater, N., & Loewenstein, G. (2016). The under-appreciated drive for sense-making. Journal of Economic Behavior & Organization, 137-141 (sections 1 and 2)
- Chater, N. (2018). The Mind is Flat. Chapter 7, "The Cycle of Thought."
- Saffran, Jenny R. (2020). Statistical Language Learning in Infancy. Child Development Perspectives 49-54.
[17 - March 12] Categories made by Individuals; Personal Possession Management; Personal Information Management
Individual categories are created to satisfy ad hoc requirements that emerge from an individual’s unique experiences, preferences, and resource collections. How people manage their personal possessions is a trendy topic and a lucrative consulting business, but some people have psychological disorders that prevent them from doing it well. How people organize their personal information has been extensively studied by researchers and designers. The Memex, proposed by Vannevar Bush almost 80 years ago, embodied many novel ideas about personal information management and is often viewed as the inspiration for personal computers and the web.
- Individual Categories, TDO v5 8.2.2
- Silvestre, D. The Life-Changing Magic of Tidying Up by Marie Kondo: Summary and Lessons.
- Matax-Cols, D. (2014). Hoarding Disorder, New England Journal of Medicine.
- Vitale, Odom, & McGrenere (2019). Keeping and Discarding Personal Data: Exploring a Design Space, Designing Interactive Systems Conference.
- Bush, V. (1945). As We May Think. The Atlantic Monthly.
[18 - March 14] 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. Chapter 1
- 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
Part 4: Sensemaking and Organizing in Defined/Institutional Contexts
[19 - March 19] Institutional Categories, Classification, and Standards
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 and require ongoing governance and maintenance. Because classification and standardization are closely related, the lecture also analyzes standards and standards-making as they apply to Organizing Systems.
- Institutional Categories, TDO v5 8.2.3
- Classification and Standardization, Bibliographic Classification, Faceted Classification, (TDO v4 CC-Edited)
[20 - March 21] Measurement, Controlled Vocabularies, Business Interoperability
Sensemaking and organizing in institutional or business contexts depend on systematic and robust descriptions of resources, events, processes, and people. Systems of measurement have evolved from ad hoc techniques for comparison, many of which were based on body parts or common work artifacts. Controlled vocabularies and standard description schemas are essential in the operation of information-intensive and data-driven businesses.
- Forms of Resource Descriptions, TDO v5 6.4
- Glushko, R., and McGrath, T. (2005). Document Engineering
- Chapter 6, "When Models Don't Match: The Interoperability Challenge" (read section 6.0)
- Cooperrider, K., and Gentner, D. (2019). The career of measurement. Cognition.
- Mosteller, F., and Youtz, C. (1990). Quantifying probabilistic expressions. Statistical Science.
[21 - April 2] Case Study Proposals
Brief presentations by students of their proposed case study.
Five more case studies assigned to further develop the analysis and design skills needed to write a case study.
- Dolan, K. (2019). Organizing for Inebriation Case Study
- Zou, J. (2019) High-end Professional Kitchens (Chez Panisse Case Study)
- Shang, S. (2018) Chinese Medicine Cabinet Case Study
- Wang, Z. (2021) Golf Round Management Case Study
- Palmquist, A. (2021) Residential Life Management Case Study
[22 - Apr 4] Document Engineering and Modeling
We will discuss techniques of Document Engineering for systematically identifying the content, structure, and presentation components in documents and datasets to enable the design of robust models for information-intensive domains. We will briefly contrast methods and formats for information models that differ in their semantic precision, maintainability, and ease of use.
- Glushko, R. (2005) Modeling Methods and Artifacts for Crossing the Data/Document Divide XML 2005 Conference Proceedings (read through section 4)
- Getting started with schema.org, sections 1 and 2
[23 - April 9] 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.
Distributed cognition and external representations help people understand and interact effectively when faced with challenging cognitive and physical tasks.
- Information Architecture and Organizing Systems (3.3.3.2). Interaction Design (3.4), Affordance and Capability (3.4.1)
- Classification by Activity Structure. TDO Section 8.5
- 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]
- Kirsh, D. (2009). Interaction, External Representation, and Sense-Making. Cognitive Science Society Annual Meeting.
Part 5: Sensemaking and Organizing with Data
[24- April 11] Organizing with Interaction Traces
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
- Jarvis, D., Westcott, K., and Jones, D. (2021). The hyperquantified athlete, Technology, Media, and Telecommunications Predictions.
- DeCarbo, B. How Data is Changing the College Experience. Wall Street Journal, 19 August 2022.
[25 - April 16] Behavioral Economics
Choices about descriptions and organizing principles involve tradeoffs that determine which interactions are efficient, possible, difficult, or impossible.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.
- Johnson, Eric J., et al. (2012) "Beyond nudges: Tools of a choice architecture."Marketing Letters 23.2 (2012): 487-504.
- Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
- Morrison, S. (2021). Dark patterns, the tricks websites use to make you say yes, explained. https://www.vox.com/recode/22351108/dark-patterns-ui-web-design-privacy
[26 - April 18] Computational Description
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
- Organizing with Descriptive Statistics, TDO 3.3.4
- Computational categories, TDO 7.2.5
- Similarity TDO 7.3.6*
[ 27 - April 23] Computational Classification; Computation {and,or, vs.} Cognition
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
Computation {and,or, vs.} Cognition
[ 28 - April 25] Case Study Working Groups
You will meet in groups to present your analyses, artifacts, whatever -- and we'll scramble the groups twice so you get feedback from a lot of your classmates.
[ 29 – April 30] Course Review
This will be a recorded lecture, no need to come to class. Watch the recording whenever it makes sense for you.
[ 30 – May 2] Case Study Presentations
May 6 - final exam
May 8 - case study reports due
Course Summary:
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