Course Syllabus

Jump to Today

CogSci 150, 3 units, Spring 2021
Tuesday & Thursday 2:10-3:30pm

TEACHING TEAM

Professor Robert J. Glushko
glushko@berkeley.edu

Nicole Hsu
nicolehsu@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, breakout sessions, and peer learning as possible during the three hours of lecture each week.

The 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.

  • 6 assignments (35%)
  • Two timed exams, administered by bCourses (30%)
  • 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 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, (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. We will be reading parts of the  "Core Concepts" and "Abridged" editions; the "Professional" edition can be consulted for more depth. 

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 19] 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 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:

[2 - Jan 21] Design Decisions for Organizing Systems

This lecture introduces six broad design questions or dimensions whose intertwined answers define an Organizing System: What, why, how,  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.

Case Studies:

[3 - Jan 26] Models for Organizing Systems; More Case Studies

Many academic fields – management, operations research, informatics, etc. – provide models for describing organizing systems. In this lecture, we'll show how these different models distinguish and highlight different aspects of the organizing system. They can be thought of as different perspectives or points of view whose effectiveness depends on the nature of the resources and organizing principles in the organizing system.

[4 - Jan 28] 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 2] Resources

The design of an Organizing System is strongly shaped by what is being organized, the first of the six 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 4] 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.

Case Study:

[7 - Feb 9] Resource Description

Categories can be defined 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.

  [8 - Feb 11] Document Engineering; The Modeling Debate

We will discuss techniques of Document Engineering for systematically identifying the content, structure, and presentation components in documents to enable the design of robust information models. We will contrast methods and formats for information models that differ in their semantic precision, maintainability, and ease of use. 

[9 - Feb 16] Describing Non-text Resources and Media; Course Review

We will apply the concepts of resource abstraction, granularity, and description to art, artifacts, and music.  We'll review the foundational concepts and methods for organizing systems. We will also introduce the requirements and schedules for the case study students write as the capstone for this course.

Case Studies

Sensemaking and Organizing in Language and Culture

[10 - Feb 18] Introduction to Categorization; Cultural Categories;  Linguistic Relativity; Bilingualism and Biculturalism

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; there are substantial similarities but interesting differences.

[11 - Feb 23] Cognitive Universals? Folk biology

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.

Studying how myths evolve and disperse over time suggest that  they are cultural constructs, not products of a universal collective unconscious.

[12 - Feb 25] 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 2] 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

[14 - March 4] 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?

[15 - March 9] EXAM

Sensemaking and Organizing As Individuals

[16 - March 11] "Automatic organizing" using perceptual processes, Gestalt principles, statistical learning; Conversation with Chater & Saffran

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.

[17 - March 16] 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

[18 - March 18] 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.  Distributed cognition and external representations help people understand and function effectively when faced with challenging cognitive and physical tasks. Vannevar Bush's proposed the Memex ("Memory Extender")  in 1945 as a novel technology for personal information management and foreshadowed much of personal computing and web browsing.

Sensemaking and Organizing in Defined/Institutional Contexts

[19 - March 30] Category Structure; Institutional Categories

"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. 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

[20 - April 1] 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.  Because classification and standardization are closely related, the lecture also analyzes standards and standards-making as they apply to Organizing Systems.

[21 - April 6] Measurement, Controlled Vocabularies, 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.

[22 - April 8] Organizing and Interaction Design; Applied Behavioral Economics

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.

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.

Sensemaking and Organizing with Data

[23 - April 13] 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

[24 - April 15] Exploratory Data Analysis and Information Visualization

Guest Lecture by Amy Fox, UCSD Cognitive Science

If you are a novice with respect to information visualization, read this article:

If you have some familiarity with information visualization, read this article:

[25 - April 20] Course Review

[26 - April 22] EXAM

[27 - April 27] 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

[ 28 - April 29] Computational Classification

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

[ 29 – May 4] Case Study Presentations

[ 30 – May 6] Case Study Presentations

Course Summary:

Date Details Due