CS382:Unit-compsoc

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Computational Sociology and Agent Based Modeling

Overview

This unit showcases Agent Based Modeling techniques and allows students to investigate them in a computational sociology context.

Background Reading for Teachers and TAs

• Computational Sociology (wikipedia) Basic overview of computational sociology.
• Tutorial on Agent Based Modeling ACM Digital Library An in depth look at ABM theory and technologies.

Reading Assignments for Students

• The Structural Dynamics of Corruption: Artificial Society Approach It's a bit longwinded and technical, but I think 13 pages is reasonable. It's a "welcome to college" kind of text. I don't expect that they'll get any of the mathy stuff, though, so I think maybe just clipping out the parts about choosing rulesets and giving them that would be useful.
• Artificial Societies, Virtual Worlds and the Shared Problems and Possibilities of Emergence This is relevant because of its discussion of emergence. I think for students it will help to justify why they're learning agent based modeling (because the emergent behavior of the models mimics the emergent behavior of these virtual worlds)
• A Proximate Mechanism for Communities of Agents to Commemorate Long Dead Ancestors Though possibly more computational anthropology, I think this is a softer intro than the corruption model, and arguably more interesting.
• Can Extremism Guarantee Pluralism? Another very long one, but such a fascinating topic I think it would engage students (especially Earlhamites).
• Computational Sociology and Agent Based Modeling Too long to read in full, but the introductory prose is extremely illuminating.

== Reference Material

• Tutorial on Agent Based Modeling ACM Digital Library An in depth look at ABM theory and technologies.

Lecture Notes

• What is Agent Based Modeling?
  • Game of Life
  • Emergent Behavior
  • What are its advantages and disadvantages?
• Where is it useful?
  • Economics
  • Sociology
  • Biology
  • Information Science
• Some Examples
  • Boids
• Computational Sociology
  • Who?
    • Axtell and Epstein - Growing Artificial Societies
  • Why?
    • Perhaps distribute and discuss introductory materials from "Computational Sociology and Agent Based Modeling" article
  • Example models, demo in class
    • Self Assembling of Information on networks
    • Sugarscape

Lab

• Tie this in with facebook/myspace/<social network here (virtual or real)>. Who do you know? Draw a graph of your best friends, good friends, acquaintances, less-than- acquaintances and follow the coloring/sizing of the model. Does this model resemble what emerges in the dynamic model (below)? Do several runs of the model and match their emergent stages against your drawing. Discuss validity of model based on this. The key for the lab is "Does this model resemble what emerges in the dynamic model?" The applet will run until it reaches a state of relative stasis. The static model that the students develop will, hopefully, have similar characteristics to the applet's emerged results. The idea is that 'reality' - the static model - appears in the emergent behavior of the dynamic model
  • Model

• Say you want to model sitting patterns in Saga. Come up with a reasonable set of agents and agent relations that you would use to acccomplish this. What are their behaviors? Think beyond just people (eg tables, food [affects how long someone would stay).

Software

Java. For applets. Only req I've encountered so far (I had to get it!)

Bill of Materials

Nada. Paper and pencil.

Evaluation

CRS Questions

• 1. What is Emergent Behavior?
  • A. The complex outcome of the interaction of many simple rulesets
  • B. How we verify and validate Agent Based models
  • C. How we define rulesets for agents in an agent based model
  • D. How we determine the formulas we use in mathematical modeling
• 2. Who wrote the seminal text on sociological agent-based modeling?
  • A. Peck & Rogers, et. al
  • B. Axtell and Epstein
  • C. Axeman and Edlefsen
  • D. Whitehall and North
• 3. What is the name of the first agent-based biological model?
  • A. Droids
  • B. BirdBots
  • C. Boids
  • D. BDroids

Quiz Questions

Explain how agent based modeling's concept of emergent behavior could be used to explain some natural phenomenon.

Agent Based Modeling and Computational Sociology Metadata

This unit introduces students to Agent Based Modeling through the use of examples and then has them work in a context of Computational Sociology to learn more This section contains information about the goals of the unit and the approaches taken to meet them.

Scheduling

Should certainly come after mathematical modeling and before systems dynamics.

Concepts and Techniques

• Pedagogical
  • Inquiry Based Learning
  • Degrees of open endedness
  • uses science to illustrate complexity of world around us
• Structural
  • CRS
  • largely platform independent

General Education Alignment

• Analytical Reasoning Requirement
  • Abstract Reasoning - From the [Catalog Description] Courses qualifying for credit in Abstract Reasoning typically share these characteristics:
    • They focus substantially on properties of classes of abstract models and operations that apply to them.
      • Agents -> abstract models
    • They provide experience in generalizing from specific instances to appropriate classes of abstract models.
      • discussion of boids, sugarscape and agent based modeling as a whole
    • They provide experience in solving concrete problems by a process of abstraction and manipulation at the abstract level. Typically this experience is provided by word problems which require students to formalize real-world problems in abstract terms, to solve them with techniques that apply at that abstract level, and to convert the solutions back into concrete results.
      • the emergent behavior is a process of abstract manipulation; comparing emergent behavior back against the real world is "converting solutions back into concrete results"
  • Quantitative Reasoning - From the [Catalog Description] General Education courses in Quantitative Reasoning foster students' abilities to generate, interpret and evaluate quantitative information. In particular, Quantitative Reasoning courses help students develop abilities in such areas as:
    • Using and interpreting formulas, graphs and tables.
      • Little of this - the point is to avoid formulas (initially), but graphs and tables come up when analyzing model results.
    • Representing mathematical ideas symbolically, graphically, numerically and verbally.
      • Little of this. We're working with people, not numbers.
    • Using mathematical and statistical ideas to solve problems in a variety of contexts.
      • Nada.
    • Using simple models such as linear dependence, exponential growth or decay, or normal distribution.
      • This could be worked in, but isn't there now.
    • Understanding basic statistical ideas such as averages, variability and probability.
      • Nope.
    • Making estimates and checking the reasonableness of answers.
      • In a more abstract way than most mathiness.
    • Recognizing the limitations of mathematical and statistical methods.
      • This is fulfilled - the point of ABM is the limitation of mathematical and statistical methods.
• Scientific Inquiry Requirement - From the [Catalog Description] Scientific inquiry:
  • Develops students' understanding of the natural world.
    • Yes. Agent based methodology informs a way of thinking about natural processes that differs from more typical techniques - the ideas of emergent behavior are present in every day life.
  • Strengthens students' knowledge of the scientific way of knowing — the use of systematic observation and experimentation to develop theories and test hypotheses.
    • None.
  • Emphasizes and provides first-hand experience with both theoretical analysis and the collection of empirical data.
    • Yes. Models = theoretical. Analyzing one's own social circle, for example, is empirical collection.

Scaffolded Learning

None. The unit is lecture and work; neither fall into a set scaffolded structure.

Inquiry Based Learning

The get to think creatively; they make a (static) model and get to think up their own agent based model (in an easy, but still interactive, way)