COMPUTATIONAL SOCIAL SCIENCE

CSS SEMINAR - Brent Auble

Friday, March 20, 3:00 p.m.
Center for Social Complexity Suite
Research Hall, Third Floor

Adding Narrative to ABMs and Identifying Which Ones Are Worth Reading

Brent Auble, PhD Student
Computational Social Science
George Mason University

ABSTRACT: One strength of agent-based models (ABM) is that they allow for collection of all details of the characteristics and behavior over time of every individual agent and the environment, theoretically enabling the analysis of micro-level interactions between individuals and within small groups. In practice, however, the volume of raw data generated by each run of a model (thousands of which might be done to test a range of parameters) makes it difficult to identify unusual agent behaviors and interactions, and analysis of models typically ends up being done by aggregating data and reporting overall trends. One solution to the challenge of falling back on aggregative analysis is to have the ABM itself generate narratives describing the behavior of individual agents and the interactions of agents. I will argue that Humans tend to like stories and reading the "life history" of an agent can be more easily understood than reviewing a series of numbers. Thus, a model that generates narratives for each agent can allow for easier analysis of individual behavior. While narratives should be easier to understand than purely numeric results, a model that generates potentially thousands of texts is not likely to be more tractable than one that does not. A solution to this challenge is to identify those agents who are most likely to be "interesting." Interestingness is identified by selecting agents whose behavior is at the extremes of expected values (e.g. the tails of a Gaussian distribution), and the most interesting agents are those who are at the extremes of more than one variable. In addition, the model should select some agent narratives whose behavior falls exactly where expected (e.g. the mean or median), in order to avoid biasing a researcher's analysis too greatly toward extreme results.

This talk will discuss techniques for generating narrative from the Zero Intelligence Traders ABM, and for identifying the agents whose narratives are most worth the time of a human to read.