Abstract:
Agent-Based Modeling (ABM) is an approach for building computer models of social situations where computer agents interact with each other within a computer-generated environment. The agents have limited information and the environment can change, simulating complex situations, and interactions between agents and the environment can result in unexpected “emergent” behaviors. One value of ABMs 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 interactions, and analysis of models ends up being done by aggregating data and reporting overall trends. This thesis explores using computer-generated narratives describing the behavior of an individual agent as an alternate method for evaluating the micro-level behavior of an ABM. In addition, approaches are demonstrated for identifying the agents whose narratives are most worth the time to read.
