Dissertation: Narrative Generation for Agent-Based Models
Abstract: This dissertation proposes a four-level framework for thinking about having agent-based models (ABM) generate narrative describing their behavior, and then provides examples of models that generate narrative at each of those levels. In addition, “interesting” agents are identified in order to direct the attention of researchers to the narratives most likely to be worth spending their time reviewing. The focus is on developing techniques for generating narrative based on agent actions and behavior, on techniques for generating narrative describing aggregate model behavior, and on techniques for identifying “interesting” agents. Examples of each of these techniques are provided in two different ABMs, Zero-Intelligence Traders (Gode & Sunder, 1993, 1997) and Sugarscape (Epstein & Axtell, 1996).
simulation consumer behavior by MABS
Opinion Dynamics, Climate Change, Economics, Behavioral Decision Making
Smarzhevskiy Ivan, born 1961, graduated from the Faculty of Mechanics and Mathematics of Moscow State University in 1983. Candidate of Economic Sciences since 2000. Smarzhevskiy Ivan is currently a lecturer in the magistracy of the Peoples’ Friendship University of Russia.
Research interests: individual and collective behavior in the organization, decision making, sociology of small groups.
decision making, sociology of small groups, agent based models
Agent-based computational economics, Economics of Migration, Behavioral Macroeconomics, Networks