Building and using agent-based models is often impractical, in part due to the cost of including ex-pensive subject matter experts (SMEs) in the de-velopment process. In this paper, we describe a method for “bootstrapping” model building to low-er the cost of overall model development. The models we are interested in here capture dynamic phenomena related to international and sub-national conflict. The method of acquiring these models begins with event data drawn from news reports about a conflict region, and infers model characteristics particular to a conflict modeling framework called the Power Structure Toolkit (PSTK). We describe the toolkit and how it has been used prior to this work. We then describe the current problem of modeling conflict and the em-pirical data available to learn models, and exten-sions to the PSTK for model generation from this data. We also describe a formative evaluation of the system that compares the performance and costs of models built entirely by an SME against models built with an SME aided by the automated model generation process. Early results indicate at least equivalent prediction rates with significant savings in model generation costs.
Reference:
Taylor, G., Quist, M., and Hicken, A. (2009). “Acquiring Agent-based Models of Conflict from Event Data.” Proceedings IJCAI 2009. AAAI Press. Pasadena, CA.