Exploring agent-based simulations in political science using Aggregate Temporal Graphs

Abstract

Agent-based simulation has become a key technique for modeling and simulating dynamic, complicated behaviors in social and behavioral sciences. As these simulations become more complex, they generate an increasingly large amount of data. Lacking the appropriate tools and support, it has become difficult for social scientists to interpret and analyze the results of these simulations. In this paper, we introduce the Aggregate Temporal Graph (ATG), a graph formulation that can be used to capture complex relationships between discrete simulation states in time. Using this formulation, we can assist social scientists in identifying critical simulation states by examining graph substructures. In particular, we define the concept of a Gateway and its inverse, a Terminal, which capture the relationships between pivotal states in the simulation and their inevitable outcomes. We propose two real-time computable algorithms to identify these relationships and provide a proof of correctness, complexity analysis, and empirical run-time analysis. We demonstrate the use of these algorithms on a large-scale social science simulation of political power and violence in present-day Thailand, and discuss broader applications of the ATG and associated algorithms in other domains such as analytic provenance.

A Network Analytic Approach to Investigating a Land-Use Change Agent-Based Model

“…The weights of the edges in such graphs would represent the number of times the two connected states were traversed in one or more runs of the simulation. Crouser et al [2] propose the use of aggregated temporal graphs (ATGs), the vertices of which encapsulate a unique state configuration of the model. Multiple model runs are then aggregated into a single graph.…”

“…Betweenness centrality (C B ) measures the extent to which a vertex lies in the pathways between all pairs of vertices, and thus captures the extent to which a vertex is a gateway while accounting for the global graph structure. 2 In order to account for higher betweenness for those vertices that receive or emit many edges, we substitute multiple edges with a single edge having a weight of the count of edges. These weights are inverted (i.e., 1/weight) so that the weighted edge between two vertices consecutively traversed in many simulation runs will be considered a shorter path than if they were traversed by few simulations.…”