Analysis of Cognitive Architecture in the Cultural Geography Model

Abstract

The Cultural Geography (CG) Model is a multi-agent discrete event simulation developed by TRAC-Monterey. It provides a framework to study the effects of operations in Irregular Warfare, by modeling behavior and interactions of populations. The model is based on social science theories; in particular, agent decision-making algorithms are built on Exploration Learning (EL) and Recognition-Primed Decision (RPD), and trust between entities is modeled to increase realism of interactions. This study analyzed the effects of these components on behavior and scenario outcome. It aimed to identify potential approaches for simplification of the model, and improve traceability and understanding of entity actions. The effect of using EL/RPD with/without trust was tested in basic stand-alone scenarios to assess its impact in isolation on entities perception of civil security. Further testing also investigated the influence on entity behavior in the context of obtaining resources from infrastructure nodes. The findings indicated that choice of decision-making methods did not significantly change scenario outcome, but variance across replications was greater when both EL and RPD were used. Trust was found to delay the rate of change in population stance due to interactions, but did not affect overall outcome if given sufficient time to reach steady state.