Predicting interactions between agents in agent-based modeling and simulation of sociotechnical systems

Agent-based modeling and simulation are a valuable research tools for the analysis of dynamic and emergent phenomena of large-scale complex sociotechnical systems. The dynamic behavior of such systems includes both the individual behavior of heterogeneous agents within the system and the emergent behavior arising from interactions between agents; both must be accurately modeled and efficiently executed in simulations. This paper provides a timing and prediction mechanism for the accurate modeling of interactions among agents, correspondingly increasing the computational efficiency of agent-based simulations. A method for assessing the accuracy of interaction prediction methods is described based on signal detection theory. An intelligent interaction timing agent framework that uses a neural network to predict the timing of interactions between heterogeneous agents is presented; this framework dramatically improves the accuracy of interaction timing without requiring detailed scenario-specific modeling efforts for each simulation configuration.