Timing agent interactions for efficient agent-based simulation of socio-technical systems

In recent decades, agent-based modeling and simulation (ABMS) has been increasingly used as a valuable approach for design and analysis of dynamic and emergent phenomena of large-scale, complex multi-agent systems, including socio-technical 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 within their work environment; both must be accurately modeled and efficiently executed in simulations. An important issue in ABMS of socio-technical systems is ensuring that agents are updated together at any time where they must interact or exchange data, even when the agents' internal models use fundamentally different methods of advancing their internal time and widely varying update rates. This requires accurate predictions of interaction times between agents within the environment. Predicting the time of interactions, however, is not a trivial problem. Thus, timing mechanisms that advance simulation time and select the proper agent to be executed are crucial to correct simulation results. This chapter describes a timing and prediction mechanism for accurate modeling of interactions among agents which also increases the computational efficiency of agent-based simulations. An experiment comparing different timing methods highlighted the gains in computational efficiency achieved with the new timing mechanisms and also emphasized the importance of identifying correct interaction times. An intelligent timing agent framework for predicting the timing of interactions between heterogeneous agents using a neural network and a method for assessing the accuracy of interaction prediction methods based on signal detection theory are described. An application of agent-based modeling and simulation to air transportation systems serves as a test case and the simulation results of different interaction prediction models are presented. The insights of using the framework and method to the design and analysis of complex socio-technical systems are discussed.