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Agent-based modeling (ABM) has many applications in the social sciences, biology, computer science, and robotics. One of the most important and challenging phases in agent-based model development is the calibration of model parameters and agent behaviors. Unfortunately, for many models this step is done by hand in an ad-hoc manner or is ignored entirely, due to the complexity inherent in ABM dynamics. In this paper we present a general-purpose, automated optimization system to assist the model developer in the calibration of ABM parameters and agent behaviors. This system combines two popular tools: the MASON agent-based modeling toolkit and the ECJ evolutionary optimization library. Our system distributes the model calibration task over very many processors and provides a wide range of stochastic optimization algorithms well suited to the calibration needs of agent-based models. 

Agent-based models can present special challenges to model calibration due in part to their high parameter count, tunable agent behaviors, complex emergent macrophenomena, and potentially long runtimes. However, due to this difficulty, these models are most often calibrated by hand, or with hand-coded optimization tools customized per-problem if at all. As simulations increase in complexity, we will require general-purpose, distributed model calibration tools tailored for the needs of agent-based models. In this paper, we present the results of a system we have developed which combines two popular tools, the MASON agent-based modeling toolkit, and the ECJ evolutionary optimization library. This system distributes the model calibration task over many processors, provides many stochastic optimization algorithms well suited to the calibration needs of agent-based models, and offers the ability to optimize not just model parameters but agent behaviors. 

This paper describes Distributed MASON, a distributed version of the MASON agent-based simulation tool. Distributed MASON is architected to take advantage of well known principles from Parallel and Discrete Event Simulation, such as the use of Logical Processes (LP) as a method for obtaining scalable and high performing simulation systems. We first explain data management and sharing between LPs and describe our approach to load balancing. We then present both a local greedy approach and a global hierarchical approach. Finally, we present the results of our implementation of Distributed MASON on an instance in the Amazon Cloud, using several standard multi-agent models. The results indicate that our design is highly scalable and achieves our expected levels of speed-up. 

MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON's architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.