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This article proposes an agent-based policy simulation framework that can be applied to the cases satisfying: 1) the agents try to maximize some intertemporal preference and 2) the impacts of different factors on agents&#x2019; behavioral tendency are monotonic. By combining the simulation and optimization methods, this framework balances the flexibility and validity of agent-based models (ABMs): the sigmoid function is modified and used to model agents&#x2019; decision-making rules, and the evolutionary training method is used to calibrate agents&#x2019; behavioral parameters. Based on an example for the emission trading scheme, the application of the framework is presented and evaluated in detail.

An Agent-Based Framework for Policy Simulation: Modeling Heterogeneous Behaviors With Modified Sigmoid Function and Evolutionary Training