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

Abstract

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' 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' decision-making rules, and the evolutionary training method is used to calibrate agents' behavioral parameters. Based on an example for the emission trading scheme, the application of the framework is presented and evaluated in detail.

Melodie: Agent-based Modeling in Python

“…Both the Calibrator and Trainer modules are based on a Genetic Algorithm (GA), and the Trainer framework is introduced in detail in Yu (2022).…”