Local Environment Analysis and Rules Inferring Procedure in an Agent Based Model - Applications in Economics

Dospinescu, Andrei Silviu

doi:10.2139/ssrn.1802459

Cited by 2 publications

(1 citation statement)

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“…This interesting study demonstrated that applying ML in improving situational awareness as well as optimizing behaviors can help achieve even better performance in decision-making. Dospinescu [103] investigated how to apply a neural network algorithm to enable the agents to scan their local environment and infer their actions based on the accumulated data during simulation. He also discussed how an agent can infer a behavior rule that was not defined at the beginning of the simulation using the Colceag approach, which can be combined with the ML technique for metalearning.…”

Section: B Microagent Behavioral Interventionmentioning

confidence: 99%

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Zhang

Valencia

Chang

2023

IEEE Trans. Neural Netw. Learning Syst.

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Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.

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Section: B Microagent Behavioral Interventionmentioning

confidence: 99%