Abstract
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.
Highlights
S CIENTIFIC models seek to represent empirical objects, phenomena, and physical processes in a logical and numerical way for exhibiting specific characteristics, which is critical for many disciplines [1]
We thoughtfully investigate papers related to versatile applications of machine learning (ML) in Agent-based modeling (ABM)
In terms of granularity and functionality, we explicitly categorize the applications of ML-based ABMs into four scenarios, i.e., microagent awareness learning, microagent behavioral interventions, macrolevel ABMs emergence emulators, and macrolevel ABMs decision-making
Summary
S CIENTIFIC models seek to represent empirical objects, phenomena, and physical processes in a logical and numerical way for exhibiting specific characteristics, which is critical for many disciplines [1]. The motivation of this article is to investigate how different ML techniques can be combined with the ABM for modeling complex, large-scale systems from the perspective of improving the model accuracy or robustness and rendering better decision-making strategies. The defined scenarios, summarized framework, and application procedures in this article can aid academic researchers and industrial practitioners interested in applying ML techniques in ABM They can quickly identify the category of the to-be modeled problem and pin down the key issues they are supposed to address by further diving into the framework and procedures we present for each scenario. We present the pros and cons of the frequently observed ML techniques in various ABM applications and discuss the future perspective of applying ML techniques in ABM with key issues that may serve as the future research topics of this area
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