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Reinforcement Learning for Economic Policy: A New Frontier?

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  • Callum Rhys Tilbury

Abstract

Agent-based computational economics is a field with a rich academic history, yet one which has struggled to enter mainstream policy design toolboxes, plagued by the challenges associated with representing a complex and dynamic reality. The field of Reinforcement Learning (RL), too, has a rich history, and has recently been at the centre of several exponential developments. Modern RL implementations have been able to achieve unprecedented levels of sophistication, handling previously unthinkable degrees of complexity. This review surveys the historical barriers of classical agent-based techniques in economic modelling, and contemplates whether recent developments in RL can overcome any of them.

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  • Callum Rhys Tilbury, 2022. "Reinforcement Learning for Economic Policy: A New Frontier?," Papers 2206.08781, arXiv.org, revised Feb 2023.
  • Handle: RePEc:arx:papers:2206.08781
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    Cited by:

    1. Benjamin Patrick Evans & Sumitra Ganesh, 2024. "Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning," Papers 2402.00787, arXiv.org.
    2. Kshama Dwarakanath & Svitlana Vyetrenko & Peyman Tavallali & Tucker Balch, 2024. "ABIDES-Economist: Agent-Based Simulation of Economic Systems with Learning Agents," Papers 2402.09563, arXiv.org.

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