Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling

• URL: http://arxiv.org/abs/2405.02161v2
• Date: Mon, 21 Oct 2024 21:20:29 GMT
• Title: Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling
• Authors: Simone Brusatin, Tommaso Padoan, Andrea Coletta, Domenico Delli Gatti, Aldo Glielmo,
• Abstract summary: We leverage multi-agent reinforcement learning (RL) to expand the capabilities of agent-based models (ABMs) We show that RL agents spontaneously learn three distinct strategies for maximising profits, with the optimal strategy depending on the level of market competition and rationality. We also find that RL agents with independent policies, and without the ability to communicate with each other, spontaneously learn to segregate into different strategic groups, thus increasing market power and overall profits.
• Score: 1.7546137756031712
• License:
• Abstract: Agent-based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general equilibrium assumptions. However, agents within an ABM follow predetermined 'bounded rational' behavioural rules which can be cumbersome to design and difficult to justify. Here we leverage multi-agent reinforcement learning (RL) to expand the capabilities of ABMs with the introduction of 'fully rational' agents that learn their policy by interacting with the environment and maximising a reward function. Specifically, we propose a 'Rational macro ABM' (R-MABM) framework by extending a paradigmatic macro ABM from the economic literature. We show that gradually substituting ABM firms in the model with RL agents, trained to maximise profits, allows for studying the impact of rationality on the economy. We find that RL agents spontaneously learn three distinct strategies for maximising profits, with the optimal strategy depending on the level of market competition and rationality. We also find that RL agents with independent policies, and without the ability to communicate with each other, spontaneously learn to segregate into different strategic groups, thus increasing market power and overall profits. Finally, we find that a higher number of rational (RL) agents in the economy always improves the macroeconomic environment as measured by total output. Depending on the specific rational policy, this can come at the cost of higher instability. Our R-MABM framework allows for stable multi-agent learning, is available in open source, and represents a principled and robust direction to extend economic simulators.

Related papers

• Deep Reinforcement Learning Agents for Strategic Production Policies in Microeconomic Market Simulations [1.6499388997661122]
  We propose a DRL-based approach to obtain an effective policy in competitive markets with multiple producers. Our framework enables agents to learn adaptive production policies to several simulations that consistently outperform static and random strategies. The results show that agents trained with DRL can strategically adjust production levels to maximize long-term profitability.
  arXiv  Detail & Related papers  (2024-10-27T18:38:05Z)
• Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning [4.40301653518681]
  Agent-based models (ABMs) have shown promise for modelling various real world phenomena incompatible with traditional equilibrium analysis. Recent developments in multi-agent reinforcement learning (MARL) offer a way to address this issue from a rationality perspective. We propose a novel technique for representing heterogeneous processing-constrained agents within a MARL framework.
  arXiv  Detail & Related papers  (2024-02-01T17:21:45Z)
• EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities [43.70290385026672]
  We introduce EconAgent, a large language model-empowered agent with human-like characteristics for macroeconomic simulation. We first construct a simulation environment that incorporates various market dynamics driven by agents' decisions. Through the perception module, we create heterogeneous agents with distinct decision-making mechanisms.
  arXiv  Detail & Related papers  (2023-10-16T14:19:40Z)
• Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning [151.03738099494765]
  We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. We propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model.
  arXiv  Detail & Related papers  (2023-02-24T17:16:27Z)
• Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning [85.86440477005523]
  We study more human-like RL agents which incorporate an established model of human-irrationality, the Rational Inattention (RI) model. RIRL models the cost of cognitive information processing using mutual information. We show that using RIRL yields a rich spectrum of new equilibrium behaviors that differ from those found under rational assumptions.
  arXiv  Detail & Related papers  (2022-01-18T20:54:00Z)
• Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning [72.23843557783533]
  We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types. Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
  arXiv  Detail & Related papers  (2022-01-03T17:00:17Z)
• Automated Machine Learning, Bounded Rationality, and Rational Metareasoning [62.997667081978825]
  We will look at automated machine learning (AutoML) and related problems from the perspective of bounded rationality. Taking actions under bounded resources requires an agent to reflect on how to use these resources in an optimal way.
  arXiv  Detail & Related papers  (2021-09-10T09:10:20Z)
• ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations [110.72725220033983]
  Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps. ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations. In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
  arXiv  Detail & Related papers  (2021-06-10T04:32:20Z)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.