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The Ultimatum Game as a Paradigm for Learning Agents: A Python Adventure

In: Machine Learning Perspectives of Agent-Based Models

Author

Listed:
  • Joaquim Margarido

    (ISEP)

  • Pedro Campos

    (University of Porto, FEP, LIAAD-INESC TEC)

Abstract

This chapter explores the application of Agent-Based Models (ABMs) in understanding economic crises and pandemics, employing a machine learning approach for description and prediction. Emphasizing Game Theory’s importance in ABMs, the Ultimatum game serves as a paradigm for learning agents. The game involves a proposer and responder, deciding on a split, with consequences based on acceptance or refusal. The implementation, conducted step by step in Python, initially establishes a baseline model with proposers using fair or unfair split strategies. Subsequently, two learning strategies, Fictitious Play and Reinforcement Learning, are introduced. Fictitious Play minimizes responder rejections, while Reinforcement Learning optimizes action policies through sequential decision processes. In the model, a crisis is triggered halfway through the defined iterations, that entails responders increasing their acceptance threshold by 50%, demonstrating the synergy between ABMs, Game Theory, and machine learning in economic modeling.

Suggested Citation

  • Joaquim Margarido & Pedro Campos, 2025. "The Ultimatum Game as a Paradigm for Learning Agents: A Python Adventure," Springer Books, in: Pedro Campos & Anand Rao & Joaquim Margarido (ed.), Machine Learning Perspectives of Agent-Based Models, chapter 0, pages 147-188, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-73354-3_7
    DOI: 10.1007/978-3-031-73354-3_7
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