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Competitive Pricing Using Model-Based Bandits

Author

Listed:
  • Lukasz Sliwinski

    (University of Edinburgh, Maxwell Institute for Mathematical Sciences, School of Mathematics)

  • Tanut Treetanthiploet

    (Naresuan University, The Institute for Fundamental Study
    Quantum Technology Foundation (Thailand))

  • David Siska

    (University of Edinburgh, Maxwell Institute for Mathematical Sciences, School of Mathematics)

  • Lukasz Szpruch

    (University of Edinburgh, Maxwell Institute for Mathematical Sciences, School of Mathematics
    Alan Turing Institute)

Abstract

The use of learning algorithms for automatic price adjustments in markets is on the rise. However, these algorithms often assume that reward distributions for actions are uncorrelated and stationary, a condition that does not hold in competitive pricing environments. In this paper, we introduce a pricing environment, find conditions under which a unique Nash equilibrium exists and verify the assumptions numerically. Then, we propose a bandit algorithm that approximates the structure of the environment and extend it to accommodate non-stationary settings. We perform numerical tests in both stationary and competitive pricing environments, analysing the potential benefits and drawbacks of incorporating the structure of the environment within learning algorithms. While modelling the stationary environment improves the algorithm’s performance in a stationary setting, it does not offer an advantage in pricing competitions between non-stationary learning agents.

Suggested Citation

  • Lukasz Sliwinski & Tanut Treetanthiploet & David Siska & Lukasz Szpruch, 2025. "Competitive Pricing Using Model-Based Bandits," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 4813-4867, December.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:6:d:10.1007_s10614-024-10816-w
    DOI: 10.1007/s10614-024-10816-w
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    References listed on IDEAS

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    1. Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
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