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Competitive Pricing Using Model-Based Bandits.

Lukasz Sliwinski1, Tanut Treetanthiploet2,3, David Siska1

  • 1Maxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD UK.

Computational Economics
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a new pricing environment for learning algorithms. Modeling the environment improves performance in stationary settings but not in competitive, non-stationary markets.

Area of Science:

  • Machine Learning
  • Market Dynamics
  • Algorithmic Game Theory

Background:

  • Learning algorithms are increasingly used for automatic price adjustments.
  • Standard algorithms assume uncorrelated and stationary reward distributions, which is unrealistic in competitive markets.
  • This assumption limitation hinders effective price adjustment strategies in dynamic environments.

Purpose of the Study:

  • To introduce a novel pricing environment that better reflects market realities.
  • To investigate the conditions for a unique Nash equilibrium in this environment.
  • To develop and evaluate bandit algorithms for price adjustments in both stationary and non-stationary competitive markets.

Main Methods:

  • Introduction of a new pricing environment model.
Keywords:
Algorithmic pricingMulti-armed banditsPricing game

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  • Analysis of conditions for Nash equilibrium existence.
  • Development of a bandit algorithm incorporating environmental structure.
  • Extension of the algorithm for non-stationary settings.
  • Numerical testing in stationary and competitive pricing scenarios.

    • Main Results:

    • The proposed pricing environment allows for the verification of unique Nash equilibrium conditions.
    • A bandit algorithm was developed that approximates the pricing environment's structure.
    • Modeling the stationary environment enhanced algorithm performance in stationary settings.
    • However, this structural modeling did not provide benefits in non-stationary competitive pricing scenarios.

    Conclusions:

    • Incorporating environmental structure into learning algorithms can improve performance in stationary markets.
    • The benefits of modeling environmental structure do not extend to non-stationary competitive pricing environments.
    • Further research is needed for adaptive learning algorithms in dynamic market competitions.