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Q-Learning algorithms in a Hotelling model

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  • Lucila Porto

Abstract

What if Q-Learning algorithms set not only prices but also the degree of differentiation between them? In this paper, I tackle this question by analyzing the competition between two Q-Learning algorithms in a Hotelling setting. I find that most of the simulations converge to a Nash Equilibrium where the algorithms are playing non-competitive strategies. In most simulations, they optimally learn not to differentiate each other and to set a collusive price. An underlying deviation and punishment scheme sustains this implicit agreement. The results are robust to the enlargement of the action space and the introduction of relocalization costs.

Suggested Citation

  • Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4587
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    References listed on IDEAS

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    1. Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," CEPR Discussion Papers 15738, C.E.P.R. Discussion Papers.
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    5. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
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    8. Joseph E Harrington, 2018. "Developing Competition Law For Collusion By Autonomous Artificial Agents," Journal of Competition Law and Economics, Oxford University Press, vol. 14(3), pages 331-363.
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    More about this item

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L4 - Industrial Organization - - Antitrust Issues and Policies

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