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Reinforcement Learning in Market Games


  • Edward W. Piotrowski


  • Jan Sladkowski


  • Anna Szczypinska



Financial markets investors are involved in many games - they must interact with other agents to achieve their goals. Among them are those directly connected with their activity on markets but one cannot neglect other aspects that influence human decisions and their performance as investors. Distinguishing all subgames is usually beyond hope and resource consuming. In this paper we study how investors facing many different games, gather information and form their decision despite being unaware of the complete structure of the game. To this end we apply reinforcement learning methods to the Information Theory Model of Markets (ITMM). Following Mengel, we can try to distinguish a class $\Gamma$ of games and possible actions (strategies) $a^{i}_{m_{i}}$ for i-th agent. Any agent divides the whole class of games into analogy subclasses she/he thinks are analogous and therefore adopts the same strategy for a given subclass. The criteria for partitioning are based on profit and costs analysis. The analogy classes and strategies are updated at various stages through the process of learning. This line of research can be continued in various directions.

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  • Edward W. Piotrowski & Jan Sladkowski & Anna Szczypinska, "undated". "Reinforcement Learning in Market Games," Departmental Working Papers 30, University of Bialtystok, Department of Theoretical Physics.
  • Handle: RePEc:sla:eakjkl:30

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    References listed on IDEAS

    1. Piotrowski, Edward W & Sładkowski, Jan, 2004. "Arbitrage risk induced by transaction costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 331(1), pages 233-239.
    2. Katarzyna Miakisz & Edward W. Piotrowski & Jan Sladkowski, "undated". "Quantization of Games: Towards Quantum Artificial Intelligence," Departmental Working Papers 21, University of Bialtystok, Department of Theoretical Physics.
    3. Piotrowski, Edward W., 2003. "Fixed point theorem for simple quantum strategies in quantum market games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 196-200.
    4. Mengel, Friederike, 2012. "Learning across games," Games and Economic Behavior, Elsevier, vol. 74(2), pages 601-619.
    5. Shneyerov, Art & Wong, Adam Chi Leung, 2007. "The Rate of Convergence to Perfect Competition of a Simple Matching and Bargaining Mechanism," working papers shneyerov-07-05-01-03-43-, Vancouver School of Economics, revised 01 May 2007.
    6. E. W. Piotrowski & M. Schroeder, 2007. "Kelly criterion revisited: optimal bets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(2), pages 201-203, May.
    7. Edward W. Piotrowski & Jan Sladkowski, "undated". "Quantum Computer: An Appliance for Playing Market Games," Departmental Working Papers 16, University of Bialtystok, Department of Theoretical Physics.
    8. Edward W. Piotrowski & Malgorzata Schroeder, "undated". "Kelly Criterion Revisited: Optimal Bets," Departmental Working Papers 24, University of Bialtystok, Department of Theoretical Physics.
    9. Edward W. Piotrowski & Jerzy Luczka, "undated". "The relativistic velocity addition law optimizes a forecast gambler's profit," Departmental Working Papers 31, University of Bialtystok, Department of Theoretical Physics.
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