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Learning competitive pricing strategies by multi-agent reinforcement learning

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  • Kutschinski, Erich
  • Uthmann, Thomas
  • Polani, Daniel
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    Abstract

    In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with different levels of competition. Q-learning manages to learn best-reply strategies well, but is expensive to train.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

    Volume (Year): 27 (2003)
    Issue (Month): 11 ()
    Pages: 2207-2218

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    Handle: RePEc:eee:dyncon:v:27:y:2003:i:11:p:2207-2218

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    Web page: http://www.elsevier.com/locate/jedc

    Related research

    Keywords: Distributed simulation; Agent-based computational economics; Dynamic pricing; Multi-agent reinforcement learning; Q-learning;

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    Cited by:
    1. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.
    2. Tharakunnel, Kurian & Bhattacharyya, Siddhartha, 2009. "Single-leader-multiple-follower games with boundedly rational agents," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1593-1603, August.
    3. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
    4. Leonardo Bargigli & Gabriele Tedeschi, 2013. "Major trends in agent-based economics," Journal of Economic Interaction and Coordination, Springer, vol. 8(2), pages 211-217, October.
    5. Tong Zhang & B. Brorsen, 2009. "Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets," Computational Economics, Society for Computational Economics, vol. 34(4), pages 399-417, November.
    6. Tong Zhang & B. Brorsen, 2011. "Oligopoly firms with quantity-price strategic decisions," Journal of Economic Interaction and Coordination, Springer, vol. 6(2), pages 157-170, November.
    7. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Society for Computational Economics, vol. 32(1), pages 147-162, September.

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