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Strategic bidding of offer curves: An agent-based approach to exploring supply curve equilibria

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  • Kimbrough, Steven O.
  • Murphy, Frederic H.

Abstract

We model a market in which suppliers bid step-function offer curves using agent-based modeling. Our model is an abstraction of electricity markets where step-function offer curves are given to an independent system operator that manages the auctions in electricity markets. Positing an elementary and computationally accessible learning model, Probe and Adjust, we present analytic results that characterize both the behavior of the learning model and the properties of step-function equilibria. Thus, we have developed a framework for validating agent-based models prior to using them in situations that are too complicated to be analyzed using traditional economic theory. In addition, we demonstrate computationally that, by using alternative policies, even simple agents can achieve monopoly rewards for themselves by pursuing more industry-oriented strategies. This raises the issue of how participants in oligopolistic markets actually behave.

Suggested Citation

  • Kimbrough, Steven O. & Murphy, Frederic H., 2013. "Strategic bidding of offer curves: An agent-based approach to exploring supply curve equilibria," European Journal of Operational Research, Elsevier, vol. 229(1), pages 165-178.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:1:p:165-178
    DOI: 10.1016/j.ejor.2013.02.006
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    Cited by:

    1. Oliveira, Fernando S. & Costa, Manuel L.G., 2018. "Capacity expansion under uncertainty in an oligopoly using indirect reinforcement-learning," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1039-1050.
    2. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2021. "Multi-unit multiple bid auctions in balancing markets: An agent-based Q-learning approach," Energy Economics, Elsevier, vol. 93(C).
    3. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2018. "Multi-unit multiple bid auctions in balancing markets: an agent-based Q-learning approach," EWI Working Papers 2018-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    4. James Fain, 2023. "Should retail stores locate close to a rival?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(1), pages 129-162, January.
    5. Mestre, Guillermo & Sánchez-Úbeda, Eugenio F. & Muñoz San Roque, Antonio & Alonso, Estrella, 2022. "The arithmetic of stepwise offer curves," Energy, Elsevier, vol. 239(PE).
    6. Liu, Yun & Onggo, Bhakti Stephan & Busby, Jerry, 2024. "Agent-based modelling of user engagement in new product development," Technovation, Elsevier, vol. 135(C).
    7. Marco Catola & Silvia Leoni, 2023. "Pollution Abatement and Lobbying in a Cournot Game. An Agent-Based Modelling approach," Discussion Papers 2023/294, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.

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