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Modeling Implicit Collusion Using Coevolution

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  • E. J. Anderson

    (Faculty of Economics and Business, University of Sydney, Sydney, New South Wales 2006, Australia)

  • T. D. H. Cau

    (Australian School of Business, University of New South Wales, Sydney, New South Wales 2052, Australia)

Abstract

Many oligopolies operate as a repeated game. In such circumstances, it can be expected that profit-maximising participants may engage in implicit collusion to profitably increase spot market prices. This paper models the emergence of such implicit collusion in a stylised market model using a coevolutionary approach. Players bid supply functions made up of a finite number of linear pieces. Each player uses a genetic algorithm to find state-based strategies depending on the price and demand in the last period and the predicted demand in the next period. We consider a symmetric duopoly and demonstrate that collusive behaviour can be learned even when there is very limited information available to the participants. Moreover, we show a type of implicit collusive behaviour that occurs even though the system does not settle into a stable equilibrium. We use a wholesale electricity market, in which supply function bids are typical, as a motivating example throughout this paper.

Suggested Citation

  • E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
  • Handle: RePEc:inm:oropre:v:57:y:2009:i:2:p:439-455
    DOI: 10.1287/opre.1080.0631
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    2. 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.
    3. Tong Zhang & B. Brorsen, 2011. "Oligopoly firms with quantity-price strategic decisions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(2), pages 157-170, November.
    4. Ding, Shasha & Sun, Hao & Sun, Panfei & Han, Weibin, 2022. "Dynamic outcome of coopetition duopoly with implicit collusion," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, vol. 12(1), pages 1-13, December.
    6. Wu, Jiang & Zou, Liuxin & Gong, Yeming & Chen, Mingyang, 2021. "The anti-collusion dilemma: Information sharing of the supply chain under buyback contracts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    7. Sapio, Alessandro & Spagnolo, Nicola, 2016. "Price regimes in an energy island: Tacit collusion vs. cost and network explanations," Energy Economics, Elsevier, vol. 55(C), pages 157-172.

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