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Convergence of outcomes and evolution of strategic behavior in double auctions

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  • Shira Fano
  • Marco LiCalzi
  • Paolo Pellizzari

Abstract

We study the emergence of strategic behavior in double auctions with an equal number of buyers and sellers, under the distinct assumptions that orders are cleared simultaneously or asynchronously. The evolution of strategic behavior is modeled as a learning process driven by a genetic algorithm. We find that, as the size of the market grows, allocative inefficiency tends to zero and performance converges to the competitive outcome, regardless of the order-clearing rule. The main result concerns the evolution of strategic behavior as the size of the market gets larger. Under simultaneous order-clearing, only marginal traders learn to be price takers and make offers equal to their valuations/costs. Under asynchronous order-clearing, all intramarginal traders learn to be price makers and make offers equal to the competitive equilibrium price. The nature of the order-clearing rule affects in a fundamental way what kind of strategic behavior we should expect to emerge. Copyright Springer-Verlag 2013

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  • Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 513-538, July.
    • Handle: RePEc:spr:joevec:v:23:y:2013:i:3:p:513-538
    DOI: 10.1007/s00191-011-0226-4
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    1. Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 513-538, July.
    2. Michiel Leur & Mikhail Anufriev, 2018. "Timing under individual evolutionary learning in a continuous double auction," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 609-631, August.
    3. Florian Hauser & Marco LiCalzi, 2011. "Learning to Trade in an Unbalanced Market," Lecture Notes in Economics and Mathematical Systems, in: Sjoukje Osinga & Gert Jan Hofstede & Tim Verwaart (ed.), Emergent Results of Artificial Economics, pages 65-76, Springer.
    4. Olga A. Rud & Jean Paul Rabanal, 2018. "Evolution of markets: a simulation with centralized, decentralized and posted offer formats," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 667-689, August.
    5. Shira Fano & Paolo Pellizzari, 2011. "Time-Dependent Trading Strategies in a Continuous Double Auction," Lecture Notes in Economics and Mathematical Systems, in: Sjoukje Osinga & Gert Jan Hofstede & Tim Verwaart (ed.), Emergent Results of Artificial Economics, pages 165-176, Springer.
    6. Paolo Pellizzari, 2011. "Optimal trading in a limit order book using linear strategies," Working Papers 2011_16, Department of Economics, University of Venice "Ca' Foscari", revised Sep 2011.
    7. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    8. Jakob Grazzini, 2013. "Information dissemination in an experimentally based agent-based stock market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 179-209, April.
    9. Giulio Bottazzi & Pietro Dindo, 2013. "Evolution and market behavior in economics and finance: introduction to the special issue," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 507-512, July.
    10. Anufriev, Mikhail & Arifovic, Jasmina & Ledyard, John & Panchenko, Valentyn, 2022. "The role of information in a continuous double auction: An experiment and learning model," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    11. Ruijgrok, Matthijs, 2012. "A single-item continuous double auction game," MPRA Paper 42086, University Library of Munich, Germany.
    12. Jean Paul Rabanal & Olga A. Rabanal, 2015. "A Simulation on the Evolution of Markets: Call Market, Decentralized and Posted Offer," Working Papers 34, Peruvian Economic Association.

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    More about this item

    Keywords

    Trading protocols; Asymptotic equivalence; Learning; Genetic algorithms; D44; D82; C63; C72;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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