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Zero-Intelligence Trading Without Resampling

In: Complexity and Artificial Markets

Author

Listed:
  • Marco LiCalzi

    (Università Ca’ Foscari di Venezia)

  • Paolo Pellizzari

    (Università Ca’ Foscari di Venezia)

Abstract

This paper studies the consequences of removing the resampling assumption from the zero-intelligence trading model in Gode and Sunder (1993). We obtain three results. First, individual rationality is no longer sufficient to attain allocative efficiency in a continuous double auction; hence, the rules of the market matter. Second, the allocative efficiency of the continuous double auction is higher than for other sequential protocols both with or without resampling. Third, compared to zero intelligence, the effect of learning on allocative efficiency is sharply positive without resampling and mildly negative with resampling.

Suggested Citation

  • Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading Without Resampling," Lecture Notes in Economics and Mathematical Systems, in: Klaus Schredelseker & Florian Hauser (ed.), Complexity and Artificial Markets, chapter 1, pages 3-14, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-70556-7_1
    DOI: 10.1007/978-3-540-70556-7_1
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    References listed on IDEAS

    as
    1. LiCalzi, Marco & Pellizzari, Paolo, 2007. "Simple market protocols for efficient risk sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3568-3590, November.
    2. Hurwicz, Leonid & Radner, Roy & Reiter, Stanley, 1975. "A Stochastic Decentralized Resource Allocation Process: Part I," Econometrica, Econometric Society, vol. 43(2), pages 187-221, March.
    3. Gjerstad, Steven & Dickhaut, John, 1998. "Price Formation in Double Auctions," Games and Economic Behavior, Elsevier, vol. 22(1), pages 1-29, January.
    4. Steven Gjerstad & Jason M. Shachat, 2007. "Individual Rationality and Market Efficiency," Purdue University Economics Working Papers 1204, Purdue University, Department of Economics.
    5. Charlotte Bruun (ed.), 2006. "Advances in Artificial Economics," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-37249-3, October.
    6. Marco LiCalzi & Paolo Pellizzari, 2007. "Which Market Protocols Facilitate Fair Trading?," Lecture Notes in Economics and Mathematical Systems, in: Andrea Consiglio (ed.), Artificial Markets Modeling, chapter 6, pages 81-97, Springer.
    7. Marco LiCalzi & Paolo Pellizzari, 2006. "The Allocative Effectiveness of Market Protocols Under Intelligent Trading," Lecture Notes in Economics and Mathematical Systems, in: Charlotte Bruun (ed.), Advances in Artificial Economics, chapter 2, pages 17-29, Springer.
    8. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    9. Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer;Economic Science Association, vol. 5(3), pages 179-208, December.
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    Cited by:

    1. 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.
    2. Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2011. "Allocative Efficiency and Traders’ Protection Under Zero Intelligence Behavior," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 5-28, Springer.
    3. 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.
    4. Annalisa Fabretti & Tommy Gärling & Stefano Herzel & Martin Holmen, 2017. "Convex incentives in financial markets: an agent-based analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 375-395, November.
    5. Jakob Grazzini, 2012. "Analysis of the Emergent Properties: Stationarity and Ergodicity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-7.
    6. 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).

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    More about this item

    Keywords

    Equilibrium Price; Allocative Efficiency; Transaction Price; Market Discipline; Double Auction;
    All these keywords.

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D51 - Microeconomics - - General Equilibrium and Disequilibrium - - - Exchange and Production Economies
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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