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Allocative efficiency and traders' protection under zero intelligence behavior

Author

Listed:
  • Marco LiCalzi

    (Department of Applied Mathematics, University of Venice)

  • Lucia Milone

    (Advanced School of Economics, University of Venice)

  • Paolo Pellizzari

    (Department of Applied Mathematics, University of Venice)

Abstract

This paper studies the continuous double auction from the point of view of market engineering: we tweak a resampling rule often used for this exchange protocol and search for an improved design. We assume zero intelligence trading as a lower bound for more robust behavioral rules and look at allocative efficiency, as well as three subordinate performance criteria: mean spread, cancellation rate, and traders' protection. This latter notion measures the ability of a protocol to help traders capture their share of the competitive equilibrium profits. We consider two families of resampling rules and obtain the following results. Full resampling is not necessary to attain high allocative efficiency, but fine-tuning the resampling rate is important. The best allocative performances are similar across the two families. However, if the market designer adds any of the other three criteria as a subordinate goal, then a resampling rule based on a price band around the best quotes is superior.

Suggested Citation

  • Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2008. "Allocative efficiency and traders' protection under zero intelligence behavior," Working Papers 168, Department of Applied Mathematics, Università Ca' Foscari Venezia, revised Nov 2009.
    • Handle: RePEc:vnm:wpaper:168
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    References listed on IDEAS

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    1. 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.
    2. Mirowski, Philip, 2007. "Markets come to bits: Evolution, computation and markomata in economic science," Journal of Economic Behavior & Organization, Elsevier, vol. 63(2), pages 209-242, June.
    3. Sunder, S., 1992. "Lower Bounds for Efficiency of Surplus Extraction in Double Auctions," GSIA Working Papers 1992-17, Carnegie Mellon University, Tepper School of Business.
    4. 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.
    5. Smith, Vernon L, 1982. "Microeconomic Systems as an Experimental Science," American Economic Review, American Economic Association, vol. 72(5), pages 923-955, December.
    6. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 603-630.
    7. Gode, Dhananjay (Dan) K. & Sunder, Shyam, 2004. "Double auction dynamics: structural effects of non-binding price controls," Journal of Economic Dynamics and Control, Elsevier, vol. 28(9), pages 1707-1731, July.
    8. 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.
    9. Steven Gjerstad & Jason M. Shachat, 2007. "Individual Rationality and Market Efficiency," Purdue University Economics Working Papers 1204, Purdue University, Department of Economics.
    10. Charlotte Bruun (ed.), 2006. "Advances in Artificial Economics," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-37249-3, December.
    11. Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
    12. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    13. Zhan, Wenjie & Friedman, Daniel, 2007. "Markups in double auction markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 2984-3005, September.
    14. 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.
    15. 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.
    16. Alvin E. Roth, 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, Econometric Society, vol. 70(4), pages 1341-1378, July.
    17. Gul, Faruk & Lundholm, Russell, 1995. "Endogenous Timing and the Clustering of Agents' Decisions," Journal of Political Economy, University of Chicago Press, vol. 103(5), pages 1039-1066, October.
    18. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    19. 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.
    20. Maslov, Sergei, 2000. "Simple model of a limit order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(3), pages 571-578.
    21. Crockett, Sean & Spear, Stephen & Sunder, Shyam, 2008. "Learning competitive equilibrium," Journal of Mathematical Economics, Elsevier, vol. 44(7-8), pages 651-671, July.
    22. 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. Roberto Cervone & Stefano Galavotti & Marco LiCalzi, 2009. "Symmetric Equilibria in Double Auctions with Markdown Buyers and Markup Sellers," Lecture Notes in Economics and Mathematical Systems, in: Cesáreo Hernández & Marta Posada & Adolfo López-Paredes (ed.), Artificial Economics, chapter 0, pages 81-92, Springer.

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    More about this item

    Keywords

    market engineering; trading protocols; competitive share; exchange market;
    All these keywords.

    JEL classification:

    • D51 - Microeconomics - - General Equilibrium and Disequilibrium - - - Exchange and Production Economies
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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