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Automated Markets and Trading Agents

In: Handbook of Computational Economics

Author

Listed:
  • MacKie-Mason, Jeffrey K.
  • Wellman, Michael P.

Abstract

Computer automation has the potential, just starting to be realized, of transforming the design and operation of markets, and the behaviors of agents trading in them. We discuss the possibilities for automating markets, presenting a broad conceptual framework covering resource allocation as well as enabling marketplace services such as search and transaction execution. One of the most intriguing opportunities is provided by markets implementing computationally sophisticated negotiation mechanisms, for example combinatorial auctions. An important theme that emerges from the literature is the centrality of design decisions about matching the domain of goods over which a mechanism operates to the domain over which agents have preferences. When the match is imperfect (as is almost inevitable), the market game induced by the mechanism is analytically intractable, and the literature provides an incomplete characterization of rational bidding policies. A review of the literature suggests that much of our existing knowledge comes from computational simulations, including controlled studies of abstract market designs (e.g., simultaneous ascending auctions), and research tournaments comparing agent strategies in a variety of market scenarios. An empirical game-theoretic methodology combines the advantages of simulation, agent-based modeling, and statistical and game-theoretic analysis.

Suggested Citation

  • MacKie-Mason, Jeffrey K. & Wellman, Michael P., 2006. "Automated Markets and Trading Agents," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 28, pages 1381-1431, Elsevier.
  • Handle: RePEc:eee:hecchp:2-28
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    Cited by:

    1. Stößer, Jochen & Neumann, Dirk & Weinhardt, Christof, 2010. "Market-based pricing in grids: On strategic manipulation and computational cost," European Journal of Operational Research, Elsevier, vol. 203(2), pages 464-475, June.
    2. Karla Atkins & Achla Marathe & Chris Barrett, 2007. "A computational approach to modeling commodity markets," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 125-142, September.
    3. 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.
    4. Chang, Myong-Hun & Harrington, Joseph Jr., 2006. "Agent-Based Models of Organizations," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 26, pages 1273-1337, Elsevier.
    5. Block, C. & Collins, J. & Ketter, W. & Weinhardt, C., 2009. "A Multi-Agent Energy Trading Competition," ERIM Report Series Research in Management ERS-2009-054-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    7. Olivier Armantier & Jean-Pierre Florens & Jean-Francois Richard, 2008. "Approximation of Nash equilibria in Bayesian games," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(7), pages 965-981.
    8. repec:gam:jeners:v:12:y:2019:i:3:p:428-:d:201811 is not listed on IDEAS

    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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