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A Computational Approach to Modeling Commodity Markets

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Author Info

  • Karla Atkins

    (Virginia Tech)

  • Achla Marathe

    (Virginia Tech)

  • Chris Barrett

    (Virginia Tech)

Abstract

We apply agent based computer techniques to develop a modeling framework that can be used to study all aspects of commodity markets. The goal is to use advances in computation to study the micro level behavior of the market and its players. This work is motivated by the understanding that the non-equilibrium dynamics of the transitioning markets can best be analyzed through an experimental framework. The experimental framework makes it possible to observe not only the equilibrium but also the disequilibrium and transition to the equilibrium. The transient dynamics that lead to the equilibrium can sometimes provide the most insightful observations and result in innovative discoveries and explanations. Such transients cannot be studied through traditional closed form economic models. Our framework provides users the ability to control individuals' preferences, behavior, market elements, initial conditions, trading mechanisms along with various other parameters. This facilitates the study of different economic structures, institutions, policies and strategies in isolation. A detailed representation of the consumers, producers and the market is used to study the micro level behavior of the market and its participants. We first describe the computational framework and its main modules. The later part describes a case study that examines the decentralized market in detail; specifically the options available for matching the buyers and suppliers in a synthetic market. The study illustrates the sensitivity of the outcome of various economic variables, such as clearing price, quantity, profits, social welfare, to different schemes for matching buyers and suppliers in a computational setting. Our results, based on seven different matching orders show that the results can vary dramatically for different pairing orders. This variation is found to be even higher for markets with a larger number of participants. This result has important implications for computational modeling based analysis. The user needs to be cognizant of the sensitivity of the ordering scheme and hence should use statistical methods for ensuring the robustness of the results.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 240.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:240

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Keywords: Computational Markets; Decentralized; Matching Orders;

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References

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  1. 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.
  2. Arciniegas, Ismael & Barrett, Chris & Marathe, Achla, 2003. "Assessing the efficiency of US electricity markets," Utilities Policy, Elsevier, vol. 11(2), pages 75-86, June.
  3. Moulin Hervé & Anna Bogomolnaia, 2001. "Random Matching and assignment under dichotomous preferences," Economics Bulletin, AccessEcon, vol. 28(19), pages A0.
  4. Daniel F. Spulber, 2002. "Market Microstructure and Incentives to Invest," Journal of Political Economy, University of Chicago Press, vol. 110(2), pages 352-381, April.
  5. Falk, Armin & Fehr, Ernst, 2003. "Why labour market experiments?," Labour Economics, Elsevier, vol. 10(4), pages 399-406, August.
  6. Bettina Klaus & Flip Klijn, 2003. "Procedurally Fair and Stable Matching," UFAE and IAE Working Papers 582.03, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  7. Arciniegas Rueda, Ismael E. & Marathe, Achla, 2005. "Important variables in explaining real-time peak price in the independent power market of Ontario," Utilities Policy, Elsevier, vol. 13(1), pages 27-39, March.
  8. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70, pages 322.
  9. Ramey Garey & Watson Joel, 2001. "Bilateral Trade and Opportunism in a Matching Market," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 1(1), pages 1-35, November.
  10. Bernardo A. Huberman & Tad Hogg, 1995. "Distributed Computation as an Economic System," Journal of Economic Perspectives, American Economic Association, vol. 9(1), pages 141-152, Winter.
  11. Rust, John & Miller, John H. & Palmer, Richard, 1994. "Characterizing effective trading strategies : Insights from a computerized double auction tournament," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 61-96, January.
  12. Mozumder, Pallab & Marathe, Achla, 2004. "Gains from an integrated market for tradable renewable energy credits," Ecological Economics, Elsevier, vol. 49(3), pages 259-272, July.
  13. Roth, Alvin E & Vande Vate, John H, 1990. "Random Paths to Stability in Two-Sided Matching," Econometrica, Econometric Society, vol. 58(6), pages 1475-80, November.
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Cited by:
  1. Dufresne, Daniel & Vázquez-Abad, Felisa, 2012. "Cobweb theorems with production lags and price forecasting," Economics Discussion Papers 2012-17, Kiel Institute for the World Economy.
  2. Gabriel Galand, 2009. "The Neutrality of Money Revisited with a Bottom-Up Approach: Decentralisation, Limited Information and Bounded Rationality," Computational Economics, Society for Computational Economics, vol. 33(4), pages 337-360, May.

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