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A computational approach to modeling commodity markets

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Author Info

  • Karla Atkins

    ()

  • Achla Marathe

    ()

  • Chris Barrett

    ()

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 b

(This abstract was borrowed from another version of this item.)

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File URL: http://hdl.handle.net/10.1007/s10614-007-9090-6
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 30 (2007)
Issue (Month): 2 (September)
Pages: 125-142

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Handle: RePEc:kap:compec:v:30:y:2007:i:2:p:125-142

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: Computational framework; Agent based; Bilateral market; Matching order;

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References

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  1. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70, pages 111.
  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. Bettina Klaus & Flip Klijn, 2006. "Procedurally fair and stable matching," Economic Theory, Springer, vol. 27(2), pages 431-447, January.
  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. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Moulin Hervé & Anna Bogomolnaia, 2001. "Random Matching and assignment under dichotomous preferences," Economics Bulletin, AccessEcon, vol. 28(19), pages A0.
  10. Falk, Armin & Fehr, Ernst, 2003. "Why labour market experiments?," Labour Economics, Elsevier, vol. 10(4), pages 399-406, August.
  11. 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.
  12. 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.
  13. 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.
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Citations

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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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