Computational Economics

, Volume 30, Issue 2, pp 125–142 | Cite as

A computational approach to modeling commodity markets



We build an agent based computational framework to study large commodity markets. A detailed representation of the consumers, producers and the market is used to study the micro level behavior of the market and its participants. The user can control players’ preferences, their strategies, assumptions of the model, its initial conditions, market elements and trading mechanisms. The first part of the paper describes the computational framework and its three main modules. The later part describes a case study that examines the decentralized market in detail, specifically the computational 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 and social welfare, to different matching schemes in a bilateral computational setting. Based on seven different matching orders for the buyers and suppliers, our study shows that the results can vary dramatically for different pairing orders.


Computational framework Agent based Bilateral market Matching order 


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  1. Arciniegas I., Barrett C., Marathe A. (2003). Assessing the efficiency of US electricity markets. Utilities Policy 11(2): 75–86CrossRefGoogle Scholar
  2. Arciniegas I., Marathe A. (2005). Important variables in explaining real-time peak price in the independent power market of ontario. Utilities Policy 3(1): 27–39CrossRefGoogle Scholar
  3. Atkins, K., Barrett, C., Homan, C., Marathe, A., Marathe, M., & Thite, S. (2004a). Marketecture: A simulation-based framework for studying experimental deregulated power markets. Los Alamos Technical Report, LAUR 04-2032.Google Scholar
  4. Atkins, K., Barrett, C., Homan, C., Marathe, A., Marathe, M., & S. Thite. (2004b). Agent based economic analysis of deregulated electricity markets. 6th IAEE European Conference, Zurich, Switzerland.Google Scholar
  5. Atkins, K., Homan, C., & Marathe, A. (2004c). Physical clearing mechanisms in power industry. To be presented at the IEEE Power Systems Conference and Exposition, New York City, New York.Google Scholar
  6. Barrett, C. L., Beckman, R. J., Berkbigler, K . P., Bisset, K. R., Bush, B. W., Eubank, S., Hurford, J. M., Konjevod, G., Kubicek, D. A., Marathe, M. V., Morgeson, J. D., Rickert, M., Romero, P. R., Smith, L. L., Speckman, M. P., Speckman, P. L., Stretz, P. E., Thayer, G. L., & Williams, M. D. (1999) TRANSIMS (TRansportation ANalysis SIMulation System):, Overview, LA-UR-99-1658, Los Alamos National Laboratory.Google Scholar
  7. Barrett C., Cook D., Faber V., Hicks G., Marathe A., Marathe M., Srinivasan A., Sussmann Y.J., Thornquist H. (2003). Experimental analysis of algorithms for bilateral-contract clearing mechanisms arising in deregulated power industry. Journal of Graph Algorithms and Applications 7(1): 3–31Google Scholar
  8. Bogomolnaia A., Moulin H. (2004). Random matching and assignment under dichotomous preferences. Econometrica 72(1): 257–279CrossRefGoogle Scholar
  9. Bunn D., Oliveira F.S. (2001). Agent-based simulation - an application to the new electricity trading arrangements of England and Wales. IEEE Transactions on Evolutionary Computation 5(5): 493–503CrossRefGoogle Scholar
  10. Falk A., Fehr E. (2003). Why labour market experiments?. Labour Economics 10: 399–406CrossRefGoogle Scholar
  11. Huberman B.A., Glance N.S. (1993). Evolutionary games and computer simulations. Proceedings of National Academy Science 90: 7716–7718CrossRefGoogle Scholar
  12. Huberman, B.A., Hogg, T. (1995). Distributed computation as an economic system. Journal of Economic Perspectives, 141–152Google Scholar
  13. Judd, K. (2005) Computationally intensive analysis in economics. Handbook of computational economics, (Vol. 2). North Amsterdam.Google Scholar
  14. Klaus B., Klijn F. (2006). Procedurally fair and stable matching. Economic Theory 27(2): 431–447CrossRefGoogle Scholar
  15. Lin, M. W. (1998). A Model and simulation of competitive electric power systems, Ph.D Dissertation, The University of Texas at Austin. Scholar
  16. Li C., Giampapa J.A., Sycara K. (2006). Bilateral negotiation decisions with uncertain dynamic outside options. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Special Issue on Game-theoretic Analysis and Stochastic Simulation of Negotiation Agents 36(1): 31–44Google Scholar
  17. MacKie-Mason, J. J., & Wellman, M. (2005). Automated markets and trading agents. Handbook of computational economics, (Vol. 2). North-Holland.Google Scholar
  18. Miller M.S., Drexler K.E. (1988). Comparative ecology: a computational perspective. In Huberman B. (eds) The Ecology of Computation. Amsterdam, Elsevier Science PublishersGoogle Scholar
  19. Mozumder P, Marathe A. (2004). Implications of an integrated market for tradable renewable energy contracts. Ecological Economics 49(3): 259–272CrossRefGoogle Scholar
  20. Ramey, G., & Watson, J. (2001). Bilateral trade and opportunism in a matching market. Contributions to Theoretical Economics, 1(1), Article 3.Google Scholar
  21. Roth A. E. (1999). Game theory as a tool for market design. Scholar
  22. Roth A. E. (2005). Matching and allocation in medicine and health care. Building a better delivery system: A new engineering/health care partnership, National Academy of Engineering and Institute of Medicine, National Academies Press, pp. 237–239.Google Scholar
  23. Roth A.E., Vande V., John H. (1990). Random paths to stability in two-sided matching. Econometrica 58(6): 1475–1480CrossRefGoogle Scholar
  24. Rust J., Miller J.H., Palmer R. (1994). Characterizing effective trading strategies: Insights from a computerized double auction tournament. Journal of Economic Dynamics and Control 18: 61–96CrossRefGoogle Scholar
  25. Sargent T.J. (1993). Bounded rationality in macroeconomics. The Arne Ryde memorial lectures. Oxford U.K., Clarendon PressGoogle Scholar
  26. Smith V.L. (1962). An experimental study of competitive market behavior. The Journal of Political Economy 70(2): 111–137CrossRefGoogle Scholar
  27. Spulber D.F. (2002). Market microstructure and incentives to invest. Journal of Political Economy 110(2): 352–381CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Virginia Bioinformatics InstituteVirginia TechBlacksburgUSA

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