An agent-based approach equipped with game theory: Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis
An agent-based approach is a numerical (computer-intensive) method to explore the complex characteristics and dynamics of microeconomics. Using the agent-based approach, this study investigates the learning speed of traders and their strategic collaboration in a dynamic market change of electricity. An example of such a market change can be found in the California electricity crisis (2000-2001). This study incorporates the concept of partial reinforcement learning into trading agents and finds that they have two learning components: learning from a dynamic market change and learning from collaboration with other traders. The learning speed of traders becomes slow when a large fluctuation occurs in the power exchange market. The learning speed depends upon the type of traders, their learning capabilities and the fluctuation of market fundamentals. The degree of collaboration among traders gradually reduces during the electricity crisis. The strategic collaboration among traders is examined by a large simulator equipped with multiple learning capabilities.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Makowski, Marek & Nakamori, Yoshiteru & Sebastian, Hans-Jurgen, 2005. "Advances in complex systems modeling," European Journal of Operational Research, Elsevier, vol. 166(3), pages 593-596, November.
- Colin F. Camerer, 1997. "Progress in Behavioral Game Theory," Journal of Economic Perspectives, American Economic Association, vol. 11(4), pages 167-188, Fall.
- Mohammadi, Hassan, 2009. "Electricity prices and fuel costs: Long-run relations and short-run dynamics," Energy Economics, Elsevier, vol. 31(3), pages 503-509, May.
- Chevillon, Guillaume & Rifflart, Christine, 2007.
"Physical Market Determinants of the Price of Crude Oil and the Market Premium,"
ESSEC Working Papers
DR 07020, ESSEC Research Center, ESSEC Business School.
- Chevillon, Guillaume & Rifflart, Christine, 2009. "Physical market determinants of the price of crude oil and the market premium," Energy Economics, Elsevier, vol. 31(4), pages 537-549, July.
- Redl, Christian & Haas, Reinhard & Huber, Claus & Böhm, Bernhard, 2009. "Price formation in electricity forward markets and the relevance of systematic forecast errors," Energy Economics, Elsevier, vol. 31(3), pages 356-364, May.
- Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
- Paul L. Joskow & Edward Kohn, 2002.
"A Quantitative Analysis of Pricing Behavior in California's Wholesale Electricity Market During Summer 2000,"
The Energy Journal,
International Association for Energy Economics, vol. 0(Number 4), pages 1-35.
- Joskow, P. & Edward Kahn, 2002. "A Quantitative Analysis of Pricing Behavior In California’s Wholesale Electricity Market During Summer 2000," Cambridge Working Papers in Economics 0211, Faculty of Economics, University of Cambridge.
- Paul Joskow & Edward Kahn, 2001. "A Quantitative Analysis of Pricing Behavior in California's Wholesale Electricity Market During Summer 2000," NBER Working Papers 8157, National Bureau of Economic Research, Inc.
- Robert Wilson, 2002. "Architecture of Power Markets," Econometrica, Econometric Society, vol. 70(4), pages 1299-1340, July.
- Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
- Yoella Bereby-Meyer & Alvin E. Roth, 2006.
"The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation,"
American Economic Review,
American Economic Association, vol. 96(4), pages 1029-1042, September.
- Roth, Alvin & Bereby-Meyer, Yoella, 2006. "The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation," Scholarly Articles 2580381, Harvard University Department of Economics.
- Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
- Ghaffari, Ali & Zare, Samaneh, 2009. "A novel algorithm for prediction of crude oil price variation based on soft computing," Energy Economics, Elsevier, vol. 31(4), pages 531-536, July.
- Severin Borenstein & James B. Bushnell & Frank A. Wolak, 2002. "Measuring Market Inefficiencies in California's Restructured Wholesale Electricity Market," American Economic Review, American Economic Association, vol. 92(5), pages 1376-1405, December.
When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1009-1024. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.