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Discrete Double Auctions with Artificial Adaptive Agents: A Case Study of an Electricity Market Using a Double Auction Simulator

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  • Koesrindartoto, Deddy P.

Abstract

A key issue raised by previous researchers is the extent to which learning versus market structure is responsible for the high efficiency regularly observed for the double auction in human-subject experiments. In this study, a computational discrete double auction with discriminatory pricing is tested regarding the importance of learning agents for ensuring market efficiency. Agents use a Roth-Erev reinforcement learning algorithm to determine their bid and ask prices. The experimental design focuses on two treatment factors: market capacity; and a key Roth?Erev learning parameter that controls that degree of agent experimentation. For each capacity setting, it is shown that changes in the learning parameter have a substantial systematic effect on market efficiency.

Suggested Citation

  • Koesrindartoto, Deddy P., 2002. "Discrete Double Auctions with Artificial Adaptive Agents: A Case Study of an Electricity Market Using a Double Auction Simulator," Staff General Research Papers Archive 10017, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:10017
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    References listed on IDEAS

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    1. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 561-592, March.
    2. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    3. Nicolaisen, James & Smith, Matthew & Petrov, Valentin & Tesfatsion, Leigh, 2000. "Concentration and Capacity Effects on Electricity Market Power," Staff General Research Papers Archive 1847, Iowa State University, Department of Economics.
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    Cited by:

    1. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    2. Deddy Koesrindartoto, 2003. "Treasury Auctions, Uniform or Discriminatory?: An Agent-based Approach," Computing in Economics and Finance 2003 241, Society for Computational Economics.
    3. Fischer, Carolyn, 2011. "Market power and output-based refunding of environmental policy revenues," Resource and Energy Economics, Elsevier, vol. 33(1), pages 212-230, January.
    4. Gaivoronskaia, E. & Tsyplakov, A., 2018. "Using a Modified Erev-Roth Algorithm in an Agent-Based Electricity Market Model," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 55-83.
    5. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.

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