Discrete Double Auctions with Artificial Adaptive Agents: A Case Study of an Electricity Market Using a Double Auction Simulator
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.
|Date of creation:||12 Sep 2002|
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- 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 119-37, February.
- 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.
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