Learning agents in an artificial power exchange: Tacit collusion, market power and efficiency of two double-auction mechanisms

DOI10.1007/S10614-008-9127-5zbMath1142.91369OpenAlexW2047290978WikidataQ60355335 ScholiaQ60355335MaRDI QIDQ943954

Stefano Ivaldi, Eric Guerci, Silvano Cincotti

Publication date: 12 September 2008

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10614-008-9127-5

zbMATH Keywords

Agent-based simulationReinforcement learningMarket powerPower exchange

Mathematics Subject Classification ID

Other game-theoretic models (91A40) Auctions, bargaining, bidding and selling, and other market models (91B26) Rationality and learning in game theory (91A26)

Related Items (1)

Agent-based Modeling and Simulation of Competitive Wholesale Electricity Markets

Cites Work

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• Handbook of computational economics. Vol. 2: Agent-based computational economics
• If multi-agent learning is the answer, what is the question?
• Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market
• Evaluating individual market power in electricity markets via agent-based simulation
• \({\mathcal Q}\)-learning
• Dynamic testing of wholesale power market designs: an open-source agent-based framework
• Supply Function Equilibria in Oligopoly under Uncertainty

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