Multi-agent learning and the descriptive value of simple models

DOI10.1016/j.artint.2007.01.001zbMath1168.68458OpenAlexW2010307534MaRDI QIDQ1028927

Alvin E. Roth, Ido Erev

Publication date: 9 July 2009

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2007.01.001

zbMATH Keywords

reinforcement learningfictitious playreciprocationequivalent number of observations (ENO)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Rationality and learning in game theory (91A26)

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Chaotic switching in a two-person game

Cites Work

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• If multi-agent learning is the answer, what is the question?
• Coordination, ``Magic, and reinforcement learning in a market entry game
• Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term
• Learning and equilibrium as useful approximations: accuracy of prediction on randomly selected constant sum games
• An iterative method of solving a game
• Prospect Theory: An Analysis of Decision under Risk
• The Dynamics of Reorganization in Matching Markets: A Laboratory Experiment Motivated by A Natural Experiments*
• Strategic Experimentation
• Nonmetric test of the minimax theory of two-person zerosum games.
• The medium prizes paradox: Evidence from a simulated casino