Multi-agent learning and the descriptive value of simple models
type: Item
instance of: scholarly article
title: Multi-agent learning and the descriptive value of simple models
zbMATH Open document ID: 1168.68458
author: Ido Erev
author: Alvin E. Roth
published in: Artificial Intelligence
publication date: 2009-07-09T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 91A26
zbMATH DE Number: 5576679
zbMATH Keywords: reinforcement learning
zbMATH Keywords: fictitious play
zbMATH Keywords: equivalent number of observations (ENO)
zbMATH Keywords: reciprocation
MaRDI profile type: Publication
full work available at URL: j.artint.2007.01.001
OpenAlex ID: W2010307534
cites work: Strategic Experimentation
cites work: Coordination, ``Magic'', and reinforcement learning in a market entry game
cites work: Learning and equilibrium as useful approximations: accuracy of prediction on randomly selected constant sum games
cites work: Q4223194
cites work: The medium prizes paradox: Evidence from a simulated casino
cites work: Prospect Theory: An Analysis of Decision under Risk
cites work: The Dynamics of Reorganization in Matching Markets: A Laboratory Experiment Motivated by A Natural Experiments*
cites work: Nonmetric test of the minimax theory of two-person zerosum games.
cites work: An iterative method of solving a game
cites work: Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term
cites work: If multi-agent learning is the answer, what is the question?
cites work: Q3266148
DOI: 10.1016/J.ARTINT.2007.01.001
description: scientific article; zbMATH DE number 5576679