LEARNING TO FACE STOCHASTIC DEMAND

DOI10.1142/S0219198900000238zbMath0989.91063OpenAlexW2014903328MaRDI QIDQ2743762

Maria Sandsmark, Sjur Didrik Flåm

Publication date: 27 September 2001

Published in: International Game Theory Review (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219198900000238

zbMATH Keywords

non-cooperative gamesdemandcommon market

Mathematics Subject Classification ID

Noncooperative games (91A10) Consumer behavior, demand theory (91B42)

Cites Work

• Convergence of least squares learning mechanisms in self-referential linear stochastic models
• Averaged predictions and the learning of equilibrium play
• Learning, estimation, and the stability of rational expectations
• Iterative computation of Cournot equilibrium
• On the stability of best reply and gradient systems with applications to imperfectly competitive models
• Calibrated learning and correlated equilibrium
• Subjective games and equilibria
• Learning dynamics in games with stochastic perturbations
• Market Dynamics and the Law of Demand
• A mathematical programming approach for determining oligopolistic market equilibrium
• A Dynamical System Approach to Stochastic Approximations

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