Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics)
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The process of gradually finding an economic equilibrium, the so called tâtonnement process, is investigated in this paper. In constrast to classical general equilibrium modelling, where a central institution with perfect information about consumer preferences and production technologies ("Walrasian auctioneer") organizes the economy, we simulate this process with learning consumer and producer agents, but no auctioneer. These agents lack perfect information on consumption preferences and are unable to explicitly optimize utility and profits. Rather, consumers base their consumption decision on past experience - formalized by reinforcement learning - whereas producers do regression learning to estimate aggregate consumer demand for profit maximization. Our results suggest that, even without perfect information or explicit optimization, it is possible for the economy to converge towards the analytically optimal state.