Autonomous Agent Models of Stock Markets
Artificial Intelligence Review
Decentralized Interaction and Co-Adaptation in the Repeated Prisoner‘sDilemma
Computational & Mathematical Organization Theory
Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions
Computational Economics
Gold Price, Neural Networks and Genetic Algorithm
Computational Economics
Can Genetic Algorithms Explain Experimental Anomalies?
Computational Economics
A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents
Artificial Intelligence Review
Robust Evolutionary Algorithm Design for Socio-economic Simulation
Computational Economics
Robust Evolutionary Algorithm Design for Socio-Economic Simulation: Some Comments
Computational Economics
Auctions and differential pricing: optimal seller and bidder strategies in second-chance offers
Computational Economics
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From the Publisher:This book deals with the learning behavior of bounded rational agents in economic systems. In particular, the modeling of learning populations by genetic algorithms is studied in detail. After an extensive review and discussion of the existing literature in the first part, a mathematical analysis of the dynamic properties of genetic algorithm learning in the general framework of systems with a state dependent fitness function is provided. It is shown that coevolutionary economic models typically fall into this class and the usefulness of the analytical results derived is illustrated in several game theoretic and microeconomic models. The mathematical analysis is complemented by extensive simulation analyses. The last part of the book demonstrates how the obtained theory may be used to design the algorithm such that the learning of equilibria of the economic system is facilitated.