Computational Techniques for Modelling Learning in Economics

Preface. List of Contributors. Part One: Simulating in Economics. Evolutionary Economics and Simulation; W. Kwasnicki. Simulation as a Tool to Model Stochastic Processes in Complex Systems; K.G. Troitzsch. Part Two: Evolutionary Approaches. Learning by Genetic Algorithms in Economics? F. Beckenbach. Can Learning-Agent Simulations Be Used for Computer Assisted Design in Economics? T.C. Price. On the Emergence of Attitudes towards Risk; S. Huck, et al. Interdependencies, Nearly-decomposability and Adaptation; K. Frenken, et al. Part Three: Neural Networks and Local Interaction. Neural Networks in Economics; R. Herbrich, et al. Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoners Dilemma; R.E. Marks, H. Schnabl. Local Interaction as a Model of Social Interaction? D.K. Herreiner. Part Four: Boundedly Rational and Rational Models. Memory, Learning and the Selection of Equilibria in a Model with Non-Uniqueness; E. Barucci. A Behavioral Approach to a Strategic Market Game; M. Shubik, N.J. Vriend. Bayesian Learning in Optimal Growth Models under Uncertainty; S.M.N. Islam. Part Five: Cognitive Learning Models. Modelling Bounded Rationality in Agent-based Simulations Using the Evolution of Mental Models; B. Edmonds. Cognitive Learning in Prisoner's Dilemma Situations; T. Brenner. A Cognitively Rich Methodology for Modelling Emergent Socioeconomic Phenomena; S. Moss. Index.