Computational Techniques for Modelling Learning in Economics

1. Front Matter

   Pages i-xiii

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2. Simulating in Economics

   1. Front Matter

      Pages 1-1

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   2. Evolutionary Economics and Simulation

      • Witold Kwaśnicki

      Pages 3-44

   3. Simulation as a Tool to Model Stochastic Processes in Complex Systems

      • Klaus G. Troitzsch

      Pages 45-69

3. Evolutionary Approaches

   1. Front Matter

      Pages 71-71

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   2. Learning by Genetic Algorithms in Economics?

      • Frank Beckenbach

      Pages 73-100

   3. Can Learning-Agent Simulations be used for Computer Assisted Design in Economics?

      • Tony Curzon Price

      Pages 101-121

   4. On the Emergence of Attitudes Towards Risk

      • Steffen Huck, Wieland Müller, Martin Strobel

      Pages 123-143

   5. Interdependencies, Nearly-Decomposability and Adaptation

      • Koen Frenken, Luigi Marengo, Marco Valente

      Pages 145-165

4. Neural Networks and Local Interaction

   1. Front Matter

      Pages 167-167

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   2. Neural Networks in Economics

      • Ralf Herbrich, Max Keilbach, Thore Graepel, Peter Bollmann-Sdorra, Klaus Obermayer

      Pages 169-196

   3. Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoners Dilemma

      • Robert E. Marks, Hermann Schnabl

      Pages 197-219

   4. Local Interaction as a Model of Social Interaction?

      • Dorothea K. Herreiner

      Pages 221-239

5. Boundedly Rational and Rational Models

   1. Front Matter

      Pages 241-241

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   2. Memory, Learning and the Selection of Equilibria in a Model with Non-Uniqueness

      • Emilio Barucci

      Pages 243-260

   3. A Behavioral Approach to a Strategic Market Game

      • Martin Shubik, Nicolaas J. Vriend

      Pages 261-282

   4. Bayesian Learning in Optimal Growth Models Under Uncertainty

      • Sardar M. N. Islam

      Pages 283-302

6. Cognitive Learning Models

   1. Front Matter

      Pages 303-303

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   2. Modelling Bounded Rationality in Agent-Based Simulations Using the Evolution of Mental Models

      • Bruce Edmonds

      Pages 305-332

   3. Cognitive Learning in Prisoner’s Dilemma Situations

      • Thomas Brenner

      Pages 333-361

Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.

• agents
• algorithms
• complex systems
• economic growth
• economics
• evolutionary economics
• genetic programming
• modeling
• simulation
• simulations

• Max-Planck-Institute for Research into Economic Systems, Germany

  Thomas Brenner

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