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Agent Learning Representation: Advice on Modelling Economic Learning

In: Handbook of Computational Economics

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Brenner, Thomas

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Abstract

This chapter presents an overview of the existing learning models in the economic literature. Furthermore, it discusses the choice of models that should be used under various circumstances and how adequate learning models can be chosen in simulation approaches. It gives advice for using the many existing models and selecting the appropriate model for each application.

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This chapter was published in: Leigh Tesfatsion & Kenneth L. Judd (ed.) Handbook of Computational Economics, , chapter 18, pages 895-947, 2006.

This item is provided by Elsevier in its series Handbook of Computational Economics with number 2-18.

Handle: RePEc:eee:hecchp:2-18

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This chapter was published in the following book, which is listed on IDEAS:
Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2, September. [Downloadable!] (restricted)
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Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

References listed on IDEAS
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  1. Jasmina Arifovic & John Ledyard, 2004. "Scaling Up Learning Models in Public Good Games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 6(2), pages 203-238, 05. [Downloadable!] (restricted)
  2. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer, vol. 28(4), pages 313-331, November. [Downloadable!] (restricted)
  2. Fernando Lozano & Jaime Lozano & Mario García, 2007. "An Artifitial Economy based on reinforcement learning and agent based modeling," DOCUMENTOS DE TRABAJO 003907, UNIVERSIDAD DEL ROSARIO - FACULTAD DE ECONOMÍA. [Downloadable!]
  3. Buda, Rodolphe, 2009. "Learning-Testing Process in Classroom: An Empirical Simulation Model," MPRA Paper 12146, University Library of Munich, Germany. [Downloadable!]
  4. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer, vol. 33(1), pages 47-78, February. [Downloadable!] (restricted)
  5. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  6. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer, vol. 30(3), pages 195-226, October. [Downloadable!] (restricted)
  7. Myong-Hun Chang & Joseph E Harrington Jr, 2004. "Agent-Based Models of Organizations," Economics Working Paper Archive 515, The Johns Hopkins University,Department of Economics. [Downloadable!]
    Other versions:
  8. Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer, vol. 28(1), pages 51-69, August. [Downloadable!] (restricted)
  9. Pierre Barbaroux & Cécile Godé-Sanchez, 2007. "Acquiring core capabilities through organizational learning: Illustrations from the U.S. military organizations," Post-Print hal-00293534_v1, HAL. [Downloadable!]
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