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Advances in Artificial Economics

Author

Listed:
  • Frédéric Amblard

    (IRIT-SMAC - Systèmes Multi-Agents Coopératifs - IRIT - Institut de recherche en informatique de Toulouse - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse - TMBI - Toulouse Mind & Brain Institut - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse, UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Francisco J. Miguel

    (UAB - Universitat Autònoma de Barcelona = Autonomous University of Barcelona = Universidad Autónoma de Barcelona)

  • Adrien Blanchet

    (GREMAQ - Groupe de recherche en économie mathématique et quantitative - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Benoit Gaudou

    (IRIT-SMAC - Systèmes Multi-Agents Coopératifs - IRIT - Institut de recherche en informatique de Toulouse - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse - TMBI - Toulouse Mind & Brain Institut - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse, UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

Abstract

The book presents a peer-reviewed collection of papers presented during the 10th issue of the Artificial Economics conference, addressing a variety of issues related to macroeconomics, industrial organization, networks, management and finance, as well as purely methodological issues. The field of artificial economics covers a broad range of methodologies relying on computer simulations in order to model and study the complexity of economic and social phenomena. The grounding principle of artificial economics is the analysis of aggregate properties of simulated systems populated by interacting adaptive agents that are equipped with heterogeneous individual behavioral rules. These macroscopic properties are neither foreseen nor intended by the artificial agents but generated collectively by them. They are emerging characteristics of such artificially simulated systems.

Suggested Citation

  • Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou, 2015. "Advances in Artificial Economics," Post-Print hal-03209315, HAL.
    • Handle: RePEc:hal:journl:hal-03209315
    DOI: 10.1007/978-3-319-09578-3
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    Citations

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    Cited by:

    1. Pierfrancesco Dotta & Marco Tolotti & Jorge Yepez, 2017. "Measuring Brand Awareness In A Random Utility Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-11, March.
    2. Klaus G. Troitzsch, 2015. "What One Can Learn from Extracting OWL Ontologies from a NetLogo Model That Was Not Designed for Such an Exercise," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-14.
    3. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    4. Klaus G. Troitzsch, 2015. "Extortion Racket Systems As Targets For Agent-Based Simulation Models. Comparing Competing Simulation Models And Emprical Data," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-19, August.
    5. Aleksandra Aloric & Peter Sollich & Peter McBurney & Tobias Galla, 2015. "Emergence of Cooperative Long-term Market Loyalty in Double Auction Markets," Papers 1510.07927, arXiv.org, revised Aug 2017.
    6. Segismundo S. Izquierdo & Luis R. Izquierdo, 2015. "The “Win-Continue, Lose-Reverse” Rule In Oligopolies: Robustness Of Collusive Outcomes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-23, August.
    7. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.
    8. Stefano Zedda & Antonella Spinace-Casale, 2021. "Modeling and Simulating Cross Country Banking Contagion Risks," JRFM, MDPI, vol. 14(8), pages 1-16, July.
    9. Aleksandra Alorić & Peter Sollich & Peter McBurney & Tobias Galla, 2016. "Emergence of Cooperative Long-Term Market Loyalty in Double Auction Markets," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-26, April.

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