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A Computational Laboratory for Evolutionary Trade Networks

Author

Listed:
  • McFadzean, David
  • Stewart, Deron
  • Tesfatsion, Leigh S.

Abstract

This report presents, motivates, and illustrates the use of a computational laboratory for the investigation of evolutionary trade network formation among strategically interacting buyers, sellers, and dealers. The computational laboratory, referred to as the Trade Network Game Laboratory (TNG Lab), is targeted for the Microsoft Windows desktop. The TNG Lab is both modular and extensible and has a clear, easily operated graphical user interface. It permits visualization of the formation and evolution of trade networks by means of run-time animations. Data tables and charts reporting descriptive performance statistics are also provided in real time. The capabilities of the TNG Lab are demonstrated by means of labor market experiments. An automatic installation program for the TNG Lab is available online, as well as TNG Lab tutorials. TNG downloads, tutorials, and research articles can be accessed at: http://www2.econ.iastate.edu/tesfatsi/TNGHome.htm

Suggested Citation

  • McFadzean, David & Stewart, Deron & Tesfatsion, Leigh S., 2001. "A Computational Laboratory for Evolutionary Trade Networks," Staff General Research Papers Archive 2049, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:2049
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    References listed on IDEAS

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    1. Tesfatsion, Leigh, 1995. "A Trade Network Game with Endogenous Partner Selection," ISU General Staff Papers 199505010700001034, Iowa State University, Department of Economics.
    2. McFadzean, David & Tesfatsion, Leigh, 1999. "A C++ Platform for the Evolution of Trade Networks," Computational Economics, Springer;Society for Computational Economics, vol. 14(1-2), pages 109-134, October.
    3. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    4. Tesfatsion, Leigh, 2001. "Structure, behavior, and market power in an evolutionary labor market with adaptive search," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 419-457, March.
    5. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
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    Citations

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    Cited by:

    1. Babanov, A. & Ketter, W. & Gini, M., 2008. "An Evolutionary Framework for Determining Heterogeneous Strategies in Multi-Agent Marketplaces," ERIM Report Series Research in Management ERS-2008-002-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    3. Mark Pingle & Leigh Tesfatsion, 2004. "Evolution Of Worker-Employer Networks And Behaviors Under Alternative Non-Employment Benefits: An Agent-Based Computational Study," World Scientific Book Chapters, in: Roberto Leombruni & Matteo Richiardi (ed.), Industry And Labor Dynamics The Agent-Based Computational Economics Approach, chapter 8, pages 129-163, World Scientific Publishing Co. Pte. Ltd..
    4. Pingle, Mark & Tesfatsion, Leigh, 2001. "Non-Employment Benefits and the Evolution of Worker-Employer Cooperation: Experiments with Real and Computational Agents," ISU General Staff Papers 200106010700001053, Iowa State University, Department of Economics.
    5. Pingle, Mark & Tesfatsion, Leigh, 2003. "Evolution of Worker-Employer Networks and Behaviors Under Alternative Non-Employment Benefits: An Agent-Based Computational Approach," Staff General Research Papers Archive 10376, Iowa State University, Department of Economics.
    6. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    7. Mark Pingle and Leigh Tesfatsion, 2001. "Unemployment Insurance and the Evolution of Worker-Employer\n Cooperation: Experiments with Real and Artificial Agents," Computing in Economics and Finance 2001 279, Society for Computational Economics.
    8. Halkos, George & Tsilika, Kyriaki, 2016. "Assessing classical input output structures with trade networks: A graph theory approach," MPRA Paper 72511, University Library of Munich, Germany.
    9. Alan G. Isaac, 2008. "Simulating Evolutionary Games: A Python-Based Introduction," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-8.
    10. George E. Halkos & Kyriaki D. Tsilika, 2016. "Trading Structures for Regional Economies in CAS Software," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 523-533, October.
    11. George E. Halkos & Kyriaki D. Tsilika, 2018. "A New Vision of Classical Multi-regional Input–Output Models," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 571-594, March.

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    More about this item

    Keywords

    Trade networks; evolutionary game; partner choice; Agent-based test bed; Trade Network Game laboratory;
    All these keywords.

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D2 - Microeconomics - - Production and Organizations
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior

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