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A computational laboratory for evolutionary trade networks

Author

Listed:
  • McFadzean, David
  • Stewart, D.
  • Tesfatsion, Leigh

Abstract

This study presents, motivates, and illustrates the use of a computational laboratory (CL) for the investigation of evolutionary trade network formation among strategically interacting buyers, sellers, and dealers. The CL, 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 real-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.

Suggested Citation

  • McFadzean, David & Stewart, D. & Tesfatsion, Leigh, 2002. "A computational laboratory for evolutionary trade networks," ISU General Staff Papers 200201010800001051, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:200201010800001051
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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. Zhuoming, Ren & Wan, Wang & Yu, Lin & Li, Zhao, 2024. "Modeling complex network perturbations on resilience of the bilateral regional trade agreements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).
    4. 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..
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

    More about this item

    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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