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Non-Employment Benefits and the Evolution of Worker-Employer Cooperation: Experiments with Real and Computational Agents

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  • Pingle, Mark
  • Tesfatsion, Leigh

Abstract

Experiments with real and computational agents are used to examine the impact of changing the level of a non-employment payoff on the evolution of cooperation between workers and employers participating in a sequential employment game with incomplete contracts. Workers either direct work offers to preferred employers or choose unemployment. and receive the non-employment payoff. Subject to capacity limitations, employers either accept work offers from preferred workers or remain-vacant' and receive the non-employment payoff. Matched workers and. employers participate in an employment relationship modeled as a prisoner's dilemma game. • In both types of experiments, increases in the non-employment payoff result in higher unemployment and vacancy rates while at the same time encouraging higher rates of cooperation among the workers and . employers who do form matches. However, the behaviors exhibited by the computational agents are coordinated to a higher degree than the behaviors of .the human subjects.. This difference , raises. challenging questions for both human-subject and computational experimentalists.

Suggested Citation

  • 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.
    • Handle: RePEc:isu:genstf:200106010700001053
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    Cited by:

    1. Haruvy, Ernan & Roth, Alvin E. & Unver, M. Utku, 2006. "The dynamics of law clerk matching: An experimental and computational investigation of proposals for reform of the market," Journal of Economic Dynamics and Control, Elsevier, vol. 30(3), pages 457-486, March.
    2. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    3. Hailu, Atakelty & Schilizzi, Steven, 2003. "Investigating the performance of market-based instruments for resource conservation: the contribution of agent-based modelling," 2003 Conference (47th), February 12-14, 2003, Fremantle, Australia 57883, Australian Agricultural and Resource Economics Society.
    4. Kimbrough, Erik O., 2011. "Heuristic learning and the discovery of specialization and exchange," Journal of Economic Dynamics and Control, Elsevier, vol. 35(4), pages 491-511, April.
    5. 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..
    6. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    7. 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.
    8. Atakelty Hailu & Steven Schilizzi, 2004. "Are Auctions More Efficient Than Fixed Price Schemes When Bidders Learn?," Australian Journal of Management, Australian School of Business, vol. 29(2), pages 147-168, December.
    9. Andreas Ortmann & Sergey Slobodyan & Samuel S. Nordberg, 2003. "(The Evolution of) Post-Secondary Education: A Computational Model and Experiments," CERGE-EI Working Papers wp208, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

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