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Learning in a “Basket of Crabs”: An Agent-Based Computational Model of Repeated Conservation Auctions

In: Nonlinear Dynamics and Heterogeneous Interacting Agents

Author

Listed:
  • Atakelty Hailu

    (University of Western Australia)

  • Steven Schilizzi

    (University of Western Australia)

Abstract

Summary Auctions are increasingly being considered as a mechanism for allocating conservation contracts to private landowners. This interest is based on the widely held belief that competitive bidding helps minimize information rents. This study constructs an agent-based model to evaluate the long term performance of conservation auctions under settings where bidders are allowed to learn from previous outcomes. The results clearly indicate that the efficiency benefits of one-shot auctions are quickly eroded under dynamic settings. Furthermore, the auction mechanism is not found to be superior to fixed payment schemes except when the latter involve the use of high prices.

Suggested Citation

  • Atakelty Hailu & Steven Schilizzi, 2005. "Learning in a “Basket of Crabs”: An Agent-Based Computational Model of Repeated Conservation Auctions," Lecture Notes in Economics and Mathematical Systems, in: Thomas Lux & Eleni Samanidou & Stefan Reitz (ed.), Nonlinear Dynamics and Heterogeneous Interacting Agents, pages 27-39, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-27296-0_3
    DOI: 10.1007/3-540-27296-8_3
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    Cited by:

    1. David Evans & Andrew Reeson, 2022. "The Performance of a Repeated Discriminatory Price Auction for Ecosystem Services," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(4), pages 787-806, April.

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