Learning in a “Basket of Crabs”: An Agent-Based Computational Model of Repeated Conservation Auctions
Atakelty Hailu () and
Steven Schilizzi ()
Additional contact information
Atakelty Hailu: University of Western Australia
Steven Schilizzi: University of Western Australia
A chapter in Nonlinear Dynamics and Heterogeneous Interacting Agents, 2005, pp 27-39 from Springer
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.
Keywords: Opportunity Cost; Auction Mechanism; Competitive Bidding; Fixed Price; Information Rent (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (1)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-540-27296-0_3
Ordering information: This item can be ordered from
http://www.springer.com/9783540272960
DOI: 10.1007/3-540-27296-8_3
Access Statistics for this chapter
More chapters in Lecture Notes in Economics and Mathematical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().