Authors:
Atakelty Hailu
1
;
John Rolfe
2
;
Jill Windle
2
and
Romy Greiner
3
Affiliations:
1
University of Western Australia, Australia
;
2
Centre for Environmental Management, Central Queensland University, Australia
;
3
River Consulting, Australia
Keyword(s):
Computational economics, Auction design, Agent-based modelling, Conservation auctions.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Economic Agent Models
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
Conservation auctions are tender-based mechanisms for allocating contracts among landholders who are intertested in undertaking conservation activities in return for monetary rewards. These auctions have grown in popularity over the last decade. However, the services offered under these auctions can be complex and auction design and implementation features need to be carefully considered if these auctions are to perform well. Computational experiments are key to bed-testing auction design as the bulk of auction theory (as the rest of economic theory) is focused on simple auctions for tractability reasons. This paper presents results from an agent-based modelling study investigating the impact on performance of four auction features: scope of conservation activities in tendered projects; auction budget levels relative to bidder population size (scale effects); endogeneity of bidder participation; and auction pricing rules (uniform versus discriminatory pricing). The results highlight
the importance of a careful consideration of scale and scope issues and that policymakers need to consider alternatives to currently used pay-as-bid or discriminatory pricing fromats. Averaging over scope variations, the uniform auction can deliver at least 25\% more benefits than the discriminatory auction.
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