Bidding and performance in multiple unit combinatorial fishery quota auctions: Role of information feedbacks
Md Sayed Iftekhar and
Marine Policy, 2015, vol. 62, issue C, 233-243
This article explores the role of market information and learning in multiple unit combinatorial markets for fishing quota. Combinatorial auctions allow trading of packages of different types of quotas (for example for different regions or industry) in the same auction market. Bidders can submit package bids which would allow them to enjoy synergy benefits. However, to realize the full benefit bidders require comprehensive understanding of the market. This article focuses on the impact of varying levels of information feedback on performance in multiple unit forward combinatorial auctions using laboratory experiments. In a general context of trade in fishery quota, it was asked whether (a) providing additional market information and (b) learning through time helps in more efficient outcomes. It is found that much of the benefits of information are derived from structural effects, like repeated rounds and package valuations. Providing additional market information does not improve auction performances to a large extent. These results will be useful in designing more efficient combinatorial markets for fisheries quota.
Keywords: Experience weighted attraction learning; Fishery quota market; Information feedback; Intelligent agent bidders (robots); Laboratory experiments; Multiple unit forward combinatorial auctions (search for similar items in EconPapers)
JEL-codes: D44 D47 Q2 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:marpol:v:62:y:2015:i:c:p:233-243
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