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Bidding and performance in multiple unit combinatorial fishery quota auctions: Role of information feedbacks
Affiliation:1. Centre for Environmental Economics & Policy (CEEP), School of Agricultural & Resource Economics (SARE), The University of Western Australia (UWA), M089, 35 Stirling Hwy, Crawley, WA 6009, Australia;2. Tasmanian School of Business and Economics, University of Tasmania, Private Bag 85, Hobart, TAS 7001, Australia;1. CSIRO Marine and Atmospheric Research, Wealth from Oceans Flagship, GPO Box 2583, Brisbane, Queensland 4001, Australia;2. Ifremer, UMR M101, AMURE, Unité d''Economie Maritime, BP 70, F-29280 Plouzané Cedex, France;3. CSIRO Marine and Atmospheric Research, Wealth From Oceans Flagship, GPO Box 1538, Hobart, Tasmania 7001, Australia;4. QLD Department of Agriculture, Fisheries and Forestry, GPO Box 46, Brisbane, Queensland 4001, Australia;1. Bicocca University, Department of Economics, Management and Statistics, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy;2. Free University of Bozen/Bolzano, Faculty of Economics and Management, Piazza Università 1, 39100 Bolzano, Italy;3. University of Brescia, Department of Economics and Management, Via S. Faustino 74/b, 25122 Brescia, Italy;1. Seafish, 18 Logie Mill, Logie Green Road, Edinburgh EH7 4HS, UK;2. Heriot-Watt University, Edinburgh EH14 4AS, UK;1. Northeast Fisheries Science Center, 166 Water St., Woods Hole, MA 02543, USA;2. Alaska Fisheries Science Center, 7600 Sand Point Way N.E., Seattle, WA 98115, USA;3. Southwest Fisheries Science Center, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA;4. Niels Bohrs Vej 9, 6700 Esbjerg, Denmark;1. Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA, 2725 Montlake Blvd. E. Seattle, WA 98112, USA;2. NOAA, NMFS, Office of Science and Technology, USA;3. NOAA, NMFS, Southeast Fisheries Science Center, USA;4. NOAA, NMFS, Northeast Fisheries Science Center, USA;5. NOAA, NMFS, Alaska Fisheries Science Center, USA;6. NOAA, NMFS, Northwest Fisheries Science Center, USA;7. NOAA, NMFS, Southeast Regional Office, USA
Abstract: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
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