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Experience weighted attraction (EWA) learning parameter value choice for multiple-unit iterative combinatorial auction games

Citation

Iftekhar, MS and Hailu, A, Experience weighted attraction (EWA) learning parameter value choice for multiple-unit iterative combinatorial auction games, Journal of Computational Optimization in Economics and Finance, 3, (1) pp. 1-11. ISSN 1941-3971 (2012) [Refereed Article]


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Copyright 2012 Nova Science Publishers Inc.

Official URL: https://www.novapublishers.com/catalog/product_inf...

Abstract

Recently, interest in multiple-unit iterative combinatorial auction games has increased. In these auctions bidders are able to combine different items and also offer different quantities of individual items. Providing feedback on implied current prices using results from intermediate rounds facilitates bidding. The presence of feedback prices makes it possible to use relatively new learning algorithms such as Experience Weighted Attraction (EWA) Learning, EWA is flexible and can take different functional forms depending on the parameter values chosen. However, the EWA algorithm has not yet been applied in multiple-unit combinatorial auction games and the exisitng literature does not provide any guidelines for suitable parameter values. We evaluated the sensitivity of auction performance to different parameter values using an agent based model (ABM) of an auction market. Our findings indicated that auction outcomes were moderately sensitive to the parameter values for EWA, and identified value ranges that improved auction outcomes.

Item Details

Item Type:Refereed Article
Keywords:experience weighted attraction learning, iterative combinatorial auctions, multiple unit auctions
Research Division:Economics
Research Group:Applied Economics
Research Field:Experimental Economics
Objective Division:Economic Framework
Objective Group:Microeconomics
Objective Field:Market-Based Mechanisms
Author:Iftekhar, MS (Mr Sayed Iftekhar)
ID Code:70559
Year Published:2012
Deposited By:Economics and Finance
Deposited On:2011-06-28
Last Modified:2017-05-26
Downloads:0

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