2007
DOI: 10.1016/j.dss.2006.09.008
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Simulating combinatorial auctions with dominance requirement and loll bids through automated agents

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Cited by 13 publications
(10 citation statements)
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“…Kim presented an agentbased simulation model to estimate the effects of auction parameters on the auction outcomes, and to find an optimal infrastructure [7]. Combinatorial auction was studied in Avenali's paper [5].…”
Section: Review Of the Related Researchmentioning
confidence: 99%
“…Kim presented an agentbased simulation model to estimate the effects of auction parameters on the auction outcomes, and to find an optimal infrastructure [7]. Combinatorial auction was studied in Avenali's paper [5].…”
Section: Review Of the Related Researchmentioning
confidence: 99%
“…Other prior research has simulated bidding behaviour in auctions (Stone et al, 2003;Reeves et al, 2005;Avenali and Bassanini, 2007). However, these works are aimed at assessing particular tools or techniques and not at predicting results of real auctions.…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, should it be possible to coordinate themselves, there remains the problem that each bidder of the group will be encouraged to engage in non-virtuous behaviors, waiting for the others to bid more to beat the current bid (free-rider problem; see Milgrom, 2000). Multiple auction mechanisms have been proposed in the literature to deal with these problems (specially the threshold one), such as for instance formats applying the Vickrey-Clarke-Groves (VCG) rule, iterative ascending schemes storing submitted bids, auctions making use of proxy agents; see for example Ausubel (2004), Ausubel and Milgrom (2002), Avenali and Bassanini (2007), AUSM procedure by Banks et al (1989), two stage PAUSE format by Kelly and Steinberg (2000), iBundle scheme by Parkes (1999), Varian and MacKie-Mason (1995). Moreover, in combinatorial auctions coordination among players in terms of submitted bids can have completely different effects on auctions performances; in fact, it can help overcoming the threshold problem, but it can also be used by players to win specific items by offering very low bids.…”
Section: Introductionmentioning
confidence: 99%