By Sofia Ceppi, Nicola Gatti (auth.), Esther David, Enrico Gerding, David Sarne, Onn Shehory (eds.)

This quantity includes 18 completely refereed and revised papers detailing fresh advances in examine on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially provided on the eleventh overseas Workshop on Agent-Mediated digital trade (AMEC 2009) collocated with AAMAS 2009 in Budapest, Hungary, or the 2009 Workshop on buying and selling Agent layout and research (TADA 2009) collocated with IJCAI 2009 in Pasadena, CA, united states. The papers concentrate on themes comparable to person agent habit and agent interplay, collective habit, mechanism layout, and computational facets, all within the context of e-commerce purposes like buying and selling, auctions, or negotiations. They mix ways from diversified fields of arithmetic, computing device technological know-how, and economics comparable to man made intelligence, disbursed structures, operations examine, and video game concept.

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Additional resources for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AAMAS Workshop, AMEC 2009, Budapest, Hungary, May 12, 2009, and IJCAI Workshop, TADA 2009, Pasadena, CA, USA, July 13, 2009, Selected and Revised Paper

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I−1 , ωi+1 , . . , ωn ). 1 Given the model just described, we want to design a mechanism so that the consumer can gather information from the individual experts, and, using the combined information, select the best service provider from amongst SP . That is, we would like the center to select: (1) k ∗ = arg max (V · P oSk (ω) − ρk ) k∈SP where P oSk (ω) is the estimated probability that provider k will successfully complete the task, given the information provided to the consumer by the experts.

First, we apply and compare a mechanism design approach as well as a scoring rule approach based on peer prediction. We show that neither of these approaches induce the agents to make accurate reports in our setting. To address this, we extend existing scoring rules so that agents are rewarded depending on the contribution that their reports have on the final outcome. However, we show that, under certain conditions, using this payment rule experts will have an incentive to misreport in order to try and manipulate the choice of the consumer.

If the true value of X is x ∈ T then the auctioneer discloses this value to the bidders. Given T , the bidders now update the probability distribution function of X, if no value is revealed from the auctioneer. The updated probability distribution function, denoted f ∗ (x), is given by: f ∗ (x) = 1− 0 f (x) f (y)d(y) y∈T if x ∈ T otherwise (12) Consequently, the bids received for x ∈ T can be calculated by substituting f (x) = f ∗ (x) in Equation 1. , can be calculated using Equation 5. The expected value for the auctioneer in this case can thus be obtained using Equation 11 where the values of gI (y) and GI (y) are calculated using the bids obtained from having the bidders update their estimate of the distribution of the value of the characteristic X.

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