By Dave Cliff (auth.), Maria Fasli, Onn Shehory (eds.)
Thedesignandanalysisoftradingagentsandelectronictradingsystemsinwhich they're deployed contain ?nding ideas to a various set of difficulties, invo- ing person behaviors, interplay, and collective habit within the context of alternate. a large choice of buying and selling situations and platforms, and agent ways to those, were studied lately. the current quantity contains a variety of papers that have been awarded as a part of the Joint foreign Workshop on buying and selling Agent layout and research and Agent-Mediated digital trade which was once collocated with the independent brokers and Multi-agent platforms (AAMAS) convention in Hakodate, Japan, in could 2006. The Joint TADA/AMEC Workshop introduced jointly the 2 winning and well-established occasions of the buying and selling Agent layout and research (TADA) and Agent-Mediated digital trade (AMEC) Workshops. The TADA sequence of workshops serves as a discussion board for featuring paintings on buying and selling agent layout and applied sciences, theoretical and empirical assessment of ideas in advanced buying and selling situations in addition to mechanism layout. TADA additionally serves because the major discussion board for the buying and selling Agent festival (TAC) study neighborhood. TAC is an annual match whose objective is to stimulate examine in buying and selling brokers and industry mechanisms by means of supplying a platform for brokers competing in we- de?ned marketplace eventualities (http://www. sics. se/tac). The AMEC sequence of wo- outlets offers interdisciplinary researchon either theoretical and functional problems with agent-mediated digital trade starting from the layout of digital marketplaces and e?cient protocols to behavioral points of brokers working in suchenvironments.
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Additional info for Agent-Mediated Electronic Commerce. Automated Negotiation and Strategy Design for Electronic Markets: AAMAS 2006 Workshop, TADA/AMEC 2006, Hakodate, Japan, May 9, 2006, Selected and Revised Papers
R. Jennings Thus, the optimal choices for a and b are: OPTA (i, t) = arg maxrj=1 EUA(i, j, t) (5) OPTB(i, t) maxrj=1 EUB(i, j, t) (6) = arg We compute the optimal choice for t = 1 by reasoning backwards from t = Nr . At t = 1, if an agent a of type i is the offering agent, then it offers the package that corresponds to b being of type OPTA(i,1). Likewise, if an agent b of type i is the offering agent, then it offers the package that corresponds to a being of type OPTB(i,1). But since OPTA(i,1) and OPTB(i,1) are obtained under uncertainty, an agreement may or may not occur at t = 1.
Hence, F a is: F a (i, j, e, t) = Uia (A(i, j, t)) if Ueb (A(i, j, t)) ≥ EUA(i, t + 1) otherwise EUB(e, γ, t + 1) where γ = OPTB(e, t + 1). The strategy A(i, j, t) for t = Nj is: A(i, j, t) = OFFER [δ n−1 , 0] if a’s turn ACCEPT otherwise and for all time periods t < Nj it is: A (i, j, t) = OFFER TRADEOFFA(EUB(j, t)) if a’s turn if Uia ([xt , y t ], t) ≥ EUA(i, t) ACCEPT else REJECT otherwise where [xt , y t ] is the package offered at t. Analogously, F b is: F b (i, j, e, t) = Uib (B(i, j, t)) if Uea (B(i, j, t)) ≥ EUB(i, t + 1) otherwise EUA(e, α, t + 1) where α = OPTA(e, t + 1).
For the former, a’s equilibrium offer at t = 1 is [1, 0] for each issue. 7. 675] for the first issue and [1, 0] for the second one. 325. Since EUA (1, 1, 1) > EUA (1, 2, 1), OPTA (1, 1) = 1 and a plays the former strategy. Now if b is actually of type 1, then it accepts a’s offer. Thus, the earliest possible time of agreement is t = 1. But if b is of type 2, it rejects a’s offer since it can get a higher expected utility at t = 2. However, since a is of type 1, negotiation ends in a conflict. If agent a’s offer at t = 1 gets rejected it knows that agent b is not of type OPTA(i, 1).
Agent-Mediated Electronic Commerce. Automated Negotiation and Strategy Design for Electronic Markets: AAMAS 2006 Workshop, TADA/AMEC 2006, Hakodate, Japan, May 9, 2006, Selected and Revised Papers by Dave Cliff (auth.), Maria Fasli, Onn Shehory (eds.)