By Martin Chapman, Gareth Tyson, Katie Atkinson (auth.), Esther David, Christopher Kiekintveld, Valentin Robu, Onn Shehory, Sebastian Stein (eds.)
This quantity includes eleven completely refereed and revised papers detailing contemporary advances in study on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially awarded on the Joint Workshop on buying and selling Agent layout and research (TADA 2012) and Agent-Mediated digital trade (AMEC 2012) co-located with AAMAS 2012 in Valencia, Spain, in June 2012.
The expanding reliance on software program brokers has created various urgent new learn demanding situations, together with the layout of acceptable agent choice algorithms, methods for predicting the advanced behaviors and interactions of a number of brokers, together with the computation of equilibria, and the engineering of protocols and mechanisms that be sure digital markets behave in a solid demeanour or satisfy different fascinating standards. Drawing upon a various variety of clinical disciplines, together with laptop technology, economics, synthetic intelligence, operations examine and video game thought, the papers amassed during this quantity characterize a cross-section of modern study and canopy themes comparable to techniques for person buying and selling brokers, the layout of markets and interplay protocols among brokers, and quite a few applications.
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Extra resources for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AMEC and TADA 2012, Valencia, Spain, June 4th, 2012, Revised Selected Papers
AMEC/TADA 2012, LNBIP 136, pp. 45–60, 2013. c Springer-Verlag Berlin Heidelberg 2013 46 T. Goﬀ et al. auction games [4, 5] and then search for equilibria in the space of those strategies . In this paper, we introduce the Java Auction Conﬁguration Kit (JACK), an auction simulator that allows researchers to customize auctions and collect empirical data from participating bidders (whether human or autonomous). We envision the uses of JACK to include: 1. In trading agent design and analysis, JACK can facilitate the simulation of autonomous agents in a speciﬁc auction domain by providing auction simulation infrastructure and not restricting the developer’s language choice.
R values range in the interval [−c, c], where c deﬁnes the maximum deviation of the ﬁnal shout price from the successful price. The success rate of the agent’s M latest shouts is computed as follows: k= M i=1 acceptedShouts(i) M (5) where acceptedShouts(i) returns 1 or 0 if the shout was successful or not respectively. Equation (5) is then normalized to the interval [−c, c]: kˆ = c · (2k − 1) (6) ˆ For example, if the Note that the risk factor could be assigned the value of k. success rate of the agent’s latest M asks is higher than 50%, then the normalized kˆ is positive, meaning that the agent should probably take risks by increasing its shout pricing policy.
Considering transaction T is the most recent, then wT = 1 and all other weights are updated based on equation (4): wi−1 = r · wi r ∈ [0, 1] (4) where r determines the importance given to former shouts by the agent. Equations (3) and (4) are valid for both bids and asks. Mertacor also behaves in a predictive manner. A risk factor R determines the agent’s eagerness to take risks in order to pursue greater proﬁt. R values range in the interval [−c, c], where c deﬁnes the maximum deviation of the ﬁnal shout price from the successful price.