Download Agent-Mediated Electronic Commerce. Designing Trading by Jordan Berg, Carleton Coffrin, Amy Greenwald (auth.), Esther PDF

By Jordan Berg, Carleton Coffrin, Amy Greenwald (auth.), Esther David, Kate Larson, Alex Rogers, Onn Shehory, Sebastian Stein (eds.)

This quantity comprises nine completely refereed and revised papers detailing fresh advances in study on designing buying and selling brokers and mechanisms for agent-mediated e-commerce. They have been initially offered on the twelfth overseas Workshop on Agent-Mediated digital trade (AMEC 2010), collocated with AAMAS 2010 in Toronto, Canada, or the 2010 Workshop on buying and selling Agent layout and research (TADA 2010), collocated with EC 2010 in Cambridge, MA, USA.

The papers learn rising subject matters corresponding to advert auctions and provide chains, or the interactions among competing markets, and current novel algorithms and rigorous theoretical effects. numerous of them overview their effects utilizing genuine information from huge e-commerce websites or from experiments with human traders.

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Extra info for Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets: AMEC 2010, Toronto, ON, Canada, May 10, 2010, and TADA 2010, Cambridge, MA, USA, June 7, 2010 Revised Selected Papers

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C. 15 zip 30 K. Cai, J. Niu, and S. Parsons playing catch-up. Naturally this will mean it is less than completely efficient. 4 Discussion The aim of this work was to test the hypotheses that: 1. The topology of the network market will affect the speed with which the set of markets reaches its steady-state configuration; and 2. The topology of the network will have a significant effect on the steady state configuration of the set of markets. The results in Table 1 suggest that the first of these hypotheses is correct — for most of the experiments that we carried out, the time we estimate it takes the set of markets to converge varies considerably from topology to topology.

3(a), Fig. 3(e)) and ring (Fig. 3(b), Fig. 3(f)) markets is pretty uniform. Chain markets, however, do not have the same symmetry, and this shows up in the distribution of traders. As Fig. 3(c) and Fig. 3(g) show, markets at the end of the chain end up with fewer traders than the markets in the middle of the chain. The effect of the loss of symmetry is even more marked in star markets, Here, as shown in Figures 3(d) and 3(h) the hub market in the star collects many more traders than the otherwise identical markets that are connected to it.

In addition, when competing in a series of auctions, bidders can learn from past bidding-action data and incorporate its predictions of other bidders’ behavior in forming their own bidding strategies that may be the best response to the expected auction outcome [13, 7]. Seller behavior studies are usually of smaller scope and smaller number than those on buyer behavior, as for many cases, only online market designers and operators, such as eBay, Amazon, and Overstock, have the capability and resources to conduct such investigations.

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