Agent-Mediated Electronic Commerce. Designing Trading by Martin Chapman, Gareth Tyson, Katie Atkinson (auth.), Esther

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 learn 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 more than a few urgent new learn demanding situations, together with the layout of acceptable agent determination algorithms, techniques 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 make sure digital markets behave in a sturdy demeanour or satisfy different fascinating standards. Drawing upon a various variety of medical disciplines, together with desktop technological know-how, economics, man made intelligence, operations examine and online game conception, the papers amassed during this quantity symbolize a cross-section of contemporary learn and canopy subject matters akin to ideas for person buying and selling brokers, the layout of markets and interplay protocols among brokers, and various applications.

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L. A. 2 f 0 if bid <= reserve price, if bid > reserve price. (10) Price Estimation The second component of the developed mechanism is responsible for estimating the best price to offer. This problem can be approached either by using stochastic optimization [16] or greedy search [12]. In both cases the idea is that given an acceptance probability estimation, we search for the prices that will optimize the offering bundle under the given constraints. The former approach requires specialized software and large computational power for deriving near real-time solutions, so we turn out focus on the latter approach.

First, in most complex auctions, computing equilibria is intractable, making it difficult to predict agent strategies. Second, even if an equilibrium can be computed, bidders may not conform to that or any other equilibrium strategy. In such auctions, it is often best to support theoretical analyses (to the extent they are even possible) with corresponding experimental and empirical studies. Experimental economists often perform user studies in simulated auction games to determine whether bidders play equilibrium strategies [1, 2, 3].

Self-adaptive Strategies. The main advantage of Reinforcement Learning strategies is their utter independence from the other agents’ actions, and sometimes even from the market. Although this may seem ineffective, techniques such as Roth-Erev (RE) achieve significant performance, especially in sealed DAs, where the amount of information that is given to the agents is limited [3]. The RE agent has been designed to mimic human behavior. The agent’s propensity of making a move is updated at each round through an experience function, which is a reflection of the agent’s satisfaction (or disappointment) concerning the considered move.

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