Jeffrey O. Kephart, Christopher H. Brooks, Rajarshi Das, Jeffrey K. MacKie-Mason, Robert S. Gazzale, and Edmund H. Durfee. Pricing information bundles in a dynamic environment. In Proceedings of the 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to take the opportunity of making the purchase at a lower total price. Merchants can create additional return by rising sales on low searched products, besides calling attention away from their competitors and into themselves. There has been some research on bundling information goods (e.g. [2, 9]) These are characterized by a high fixed (first copy) cost and a negligible marginal cost, which makes bundling an appealing strategy. 3.2 Demand Aggregation The aggregation of demand is a powerful tool, because it can create great opportunities to buyers and thus be a very successful ....

Kephart, J. O., Brooks, C. H. and Das, R. (2001). Pricing Information Bundles in a Dynamic Environment, in Proceedings of the ACM Conference on Electronic Commerce (EC '01), October 2001.

....quantity and or quality of the purchased good (cf. 16] The distinguishing aspect of second degree price discrimination is that customers can self select the best purchase. Traditionally, customers are o#ered a menu of options where a tari# assigns a price to the option in the menu. The work of [3, 9] discusses algorithms which given a particular tari # structure learn the best tari#s on line. They conclude that (especially in a dynamic environment) complex tari#s are generally not the most profitable strategy. The distinguishing aspect of the developed system is that instead of having ....

J. O. Kephart, C. H. Brooks, and R. Das. Pricing information bundles in a dynamic environment. In Proceedings of the 3rd ACM Conference on Electronic Commerce, pages 180--190. ACM Press, 2001.

....at Agent Mediated Electronic Commerce IV (AMEC IV) a workshop of AAMAS 2002, Bologna. 1. Introduction Electronic recommender systems, electronic auctions, and virtual shops are increasing in economic signi cance [ Dailianas et al. 2000; Dellarocas and Klein, 2000; Yoona et al. 2000; Kephart et al. 2001 ] As these systems grow in size and increase in the number of customers, the problem of how to match customers correctly to providers increases in complexity and cost. This is especially the case when using centralized electronic applications as large knowledge bases and communication overhead ....

....Gaussian that determines the rate of drop with distance. However, any bell shaped function would be realistic. Note that we assume that all customers have an identical chance of purchase function. Furthermore, the buying behavior of the customers is static, as is quite common in customer modeling [ Kephart et al. 2001 ] In the prototype, we integrate the knowledge of the shape of the function of the chance of purchase by a customer; the shop negotiator knows that this is a Gaussian, a bell shaped curve dependent on the distance in pro le space. Furthermore, the shop negotiators are provided with some upper ....

J. O. Kephart, Christopher H. Brooks, and Rajarshi Das. Pricing information bundles in a dynamic environment, 2001. Third ACM Conference on Electronic Commerce Tampa, Florida, USA.

....presented at Agent Mediated Electronic Commerce IV (AMEC IV) a workshop of AAMAS 2002, Bologna. 1. INTRODUCTION Electronic recommender systems, electronic auctions, and virtual shops are increasing in economic significance [Dailianas et al., 2000; Dellarocas and Klein, 2000; Yoona et al., 2000; Kephart et al., 2001]. As these systems grow in size and increase in the number of customers, the problem of how to match customers correctly to providers increases in complexity and cost. This is especially the case when using centralized electronic applications as large knowledge bases and communication overhead can ....

....Gaussian that determines the rate of drop with distance. However, any bell shaped function would be realistic. Note that we assume that all customers have an identical chance of purchase function. Furthermore, the buying behavior of the customers is static, as is quite common in customer modeling [Kephart et al. 2001]. In the prototype, we integrate the knowledge of the shape of the function of the chance of purchase by a customer; the shop negotiator knows that this is a Gaussian, a bell shaped curve dependent on the distance in profile space. Furthermore, the shop negotiators are provided with some upper ....

J. O. Kephart, Christopher H. Brooks, and Rajarshi Das. Pricing information bundles in a dynamic environment, 2001. Third ACM Conference on Electronic Commerce Tampa, Florida, USA.

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Jeffrey O. Kephart, Christopher H. Brooks, Rajarshi Das, Jeffrey K. MacKie-Mason, Robert S. Gazzale, and Edmund H. Durfee. Pricing information bundles in a dynamic environment. In Proceedings of the 2001.

....are rational utility maximizers. That is, they know their preferences and, on every iteration, will act so as to maximize their expected utility. We assume consumer preferences follow a simple two parameter model originally introduced by Chuang and Sirbu [5] and described in our previous work [4, 13]. This model has the advantage of being analytically tractable while still providing significant nonlinearities in consumer demand when consumers are heterogeneous. The model consists of two parameters: which indicates a consumer s value for its most preferred article, and , which indicates ....

Jeffrey O. Kephart, Christopher H. Brooks, Rajarshi Das, Jeffrey K. MacKie-Mason, Robert S. Gazzale, and Edmund H. Durfee. Pricing information bundles in a dynamic environment. In Proceedings of the 2001.

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