Kephart, J. and A. Greenwald, "Shopbot Economics," Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, (July, 1999).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....may have different strategies for selecting the seller, ranging from random to the selection of the cheapest seller on the market (bargain hunters) while the sellers use the same pricing strategy. A similar model but with populations of sellers using different strategies has been studied in [14, 15, 16] The pricing problem can be viewed as a very simple instance of our topic selection task (namely, as a single topic case with some modifications to the performance model) 7 Conclusions and Future Work Heterogeneous search environments provide access to arguably much larger volumes of ....

J. O. Kephart and A. R. Greenwald, "Shopbot economics," in Proceedings of the 5th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU-99) (A. Hunter and S. Parsons, eds.), vol. 1638 of LNAI, (Berlin), pp. 208--220, Springer, July 5--9 1999.

....the effects of different AI techniques, such as planning and coordination, on the InfoCenter decision process. 2. RELATED WORK Researchers at IBM have suggested the concept of an Information Economy as the context in which humans and automatic agents could find and trade information over the Web [5, 4]. These researchers focus on two kinds of agents: ShopBors and PriceBors. The ShopBot agent compares prices for the buyer and helps find the lowest price for the information the buyer needs. The PriceBot agent helps the seller set prices for the information commodities it offers. Our study is ....

....that includes buyers and sellers interacting with InfoCenter agents that can obtain manipulated information fTom InfoSP agents. Figure 1: A basic e marketplace including InfoCenter and InfoSP agents 4. THE MODEL Our study is based on the same marketplace model pro posed by Kephart et al. [5, 4]. This marketplace contains commodities that are offered by S sellers, and which may be bought by any of the B buyers, assuming B . Each buyer generates purchase orders at random times, at a rate of Pb, while each seller resets his price at random times, at a rate of p, The worth of a good to ....

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J. O. Kephart and A. Greenwald. Shopbot economics. In Proceedings of the Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, London, UK, July 1999.

....seller uses available information about the market, such as the distribution of buyer preferences, or its competitor s prices. There has been recent work in the literature which attempt to address the question of automated dynamic pricing assuming more or less complete information about the market [6, 8, 12]. But what if the seller has only limited information about its environment In our earlier work, we have explored how a monopolistic seller might dynamically set its price schedule to maximize profit in a market where it has to learn the buyer preferences [2, 7] In this work, we study markets ....

....the interacting dynamics and the performance of agents employing the different pricing strategies through simulations. We conclude with an outline of the directions for future research. 2 Model We study the price dynamics in a simple model of the shopbot economy proposed by Kephart and Greenwald [6, 8, 9]. In this model, the market consists of S sellers who compete to provide B buyers (B S) with a single indivisible commodity, such as a specific book. Each buyer has a valuation pm corresponding to the maximum unit price it is willing to pay. Prior to purchase, each buyer samples the market ....

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J. O. Kephart and A. R. Greenwald. Shopbot economics. In Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 1999.

....that download a small portion of the Web site (for pre caching purposes) resemble the characteristics of human users. Table 2 summarizes the characteristics and navigational goals of several types of Web robots. Other types of Web robots include personal browsing assistants [2, 16] shopbots [11, 3], resume hunters and other special purposed software agents. 2.2 Common Robot Detection Techniques In this section, we will present some of the common techniques used to identify Web robot sessions: 1. By examining sessions that access a specially formatted le called robots.txt The Robot ....

J. Kephart and A. Greenwald. Shopbot economics. In Agents, 1999.

....missing pages [9, 5] Many of these utilities use the HEAD method to request information about a particular Web page. Unlike the GET method, the response message to a HEAD request contains only the Web page meta information and does not involve a full transfer of the Web document. Kephart et al.[14] de ned shopbots as programs that automatically search for information regarding the price and quality of goods or services on behalf of a consumer. The deployment of shopbots has received mixed reactions from di erent vendors. Some vendors attempt to block accesses by these robots while others ....

J. Kephart and A. Greenwald. Shopbot economics. In Agents, 1999.

....on reference links that will lead them to documents of interest. Hyperlink checkers are Internet utility programs used by Web site administrators to test for broken links or missing pages [9, 5] Many of these utilities use the HEAD request method to test for the validity of a link. Kephart et al.[14] de nes shopbots as programs that automatically search for information regarding the price and quality of goods or services on behalf of a consumer. The deployment of shopbots has received mixed reactions from di erent vendors. Some vendors attempt to block accesses by these robots while others ....

J. Kephart and A. Greenwald. Shopbot economics. In Agents, 1999.

.... informational and computational demands: game theoretic pricing (GT) myoptimal pricing (MY) derivative following (DF) and no regret learning (NR) Previously, we studied the dynamics that ensue when shopbot assisted buyers interact with pricebots utilizing only a subset of these strategies [19, 25, 29]. In this work, we simulate additional, more sophisticated, pricebot strategies, and nd that the game theoretic equilibrium can arise dynamically as the outcome of adaptive learning in our model of shopbots and pricebots. This paper is organized as follows. The next section presents our model of ....

....is greater than vb . The relationship between models of exogenously determined buyer behavior and the endogenous approach which incorporates the cost of information acquisition and explicitly allows for buyer decision making is further explored in computational settings in Kephart and Greenwald [25]; in the economics literature, see, for example, Burdett and Judd [5] and Wilde and Schwartz [33] A seller s s expected pro t per unit time s is a function of the price vector p, as follows: s (p) p s c s )D s (p) where D s (p) is the rate of demand for the good produced by seller s. ....

J.O. Kephart and A.R. Greenwald. Shopbot economics. In Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 208-220, July 1999.

.... manually, just trying a random seller and buying if the price is right (s = 1) while the remaining 75 may use a shopbot to search all sellers prices (s = S) Alternatively, if buyers set their search strategies 4 For a more complete presentation and study of the model, see references [ 10, 11, 17 ] 6 so as to minimize the expected cost of the good plus the cost of the searching, endogeneous forces may cause w to evolve over time. In the next three subsections, we explore a few points in a broad spectrum of possible utility maximization strategies that sellers, buyers, or shopbot ....

....their performance on systems with three sellers. 14 3.3 Buyers and their search strategies In the information economy, buyer agents will also make strategic choices based on economic considerations. We have explored economic decision making by buyers within the context of the shopbot model [ 17 ] Suppose that there is a cost c s for obtaining s price quotes. This might represent an intrinsic, implicit cost that reflects the time and effort required to obtain the quotes, or it may represent a real fee paid to a shopbot. Then a rational buyer b would not blindly adhere to a fixed search ....

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J. O. Kephart and A. R. Greenwald. Shopbot economics. In Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, July 1999.

.... informational and computational demands: game theoretic pricing (GT) myoptimal pricing (MY) derivative following (DF) and no regret learning (NR) Previously, we studied the dynamics that ensue when shopbot assisted buyers interact with pricebots utilizing only a subset of these strategies [19, 25, 29]. In this work, we simulate additional, more sophisticated, pricebot strategies, and find that the game theoretic equilibrium can arise dynamically as the outcome of adaptive learning in our model of shopbots and pricebots. This paper is organized as follows. The next section presents our model ....

....is greater than vb . The relationship between models of exogenously determined buyer behavior and the endogenous approach which incorporates the cost of information acquisition and explicitly allows for buyer decision making is further explored in computational settings in Kephart and Greenwald [25]; in the economics literature, see, for example, Burdett and Judd [5] and Wilde and Schwartz [33] A seller s s expected profit per unit time s is a function of the price vector p, as follows: s (p) p s Gamma c s )D s (p) where D s (p) is the rate of demand for the good produced by seller ....

J.O. Kephart and A.R. Greenwald. Shopbot economics. In Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pages 208--220, July 1999.

.... and computational needs: game theoretic pricing (GT) myoptimal pricing (MY) derivative following (DF) and Q learning (Q) Previously, we studied the price and profit dynamics that ensue when shopbotassisted buyers interact with homogeneous collections of pricebots that utilize these algorithms [9, 10, 15]. In this work, we first establish that our previous results are not significantly altered when buyers valuations are inhomogeneous rather than identical. Later, we examine the behavior that ensues when pricebots employing different strategies are pitted against one another. This paper is ....

....that seller s price is less than the buyer s valuation v b . Price ties are broken randomly. A few special cases are worth mentioning. A buyer of type i = 0 simply opts out of the market without checking any prices. Buyers of types i = 1, i = 2, and i = S have been referred to in previous work [9, 10] as employing the Any Seller, Compare Pair and Bargain Hunter strategies, respectively; the latter corresponds to buyers who take advantage of shopbots. 1 The buyer population is assumed to consist of a mixture of buyers employing one or another of these strategies. Specifically, a fixed, ....

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J. Kephart and A. Greenwald. Shopbot economics. In Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, To Appear, July 1999.

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Kephart, J. and A. Greenwald, "Shopbot Economics," Proceedings of Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, (July, 1999).

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Kephart JO, Greenwald A. Shopbot economics. Proceedings of 5th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty; London, UK; Jul 6, 1999. Available at IBM Research, Information Econo- mies Project Web site, at: http://www.research.ibm.com/ infoecon/researchpapers.html.

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J. Kephart and A.R. Greenwald, Shopbot Economics. In Proceedings of the Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty

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