Efficient Methods for Automated Multi-Issue Negotiation: Negotiating over a Two-Part Tariff

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win-win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win-win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy

Research on the Model of Making a Price Match Based-on Automatic Negotiated Price for Electronic Commerce

The paper established a new sealed bargaining mechanism based on the electronic business negotiation model and considering the opaqueness of information on demand and supply. Using the supply function and demand function to analyze the behavior rule during the course of the price change, in the paper we established and proved a series of intersecting chord theorems about concave supply function and demand function, thus we got a transaction mechanism of negotiating prices that manufacturers and distributors submitted the supply and demand according to node gradually recursion algorithm after the first offer made by the e-commerce platform, And proved the negotiated price converged to the equilibrium price of supply and marketing

Improving Learning Performance by Applying Economic Knowledge

Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their position as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.

An Overview of Information Goods Pricing

[[abstract]]Although information economy has been the focus of considerable research, no unified and exhaustive classification model for current pricing methods exists. This work presents a novel unifying pricing framework. Each category in the framework is defined by the structural elements that accounts for its behaviour and particular aims. This work also identifies the implicit joints among categories as the basis for optimising prices (only in terms of different perspectives). The benefits of the unifying framework are that it provides a conceptual abstract model that differentiates between different pricing methods, and positions the future effectual pricing methods.[[journaltype]]國

Improving Learning Performance by Applying Economic Knowledge

Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their positions as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or by introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.http://deepblue.lib.umich.edu/bitstream/2027.42/50435/1/improving-amec03-lncs04.pd

Dynamic pricing with limited competitor information in a multi-agent economy

Abstract. We study the price dynamics in a multi-agent economy consisting of buyers and competing sellers, where each seller has limited information about its competitors ’ prices. In this economy, buyers use shopbots while the sellers employ automated pricing agents or pricebots. A pricebot resets its seller’s price at regular intervals with the objective of maximizing revenue in each time period. Derivative following provides a simple, albeit naive, strategy for dynamic pricing in such a scenario. In this paper, we refine the derivative following algorithm and introduce a model-optimizer algorithm that re-estimates the priceprofit relationship for a seller in each period more efficiently. Simulations using the model-optimizer algorithm indicate that it outperforms derivative following even though it does not have any additional information about the market. Our results underscore the role machine learning and optimization can play in fostering competition (or cooperation) in a multi-agent economy where the agents have limited information about their environment