Through the Telescope: UCITA and the Future of E-Commerce

Symposium: The New World of Intellectual Propert

Multiple Issue Action and Market Algorithms for the World Wide Web

The Internet is quickly changing the way business-to-consumer and business-to-business commerce is conducted in the world. The Electronic Revolution has also spawned a trend of price wars and, in some instances, chaos because of the zero-sum nature of the electronic channel. The technology has created an opportunity to get beyond the lose-lose nature of single issue price wars by determining sellers' and buyers' preferences across multiple issues and encouraging negotiations, thereby creating possible joint gains for all parties. We develop simple multiple issue algorithms and heuristics that could be used in electronic auctions and electronic markets, to match business to business and consumers based on dovetailing underlying interests and preferences. We provide arguments that such dovetailed matches should help stabilize markets and make them more efficient

Human-Agent Negotiations: The Impact Agents’ Concession Schedule and Task Complexity on Agreements

Employment of software agents for conducting negotiations with online customers promises to increase the flexibility and reach of the exchange mechanism and reduce transaction costs. Past research had suggested different negotiation tactics for the agents, and had used them in experimental settings against human negotiators. This work explores the interaction between negotiation strategies and the complexity of the negotiation task as represented by the number of negotiation issues. Including more issues in a negotiation potentially allows the parties more space to maneuver and, thus, promises higher likelihood of agreement. In practice, the consideration of more issues requires higher cognitive effort, which could have a negative effect on reaching an agreement. The results of human–agent negotiation experiments conducted at a major Canadian university revealed that there is an interaction between chosen strategy and task complexity. Also, when competitive strategy was employed, the agents\u27 utility was the highest. Because competitive strategy resulted in fewer agreements the average utility per agent was the highest in the compromising–competitive strategy

Human-Computer Negotiations: A Systematic Evaluation of the Effects of Timespan, Tactic, and Search Mechanism

Artificial Intelligence and Computer Agents are having a substantial impact on our everyday lives. The current paper focuses on the prospects of humans negotiating with computer agents in e-commerce settings. We conducted experiments where the subjects negotiated the purchase of mobile plans with computer agents acting as sellers. Three time-based negotiation tactics and two search mechanisms were employed in synchronous vs. asynchronous sessions. The results suggest that computer agents’ negotiation tactics and search mechanisms have significant effects on both the subjective and objective outcomes of the negotiations, while timespan has marginal effects on the agreement rate of the negotiation

Electronic marketplace

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaf 34).by David Yi Wang.M.Eng

The WTO and market-supportive regulation: a way forward on new competition, technological and labor issues

World Trade Organization

[[alternative]]An Interactive Negotiation Agent Based on Multi-Attribute Utility Theory for Internet Commerce

計畫編號：NSC91-2213-E032-020研究期間：200208~200307研究經費：612,000[[sponsorship]]行政院國家科學委員

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing