2004/12/23 by Koye Somefun, Somefun, Koye, Tomas Klos +3
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Game Theory and Applications #Game Theory and Voting Systems #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #cs.GT #cs.LG #cs.MA
paper · pdf · doi:10.48550/arxiv.cs/0412106
10 pages, 5 eps figures, ACM Proceedings documentclass, Published in "Proc. 6th Int'l Conf. on Electronic Commerce ICEC04, Delft, The Netherlands," M. Janssen, H. Sol, R. Wagenaar (eds.). ACM Press
arxiv created 2004/12/23 · openalex publication_date 2004/12/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that havenonlinear_ preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.