NDLI: Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

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Online learning of aggregate knowledge about non-linear preferences applied to negotiating prices and bundles

Content Provider	ACM Digital Library
Author	La Poutré, J. A. Somefun, D. J. A. Klos, T. B.
Abstract	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 have non-linear 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.
Starting Page	361
Ending Page	370
Page Count	10
File Format	PDF
ISBN	1581139306
DOI	10.1145/1052220.1052266
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-03-25
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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