Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles

TL;DR

This paper introduces an online learning approach that combines aggregate customer preference data with real-time negotiation information to improve bargaining efficiency and deal quality in multi-issue negotiations involving bundles.

Contribution

It presents a novel procedure that learns and utilizes aggregate preferences online, enhancing negotiation outcomes for non-linear customer preferences.

Findings

Increases speed of reaching deals

Improves number of Pareto-efficient deals

Effective with various customer heuristics

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_nonlinear_ 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.