Pairwise issue modeling for negotiation counteroffer prediction using neural networks

a r t i c l e i n f o Electronic negotiation systems can incorporate computational models and algorithms in order to help negotiators achieve their objectives. An important opportunity in this respect is the development of a component, which can assess an expected reaction by a counterpart to a given trial offer before it is submitted. This work proposes a pairwise modeling approach that provides the possibility of developing flexible and generic models for counteroffer prediction when the negotiation cases are similar. The key feature is that each negotiated issue is predicted while paired with each of the other issues and the permutations of issue pairs across all negotiation offers are confounded together. This data fusion permits extractions of common relationships across all issues, resulting in a type of pattern fusion. Experiments with electronic negotiation data demonstrated that the model's predictive performance is equivalent to case-specific models while offering a high degree of flexibility and generality even when predicting to a new issue. Business negotiations are an important type of exchange mechanism. The competency in conducting negotiations critically affects long-term business relationships, profitability, and reputations of businesses. Due to the rise of e-business, electronic negotiations have gained heightened importance lately [29,32]. Electronic negotiations systems, which are web-based successors of negotiation support systems [27], allow parties located in various parts of the world to seek mutually acceptable agreements by exchanging offers over the networks in a structured or unstructured fashion. The organic involvement of the digital medium in these exchanges provides new opportunities for employing support and automation tools, such as preference modeling and software agents, for promoting effective decision-making. The purpose of this paper is to investigate the feasibility of developing a generalized approach for empirically modeling an opponent's future offers. Consequently, the main contribution of this work is a pairwise modeling approach that is flexible with respect to the set of issues in a negotiation case and even to new unseen issues. In others words, the model has inputs and outputs that are independent of the particular issues of a specific negotiation case. The approach is tested using data obtained from electronic negotiation experiments, which provide a rich source of information about the relationships between negotiators, their individual actions, and the negotiation dynamics. Advanced negotiation support tools equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics, can effectively utilize this …