Pairwise issue modeling for negotiation counteroffer prediction using neural networks

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.