Predicting the outcome of Automated Negotiations using Machine Learning

Abstract

Knowing the outcome of an automated negotiation before it is terminated offers many advantages. It allows one to change their strategy to obtain a better result, it gives (multi-issue) negotiators insight into which negotiations are worth the effort and resources and which are better to terminate, and it can aid in the development of negotiating assistants. Despite these advantages, no research has yet been done on predicting negotiation outcomes. This thesis tackles the challenge of predicting the outcome of automated negotiations. We limit the scope of these predictions to bilateral alternating-offer negotiations between two time-dependent agents. Our method divides the prediction into two parts. First, the bids made by the negotiating agents are converted into utility time series. The future trajectory is then forecast by six time-series forecasting methods. Second, these forecasts are used to find a distribution of the most likely intersection points and, with that, a distribution of the most likely negotiation outcomes. In addition, the network gives the probability of ending the negotiation with an agreement. In general, neural networks performed best in both time series forecasting and outcome prediction, achieving an F1-score of 0.876. We found that negotiation time series are difficult to predict for classic statistical models, as the series have a low level of predictability. The predictability of time series can be improved by applying a behavior-based strategy, which we model by introducing a learning rate. Our work shows that neural networks are a promising direction for automated negotiation outcome prediction. We believe these results are only the beginning of their capabilities and can further be improved by experimenting with more network architectures and negotiation settings. Therefore, we encourage other researchers to take the next steps to improve our results.