Personalized Prediction of Bounded-Rational Bargaining Behavior in Network Resource Sharing

There have been many studies leveraging bargaining to incentivize the sharing of network resources between resource owners and seekers. They predicted bargaining behavior and outcomes mainly by assuming that bargainers are fully rational and possess sufficient knowledge about their opponents. Our work addresses the prediction of bargaining behavior in network resource sharing scenarios where these assumptions do not hold, i.e., bargainers are bounded-rational and have heterogeneous knowledge. Our first key idea is using a multi-output Long Short-Term Memory (LSTM) neural network to learn bargainers' behavior patterns and predict both their discrete and continuous decisions. Our second key idea is assigning a unique latent vector to each bargainer, characterizing the heterogeneity among bargainers. We propose a scheme to jointly learn the LSTM weights and latent vectors from real bargaining data, and utilize them to achieve a personalized behavior prediction. We prove that estimating our LSTM weights corresponds to a special design of LSTM training, and also theoretically characterize the performance of our scheme. To deal with large-scale datasets in practice, we further propose a variant of our scheme to accelerate the LSTM training. Experiments on a large real-world bargaining dataset demonstrate that our schemes achieve more accurate personalized predictions than baselines.