Predicting Concession Curves of Negotiating Agents Using Machine Learning

Accurate opponent modeling is critical for effective automated negotiation, enabling agents to adapt their strategies based on the type of opponent. This study investigates machine learning approaches for classifying negotiation agent strategies from offer sequences across three scenarios: time-dependent agents following predetermined concession functions, strategic agents adapting to opponent behavior with deadline-only termination, and strategic agents with realistic termination through mutual agreement or deadline expiration. We systematically evaluate four algorithms—Naive Bayes, Random Forest, Support Vector Machines, and Neural Networks— on a number of simulated negotiations, comparing classification performance with and without temporal feature augmentation. A key contribution of this work is the introduction of temporal feature augmentation, where quarterly concession patterns and variance metrics are used to capture adaptive negotiation behavior that raw offer sequences alone cannot reveal. The augmented features encode temporal adaptation characteristics that distinguish Boulware, Linear, Conceder, and strategic negotiation behaviors. Feature augmentation produced statistically significant improvements in 7 of 12 model–scenario combinations, with the most notable gains observed in strategic agent identification.