A Contextual-Based Framework for Opinion Formation

During opinion formation, interacting agents can be assumed to be engaging in learning and decision-making processes to satisfy their individual goals. These goals are determined by the agents’ preferences – which are often unknown, complex and unpredictable. Most opinion formation frameworks however, assume static preferences and fail to model practical situations where human preferences change. We propose a new framework to simulate the process of opinion formation under uncertainty and dynamism. Agents who are unaware of their implicit con-textual preferences utilize inverse reinforcement learning to compute reward functions that determines their preferences. Reinforcement learning is subsequently used to optimize the agents’ behavior and satisfy their individual goals. The novelty of our approach lies in its ability to capture uncertainty and dynamism in the agent’s preferences, which are assumed to be unknown initially. This framework is compared to a baseline method based on reinforcement learning, and results show its ability to per-form better under dynamic scenarios.