Empathetic Decision Making in Social Networks


Social networks play a central role in the transactions and decision making of individuals by correlating the behaviors and preferences of connected agents. We introduce a notion of empathy in social networks, in which individuals derive utility based on both their own intrinsic preferences, and empathetic preferences determined by the satisfaction of their neighbors in the network. After theoretically analyzing the properties of our empathetic framework, we study the problem of group recommendation, or consensus decision making, within this framework. We show how this problem translates into a weighted form of classical preference aggregation (e.g., social welfare maximization or certain forms of voting), and develop scalable optimization algorithms for this task. Furthermore, we show that our framework can be generalized to encompass other multiagent systems problems, such as constrained resource allocation, and provide scalable iterative algorithms for these generalizations. Our empirical experiments demonstrate the value of accounting for empathetic preferences in group decisions, and the tractability of our algorithms.

In Artificial Intelligence (2019).