Network Games with Strategic Machine Learning.

In this paper, we study the strategic machine learning problem with a planner (decision maker) and multiple agents. The planner is the first-mover, who designs, publishes, and commits to a decision rule. The agents then best-respond by manipulating their input features to obtain a desirable decision outcome so as to maximize their utilities. Earlier works in strategic machine learning assume that every agent’s strategic action is independent of others’. By contrast, we consider a different case where agents are connected in a network and can either benefit from their neighbors’ positive decision outcomes from the planner or benefit from their neighbors’ actions. We study the Stackelberg equilibrium in this new setting and highlight the similarities and differences between this model and the literature on network/graphical games and strategic machine learning.