[2603.02495] Revealing Positive and Negative Role Models to Help People Make Good Decisions

Computer Science > Artificial Intelligence arXiv:2603.02495 (cs) [Submitted on 3 Mar 2026] Title:Revealing Positive and Negative Role Models to Help People Make Good Decisions Authors:Avrim Blum, Keziah Naggita, Matthew R. Walter, Jingyan Wang View a PDF of the paper titled Revealing Positive and Negative Role Models to Help People Make Good Decisions, by Avrim Blum and Keziah Naggita and Matthew R. Walter and Jingyan Wang View PDF Abstract:We consider a setting where agents take action by following their role models in a social network, and study strategies for a social planner to help agents by revealing whether the role models are positive or negative. Specifically, agents observe a local neighborhood of possible role models they can emulate, but do not know their true labels. Revealing a positive label encourages emulation, while revealing a negative one redirects agents toward alternative options. The social planner observes all labels, but operates under a limited disclosure budget that it selectively allocates to maximize social welfare (the expected number of agents who emulate adjacent positive role models). We consider both algorithms and hardness results for welfare maximization, and provide a sample-complexity guarantee when the planner observes a sampled subset of agents. We also consider fairness guarantees when agents belong to different groups. It is a technical challenge that the ability to reveal negative role models breaks submodularity. We thus introduc...