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

Researchers present a framework for social planners to strategically reveal positive and negative role models to influence agent behavior in social networks. The study addresses optimization challenges when disclosure budgets are limited and proposes algorithms to maximize social welfare while maintaining fairness across different groups.

• →Social planners can optimize welfare by selectively revealing positive role models to encourage emulation and negative ones to redirect behavior.
• →The research introduces a proxy welfare function that maintains submodularity despite the complexity of revealing negative role models.
• →Algorithms achieve constant-factor approximation to optimal welfare gain when agents have limited negative neighbors.
• →The framework includes fairness guarantees ensuring each demographic group's welfare remains within constant factor of optimal allocation.
• →Experimental validation on four real-world datasets demonstrates the effectiveness of proposed intervention strategies.