Academic

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

arXiv:2603.02495v1 Announce Type: new 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 techn

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Avrim Blum, Keziah Naggita, Matthew R. Walter, Jingyan Wang · · 1 min read · 2 views
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arXiv:2603.02495v1 Announce Type: new 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 introduce a proxy welfare function that remains submodular even when revealed targets include negative ones. When each agent has at most a constant number of negative target neighbors, we use this proxy to achieve a constant-factor approximation to the true optimal welfare gain. When agents belong to different groups, we also show that each group's welfare gain is within a constant factor of the optimum achievable if the full budget were allocated to that group. Beyond this basic model, we also propose an intervention model that directly connects high-risk agents to positive role models, and a coverage radius model that expands the visibility of selected positive role models. Lastly, we conduct extensive experiments on four real-world datasets to support our theoretical results and assess the effectiveness of the proposed algorithms.

Executive Summary

This article presents a novel approach to maximizing social welfare in a setting where agents take action based on their role models in a social network. The social planner has a limited disclosure budget to reveal whether role models are positive or negative, encouraging or discouraging emulation. The authors develop algorithms and hardness results for welfare maximization, providing sample-complexity and fairness guarantees. They introduce a proxy welfare function to address submodularity issues and propose intervention and coverage radius models to expand the model's applicability. Experiments on four real-world datasets support the theoretical results. The proposed approach could be used to design more effective social interventions, but its practicality and scalability require further investigation.

Key Points

  • ▸ The article presents a new framework for social welfare maximization in a social network setting.
  • ▸ The authors develop algorithms and hardness results for welfare maximization under a limited disclosure budget.
  • ▸ A proxy welfare function is introduced to address submodularity issues when revealing negative role models.

Merits

Strength in theoretical contribution

The article makes significant theoretical contributions to the field of social network analysis, including the development of new algorithms and hardness results for welfare maximization.

Demerits

Limitation in practical applicability

The proposed approach assumes a high level of centralized control, which may not be feasible in real-world scenarios, and its scalability and practicality require further investigation.

Expert Commentary

The article presents a novel and technically sound approach to social welfare maximization in a social network setting. However, the authors' assumption of centralized control and lack of consideration for realistic constraints, such as communication costs and privacy concerns, limit the approach's practicality and scalability. Nevertheless, the theoretical contributions and experimental results demonstrate the potential of this approach to inform social interventions and policy design. Future research should focus on addressing these limitations and exploring the implications of this work for real-world applications.

Recommendations

  • ✓ Future research should investigate the feasibility and scalability of the proposed approach in real-world scenarios.
  • ✓ The authors should consider incorporating realistic constraints, such as communication costs and privacy concerns, into their model and algorithms.

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