mlsanityData to 14 Oct 2026

The Battling Influencers Game: Nash Equilibria Structure of a Potential Game and Implications to Value Alignment

arXiv 2502.01127 ↗First posted 3 Feb 2025cs.GT cs.AIcited by 1

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Abstract (arXiv)

When multiple influencers attempt to compete for a receiver's attention, their influencing strategies must account for the presence of one another. We introduce the Battling Influencers Game (BIG), a multi-player simultaneous-move general-sum game, to provide a game-theoretic characterization of this social phenomenon. We prove that BIG is a potential game, that it has either one or an infinite number of pure Nash equilibria (NEs), and these pure NEs can be found by convex optimization. Interestingly, we also prove that at any pure NE, all (except at most one) influencers must exaggerate their actions to the maximum extent. In other words, it is rational for the influencers to be non-truthful and extreme because they anticipate other influencers to cancel out part of their influence. We discuss the implications of BIG to value alignment.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 3 Feb 2025.

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17 of 24 references.

References · 17

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  3. 2021Convex optimizationcited by 2,515
  4. 2023Whose Opinions Do Language Models Reflect?cited by 773
  5. 2023Nash Learning from Human Feedbackcited by 224
  6. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  7. 2024Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferencescited by 178
  8. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 144
  9. 2024Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedbackcited by 100
  10. 2024RLHF from Heterogeneous Feedback via Personalization and Preference Aggregationcited by 74
  11. 2024PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferencescited by 54
  12. 2024Truthful Aggregation of LLMs with an Application to Online Advertisingcited by 29
5 more
  1. 2024Mechanism Design for LLM Fine-tuning with Multiple Reward Modelscited by 18
  2. 2017Online Prediction with Selfish Expertscited by 10
  3. 2020Simultaneous Preference and Metric Learning from Paired Comparisonscited by 10
  4. 2024Online Learning from Strategic Human Feedback in LLM Fine-Tuningcited by 10
  5. 2016Actively Learning Hemimetrics with Applications to Eliciting User Preferencescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AI Alignment through a Game-theoretic Lens: A Surveycited by 0