# Coordinated incentives in AI-generated misinformation governance

arXiv: https://arxiv.org/abs/2608.07070 · HTML: https://mlsanity.com/paper/2608.07070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services](https://mlsanity.com/paper/2303.16129/) (2023)
- [StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing](https://mlsanity.com/paper/2208.13423/) (2022)
- [Optimizing Resource Allocation for Multi-modal Semantic Communication in Mobile AIGC Networks: A Diffusion-based Game Approach](https://mlsanity.com/paper/2409.17506/) (2024)
- [Reward Mechanism for Blockchains Using Evolutionary Game Theory](https://mlsanity.com/paper/2104.05849/) (2021)
- [Spatial public goods games with queueing and reputation](https://mlsanity.com/paper/2505.09154/) (2025)
- [The evolution of cooperation in spatial public goods game with tolerant punishment based on reputation threshold](https://mlsanity.com/paper/2412.17351/) (2024)
- [Open Data in the Digital Economy: An Evolutionary Game Theory Perspective](https://mlsanity.com/paper/2502.05604/) (2025)

## Abstract (arXiv)

With the rapid diffusion of AI-generated content, AI-driven misinformation is becoming increasingly pervasive and difficult to govern, undermining information credibility and social trust. This study models the strategic interdependence among a government regulator, an AI enterprise, and users through a three-party evolutionary game that incorporates heterogeneous rewards and punishments. From the resulting replicator equations, we characterize the evolutionary stability of competing governance and production strategies. The analysis indicates that neither unilateral regulation nor market incentives alone can effectively curb misinformation. Instead, an evolutionarily stable regime of real-information production arises only when regulatory rewards and punishment intensity, enterprise reputation loss, and user adoption incentives collectively surpass critical thresholds. The findings highlight the need for coordinated and adaptive policy mixes that align regulatory instruments with enterprise behavior and user uptake while managing governance costs.