# Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry

arXiv: https://arxiv.org/abs/2602.17787 · HTML: https://mlsanity.com/paper/2602.17787/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [ORPO: Monolithic Preference Optimization without Reference Model](https://mlsanity.com/paper/2403.07691/) (2024)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Generative Monoculture in Large Language Models](https://mlsanity.com/paper/2407.02209/) (2024)
- [Regression Equilibrium](https://mlsanity.com/paper/1905.02576/) (2019)
- [Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition](https://mlsanity.com/paper/2306.14670/) (2023)
- [Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?](https://mlsanity.com/paper/2402.15467/) (2024)
- [Non-linear Welfare-Aware Strategic Learning](https://mlsanity.com/paper/2405.01810/) (2024)
- [Addressing Polarization and Unfairness in Performative Prediction](https://mlsanity.com/paper/2406.16756/) (2024)
- [User Welfare Optimization in Recommender Systems with Competing Content Creators](https://mlsanity.com/paper/2404.18319/) (2024)
- [Self-Consuming Generative Models with Adversarially Curated Data](https://mlsanity.com/paper/2505.09768/) (2025)
- [Competing Bandits: Learning under Competition](https://mlsanity.com/paper/1702.08533/) (2017)
- [Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions](https://mlsanity.com/paper/2405.08027/) (2024)
- [Braess's Paradox of Generative AI](https://mlsanity.com/paper/2409.05506/) (2024)
- [A Market for Accuracy: Classification under Competition](https://mlsanity.com/paper/2502.18052/) (2025)

## Abstract (arXiv)

Generative model ecosystems increasingly operate as competitive multi-platform markets, where platforms strategically select models from a shared pool and users with heterogeneous preferences choose among them. Understanding how platforms interact, when market equilibria exist, how outcomes are shaped by model-providers, platforms, and user behavior, and how social welfare is affected is critical for fostering a beneficial market environment. In this paper, we formalize a three-layer model-platform-user market game and identify conditions for the existence of pure Nash equilibrium. Our analysis shows that market structure, whether platforms converge on similar models or differentiate by selecting distinct ones, depends not only on models' global average performance but also on their localized attraction to user groups. We further examine welfare outcomes and show that expanding the model pool does not necessarily increase user welfare or market diversity. Finally, we design novel best-response training schemes that allow model providers to strategically introduce new models into competitive markets.