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[2602.17787] Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry
Summary
This paper explores market dynamics in generative model ecosystems, focusing on equilibria, welfare implications, and strategic entry by model providers and platforms.
Why It Matters
Understanding the competitive landscape of generative models is crucial for developers and researchers to foster effective market environments. Insights from this study can inform strategies for model deployment and user engagement, ultimately influencing the development of AI technologies.
Key Takeaways
- Platforms in generative model markets can achieve pure Nash equilibria under specific conditions.
- Market structure is influenced by both global model performance and localized user preferences.
- Expanding the model pool does not guarantee increased user welfare or market diversity.
- Strategic model introduction can enhance competitive dynamics in AI ecosystems.
- Understanding user behavior is essential for optimizing model selection by platforms.
Computer Science > Computer Science and Game Theory arXiv:2602.17787 (cs) [Submitted on 19 Feb 2026] Title:Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry Authors:Xiukun Wei, Min Shi, Xueru Zhang View a PDF of the paper titled Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry, by Xiukun Wei and 2 other authors View PDF HTML (experimental) Abstract: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 ...
