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Modeling the Economic Impacts of AI Openness Regulation
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Builds a stylized game-theoretic model of a generalist choosing model openness and a specialist fine-tuning under an open-source threshold and penalty, deriving equilibria and regulatory regions (indifference curve, Pareto improvements).
Problem
Open-source definitions for general-purpose AI models in regulation (e.g., EU AI Act) are ambiguous, and it is unclear how thresholds and penalties affect developers' release openness and downstream fine-tuning investments.
Approach
Models openness as a continuous decision variable for a generalist facing an open-source threshold and penalty, with a specialist who fine-tunes the model and a bargained revenue share; closed-form subgame-perfect equilibria are derived and used to map regulatory outcomes across penalty and threshold combinations.
Key results
- Generalist openness equilibrium depends on baseline performance: low alpha_0 yields partial openness, high alpha_0 yields full closure under no regulation.
- Indifference curve p = theta(delta + alpha_0/alpha_1 - epsilon - c_omega) alpha_1 separates full closure from compliance regions.
- For low initial performance (alpha_0=0.1), some regulatory settings yield Pareto improvements over generalist and specialist utilities and openness and performance.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the open-source threshold below the compliance boundary | Generalist-specialist equilibrium with low baseline performance (α0=0.1) · Regulation profiles (p, θ) | joint utility (UG, UD) | Helped |
| Increase the open-source threshold below the indifference curve | Generalist-specialist equilibrium with high baseline performance (α0=1) · Regulation profiles (p, θ) | specialist performance (α1) | Helped |
| Increase the open-source threshold below the indifference curve | Generalist-specialist equilibrium with high baseline performance (α0=1) · Regulation profiles (p, θ) | generalist utility (UG) | Hurt |
| Raise the penalty or threshold above the compliance boundary | Generalist-specialist equilibrium · Regulation profiles (p, θ) | generalist openness | No clear effect |
| Raise the open-source threshold beyond the point that preserves participation | Generalist-specialist equilibrium · Overly stringent regulation | joint utility (UG, UD) | Hurt |
| Impose the same regulatory penalty on the specialist as on the generalist (ablation) | Generalist-specialist equilibrium · Regulation profiles (p, θ), outside abstention regions | qualitative equilibrium outcomes | No clear effect |
| Impose a specialist-side regulatory penalty (ablation) | Generalist-specialist equilibrium · Closed models that fail to meet the threshold | specialist participation | Hurt |
| Use logarithmic rather than linear production costs for the generalist (ablation) | Generalist-specialist equilibrium · Low baseline performance (α0=0.1) | generalist utility (UG) | Helped |
| Use logarithmic rather than linear production costs for the generalist (ablation) | Generalist-specialist equilibrium · Regulation profiles (p, θ) | qualitative equilibrium outcomes | No clear effect |
| Make the generalist's operation costs depend on baseline performance α0 rather than final performance α1 (ablation) | Generalist-specialist equilibrium · Equilibrium outcomes across cω ∈ {0.01, 0.1, 0.5, 1} | qualitative equilibrium outcomes | No clear effect |
| Make the generalist's operation costs depend on baseline performance α0 rather than final performance α1 (ablation) | Generalist-specialist equilibrium · Equilibrium outcomes | generalist utility (UG) | Helped |
Surprising findings
- Regulation above the indifference curve causes deadweight loss: penalties lower both players' utilities without openness gains.
- Generalist chooses intermediate openness when the bargained revenue share strongly favors it, rather than fully open or fully closed.
Limitations
- Stylized model abstracts from behavioral anchoring and multi-firm competition with closed-source incumbents.
- Analysis focuses on Nash bargaining with alternative solutions only as robustness; non-standard bargaining is not modeled.
- Performance is a coarse one-dimensional measure and openness is collapsed to a single scalar.
Experimental setup
Metrics: generalist utility specialist utility openness level fine-tuned performance
Baselines: Xu et al. fine-tuning game
Builds on: Laufer et al. fine-tuning games Xu et al. fine-tuning games Nash bargaining solution
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