2010.00403
Mediating artificial intelligence developments through negative and positive incentives
The Anh Han, Luís Moniz Pereira, Tom Lenaerts, Francisco C. Santos
correctmedium confidence
- Category
- Not specified
- Journal tier
- Strong Field
- Processed
- Sep 28, 2025, 12:55 AM
- arXiv Links
- Abstract ↗PDF ↗
Audit review
The model’s derivations match the paper’s logic and thresholds for the baseline (AS vs AU), reward (RS vs AU), and the limiting punishment case sα ≥ s. For punishment with sα < s, both follow the same large-B risk-dominance inequality; the paper then states Eq. (7) as pr > 1 − 1/(s + 2Wr), but the immediately preceding algebra in the paper itself yields pr > 1 − 1/(s + 2W/r). This appears to be a notational/typographical slip in Eq. (7). The candidate derives the correct form but attributes the discrepancy to a redefinition of r, which is unnecessary. Aside from this minor notation bug, the proofs are the same in substance and reach the same thresholds.
Referee report (LaTeX)
\textbf{Recommendation:} minor revisions
\textbf{Journal Tier:} strong field
\textbf{Justification:}
The analytical framework is well-motivated and delivers crisp, interpretable thresholds with clear policy interpretations. Numerical results back the analytics. The only substantive issue is a minor notational/typographical error in Eq. (7) that could mislead readers; fixing this and clarifying the definition of r will resolve the discrepancy. Otherwise, the work is methodologically sound and sufficiently novel within the AI governance modeling literature.