arXiv:2606.28710v1 Announce Type: new Abstract: We ask under what conditions an agent with a harm-minimizing policy can displace an approval-seeking (RLHF) agent in a competitive market, and when that policy is sufficient to prevent community harm. We use evolutionary game theory (finite-population Moran-Fermi pairwise comparison) to formalize this subject to assumptions of wisher hindsight, peer testimony, a monotone harm ledger, sufficient information density of community feedback, and a finite, depleting resource pool, in a negative-sum environment. We show that adoption is favored when the
The increasing focus on AI alignment, safety, and governance, especially with powerful autonomous agents, necessitates theoretical frameworks to understand their market behavior and societal impact.
This research provides a foundational theoretical model for how audit-grounded, harm-minimizing AI might compete with and potentially displace approval-seeking AI, which has significant implications for AI market dynamics and public welfare.
The debate around AI governance moves from purely ethical considerations to a more formal, game-theoretic understanding of adoption mechanisms and policy efficacy in competitive AI ecosystems.
- · AI ethicists and governance researchers
- · Developers of harm-minimizing AI policies
- · Regulatory bodies focused on AI safety
- · AI developers focused solely on short-term approval/performance
- · Unregulated AI markets
The adoption of audit-grounded AI becomes theoretically predictable under specific market conditions.
Increased investment and research into 'harm-minimizing' AI architectures and competitive strategies.
Potential for new AI certification standards based on resilience to displacement by harm-minimizing agents envisioned in such models.
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Read at arXiv cs.AI