One way to address the risk of catastrophic bargaining failures—where advanced AI systems and humans (or AI systems themselves) end up in conflicts with no room for compromise—could be to develop practical frameworks and tools based on game theory, decision theory, and governance principles. Unlike traditional AI safety efforts that focus on aligning AI goals with human intentions, this approach would emphasize designing systems that incentivize cooperation, even when power imbalances exist.

Key Components of the Framework

The idea revolves around three core elements:

• Game Theory: Mechanisms like credible commitments, side payments, or iterative bargaining to discourage AI systems from acting against human interests.
• Decision Theory: Protocols that allow AI systems to reason about uncertainty and long-term consequences before making irreversible choices.
• Governance Structures: Policies or institutions that enforce rules, such as transparency measures and audits, to prevent unilateral power grabs.

Outputs could include a simulation platform for testing negotiation scenarios between AI agents, a checklist for developers to embed cooperative features, and policy guidelines for regulators.

Potential Applications and Stakeholders

This idea could be particularly useful for:

• AI developers, who might adopt such frameworks to avoid reputational damage or regulatory backlash.
• Governments, which could leverage pre-built tools to enforce cooperative AI behavior without needing deep technical expertise.
• Longtermist organizations, especially those focused on existential risk reduction and suffering prevention.

One way to incentivize adoption might be positioning cooperative AI frameworks as a way for tech companies to demonstrate responsible innovation, potentially preempting stricter regulations.

Implementation Strategy

A step-by-step approach could start with a minimal viable product (MVP), such as an open-source simulation tool for basic bargaining scenarios (e.g., Prisoner’s Dilemma with AI agents). Based on feedback, collaborations with AI labs could test these protocols in real-world systems before scaling up to advocacy for broader governance standards. Early testing could focus on domain-specific cases—such as autonomous vehicles negotiating right-of-way—before expanding to more complex applications.

By integrating governance with strategic interaction principles, this approach could complement existing alignment-focused AI safety efforts while addressing the unique challenges posed by multi-agent conflicts.