How “reverse game theory” could keep AI agents in check

Researchers are exploring "reverse game theory" or mechanism design as an alternative to interpretability-focused AI alignment, proposing incentive structures that make rule-following the rational choice for AI agents regardless of their internal reasoning. Proposed approaches include cross-checking model outputs against peer predictions and creating adversarial auditor-solver dynamics inspired by human institutional systems, though challenges remain around agent collusion, market incentives, and whether these mechanisms will translate to real frontier models.

Source: MIT Technology Review