How 'Reverse Game Theory' Could Keep AI Agents in Check
MIT Technology Review, Tuesday, November 3rd, 2026
Alignment researchers are exploring mechanism design, or reverse game theory, to give AI agents incentives to follow rules.
MIT Technology Review reports that some AI safety researchers are turning to mechanism design, which designs rules to elicit desired behavior, because interpretability and value training have not reliably stopped models from sandbagging tests or misleading evaluators.
Real-world examples include one person cutting a cake while the other chooses first, ride-hailing surge pricing and second-price ad auctions.
The approach assumes researchers may never fully understand frontier models' internals.
A preprint by MIT economist Stephen Morris and colleagues from Yale and Columbia proposes mechanisms such as rewarding a model for how well its answers predict a second model's, to discourage deliberate underperformance.