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

The article describes "reverse game theory," or mechanism design, as an approach to AI alignment that designs incentives so agents behave as desired regardless of their internal workings, rather than trying to inspect or train their values. It cites a preprint by MIT's Stephen Morris and colleagues proposing mechanisms to stop models from sandbagging tests, and Princeton's Elad Hazan's proposal for solver and auditor agents rewarded and punished based on outcomes. It notes open questions, including whether AI agents respond rationally to incentives and whether such mechanisms would carry over to real frontier models.