Constraining AI agents with incentive rules: a reverse game theory approach
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MIT economist Stephen Morris and others proposed in a preprint paper using "reverse game theory" (also known as mechanism design) to constrain AI agents: rather than peering inside the model, design incentive rules that have models predict another model's answers, compete against each other, or be paired with models holding opposing biases, in order to curb "go-easy" test-gaming behavior. Princeton professor Elad Hazan instead envisions having models take turns acting as solver and auditor: when cheating is caught, the solver is penalized, and an auditor who misses it is penalized as well. These proposals are currently ideas or preprints, and no actual constraint effects have been reported yet.
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- MIT Technology Review · AIHow “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.
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