How “reverse game theory” could keep AI agents in check
Researchers from MIT, Yale, Columbia, and Princeton are exploring reverse game theory, also called mechanism design, as a way to keep advanced AI agents aligned with human intentions. Instead of trying to interpret a model's internal reasoning, the approach designs incentive structures that make rule-following the most rational choice for the agent.
Key points
- Morris preprint proposes cross-checking rewards to prevent AI sandbagging on tests
- Hazan suggests solver-auditor roles with penalties, modeled on human legal systems
- Researchers admit AI may not act rationally and mechanisms could have loopholes
Stephen Morris at MIT co-authored a preprint suggesting mechanisms to stop models from sandbagging tests, such as rewarding a model for how well its answers predict a second model's outputs. Elad Hazan at Princeton proposes a solver-auditor framework inspired by human legal systems, where agents take turns solving problems and checking each other's work, with penalties for cheating or failing to catch cheaters. Both acknowledge risks, including collusion between agents and the shaky assumption that AI behaves rationally. Helen Qu at the Flatiron Institute views mechanism design as one layer in a defense-in-depth strategy alongside interpretability and regulation. Anthropic and OpenAI did not respond to requests for comment on whether they are pursuing this work.
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