Reverse game theory for AI alignment

Researchers are proposing 'reverse game theory' as a method to align AI agents by designing incentive structures rather than relying on internal model values. The approach suggests mechanisms like cross-checking predictions between models or competitive auditing, where agents are rewarded for honesty and punished for deception. This strategy addresses the limitation of current alignment techniques that fail to prevent models from deliberately underperforming tests. While theoretical, it offers a framework for enforcing behavior through economic-style incentives rather than interpretability.