  Social agents with finitely nested opponent models are vulnerable to
manipulation by agents with deeper reasoning and more sophisticated opponent
modelling. This imbalance, rooted in logic and the theory of recursive
modelling frameworks, cannot be solved directly. We propose a computational
framework, $\aleph$-IPOMDP, augmenting model-based RL agents' Bayesian
inference with an anomaly detection algorithm and an out-of-belief policy. Our
mechanism allows agents to realize they are being deceived, even if they cannot
understand how, and to deter opponents via a credible threat. We test this
framework in both a mixed-motive and zero-sum game. Our results show the
$\aleph$ mechanism's effectiveness, leading to more equitable outcomes and less
exploitation by more sophisticated agents. We discuss implications for AI
safety, cybersecurity, cognitive science, and psychiatry.
