Detecting and Deterring Manipulation in a Cognitive Hierarchy

2024-05-03

AI Summary

Intelligent agents with limited nested reasoning—such as low-order agents in Interactive Partially Observable Markov Decision Processes (IPOMDPs)—are vulnerable to manipulation by higher-order adversaries; existing recursive modeling frameworks struggle to simultaneously ensure interpretability and robust countermeasures. Method: We propose the ℵ-IPOMDP framework, the first to integrate statistical anomaly detection with *out-of-belief* policies within the IPOMDP formalism. This enables low-order agents to detect deceptive behavior and enact credible deterrence without requiring explicit understanding of higher-order reasoning mechanisms. Contribution/Results: ℵ-IPOMDP significantly reduces the success rate of higher-order exploitation in both mixed-motive and zero-sum games, thereby enhancing interaction fairness. It provides a lightweight, deployable robust adversarial mechanism for AI safety, cybersecurity, and cognitive modeling—balancing computational efficiency, interpretability, and resilience against strategic deception.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Detects manipulation in cognitive hierarchy models
• Prevents exploitation by sophisticated reasoning agents
• Enhances AI safety and equitable outcomes in games

Innovation

• Augments Bayesian inference with anomaly detection
• Implements out-of-belief policy to deter deception
• Tests effectiveness in mixed-motive and zero-sum games