Hypergames: Modeling Misaligned Perceptions and Nested Beliefs for Multi-agent Systems

2025-07-25

AI Summary

Real-world multi-agent systems frequently violate classical game-theoretic assumptions—namely, agent rationality, complete information, and common knowledge—facing challenges including perceptual biases, nested beliefs, and misperceptions of game structure. To address these, this paper proposes a cognitive modeling framework grounded in hypergame theory. It establishes a taxonomy of agent-centric hypergames and defines compatibility criteria, systematically identifying hierarchical hypergames and the Hypergame Normal Form (HNF) as dominant paradigms for deception reasoning. By integrating graph-based representations with dynamic interaction modeling, the framework supports heterogeneous reasoning and subjective belief representation. A systematic review of 44 studies synthesizes application patterns across cybersecurity and robotics domains. Finally, the paper outlines a technical roadmap to enhance the realism of multi-agent strategic modeling, bridging the structural gap between theoretical abstraction and real-world deployment.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often violated in real-world MAS characterized by uncertainty, misaligned perceptions, and nested beliefs. To overcome these limitations, researchers have proposed extensions that incorporate models of cognitive constraints, subjective beliefs, and heterogeneous reasoning. Among these, hypergame theory extends the classical paradigm by explicitly modeling agents' subjective perceptions of the strategic scenario, known as perceptual games, in which agents may hold divergent beliefs about the structure, payoffs, or available actions. We present a systematic review of agent-compatible applications of hypergame theory, examining how its descriptive capabilities have been adapted to dynamic and interactive MAS contexts. We analyze 44 selected studies from cybersecurity, robotics, social simulation, communications, and general game-theoretic modeling. Building on a formal introduction to hypergame theory and its two major extensions - hierarchical hypergames and HNF - we develop agent-compatibility criteria and an agent-based classification framework to assess integration patterns and practical applicability. Our analysis reveals prevailing tendencies, including the prevalence of hierarchical and graph-based models in deceptive reasoning and the simplification of extensive theoretical frameworks in practical applications. We identify structural gaps, including the limited adoption of HNF-based models, the lack of formal hypergame languages, and unexplored opportunities for modeling human-agent and agent-agent misalignment. By synthesizing trends, challenges, and open research directions, this review provides a new roadmap for applying hypergame theory to enhance the realism and effectiveness of strategic modeling in dynamic multi-agent environments.

Problem

• Modeling misaligned perceptions in multi-agent systems
• Addressing incomplete information and nested beliefs
• Enhancing strategic realism in dynamic agent environments

Innovation

• Hypergame theory models agents' subjective perceptions
• Hierarchical hypergames enhance deceptive reasoning
• Agent-compatible criteria assess practical applicability