Abstract
Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs—assumptions often violated in real-world Multi-Agent Systems (MAS) characterized by uncertainty, misaligned perceptions, and nested beliefs. To address these limitations, researchers have proposed extensions incorporating 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, termed perceptual games, where agents may hold divergent beliefs about the structure, payoffs, or available actions.
This review systematically examines agent-compatible applications of hypergame theory, analyzing how its descriptive capabilities have adapted to dynamic and interactive MAS contexts. We scrutinized 49 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 trends, 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, such as 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.
Blogger's Review: Hypergame theory offers a fresh perspective on complex interactions within multi-agent systems, particularly in navigating uncertainties and cognitive discrepancies. Future research should focus on standardizing models and broadening their applicability to facilitate widespread adoption in real-world scenarios.