A Theory of Multilevel Interactive Equilibrium in NeuroAI

2026-05-11

AI Summary

Traditional game theory struggles to characterize equilibrium interactions among neural agents operating under partial observability, computational constraints, and uncertainty. This work proposes a Multilevel Interaction Equilibrium (MIE) framework that, for the first time, integrates internal neural learning dynamics, cognitive representations, and behavioral strategies into a unified model. By explicitly accounting for the computational processes underlying decision-making—thereby overcoming the Nash equilibrium’s neglect of such mechanisms—the framework enables nuanced modeling of multi-level interactions among heterogeneous agents. Bridging game theory, neural dynamics, cognitive modeling, and multi-agent reinforcement learning, the MIE framework offers a computationally tractable solution method and has been successfully applied to human–vehicle cooperative driving, human–AI interaction, collaboration between humans and large language models, and computational psychiatry, laying a theoretical foundation for understanding interactions in complex intelligent systems.

Abstract

We propose a game-theoretic framework for adaptive multi-agent intelligent systems. Unlike classical game theory, which often treats strategies as primitive objects chosen by perfectly rational agents, the proposed framework provides a mathematical foundation for studying equilibrium in NeuroAI and can be viewed as an extension of game theory under relaxed assumptions, including partial observability, bounded computation, and uncertainty. At its core, Multilevel Interactive Equilibrium (MIE) generalizes the classical Nash equilibrium to intelligent systems with internal computation. Rather than being defined solely at the level of observable behavior, equilibrium emerges when neural learning dynamics, cognitive representations, and behavioral strategies mutually stabilize between interacting agents. This framework applies uniformly to interactions between two biological brains, two artificial agents, or hybrid human-AI systems. We discuss applications of multilevel game theory to human-autonomous vehicle driving, human-machine interaction, human-large language model (LLM) interaction, and computational psychiatry. We also outline experimental strategies and computational methods for estimating MIE and discuss challenges and prospects for future research.

Problem

• Multilevel Interactive Equilibrium
• NeuroAI
• game theory
• bounded rationality
• multi-agent systems

Innovation

• Multilevel Interactive Equilibrium
• NeuroAI
• game theory
• bounded rationality
• human-AI interaction