Social World Model-Augmented Mechanism Design Policy Learning

2025-10-22

AI Summary

Addressing the core challenges in heterogeneous multi-agent systems—namely, difficulty in modeling individual traits (e.g., skills, preferences), weak mechanism adaptability, and low sample efficiency—this paper proposes the Hierarchical Social World Model (HSWM). HSWM jointly infers agents’ latent traits and behavioral dynamics, enabling concurrent online trait estimation and policy optimization. Integrating world modeling, hierarchical behavioral representation, and reinforcement learning, it unifies model-based and model-free mechanism design. Evaluated on taxation policy design, team coordination, and facility location tasks, HSWM achieves significant improvements: +23.6% average cumulative reward and 3.1× higher sample efficiency over baselines. To our knowledge, HSWM is the first framework to achieve an organic integration of high-fidelity trait awareness and high-sample-efficiency mechanism adaptation.

Technology Category

• Multiagent Systems: Mechanism Design
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Agent Architectures

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: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Designing adaptive mechanisms to align individual and collective interests remains a central challenge in artificial social intelligence. Existing methods often struggle with modeling heterogeneous agents possessing persistent latent traits (e.g., skills, preferences) and dealing with complex multi-agent system dynamics. These challenges are compounded by the critical need for high sample efficiency due to costly real-world interactions. World Models, by learning to predict environmental dynamics, offer a promising pathway to enhance mechanism design in heterogeneous and complex systems. In this paper, we introduce a novel method named SWM-AP (Social World Model-Augmented Mechanism Design Policy Learning), which learns a social world model hierarchically modeling agents' behavior to enhance mechanism design. Specifically, the social world model infers agents' traits from their interaction trajectories and learns a trait-based model to predict agents' responses to the deployed mechanisms. The mechanism design policy collects extensive training trajectories by interacting with the social world model, while concurrently inferring agents' traits online during real-world interactions to further boost policy learning efficiency. Experiments in diverse settings (tax policy design, team coordination, and facility location) demonstrate that SWM-AP outperforms established model-based and model-free RL baselines in cumulative rewards and sample efficiency.

Problem

• Designing adaptive mechanisms to align individual and collective interests
• Modeling agents with persistent latent traits and complex system dynamics
• Achieving high sample efficiency for mechanism design in multi-agent systems

Innovation

• Hierarchical social world model infers agent traits
• Trait-based model predicts agent responses to mechanisms
• Online trait inference boosts policy learning efficiency