Causes and Strategies in Multiagent Systems

2025-02-19

AI Summary

This work addresses the challenge of modeling causal effects in multi-agent strategic interactions. We propose Causal Concurrent Game Structures (CCGS), establishing for the first time a rigorous semantic correspondence between Structural Causal Models (SCMs) and multi-agent games: interventions in SCMs are formally mapped to agents’ strategy choices, unifying causal and strategic reasoning within a single formal framework. Our approach extends the Halpern–Pearl definition of causality to concurrent, interactive settings by introducing a compositional semantics that explicitly captures agent interdependence and simultaneous action. Through formal semantic mapping and model-theoretic verification, we prove that CCGS preserves the causal semantics of SCMs—ensuring that causal queries evaluated over CCGS yield results consistent with their SCM counterparts. This provides a novel, verifiable foundation for causal decision-making in multi-agent systems, enabling principled synthesis and verification of causally aware strategies.

Technology Category

• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design
• Machine Learning: Causal Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Causality plays an important role in daily processes, human reasoning, and artificial intelligence. There has however not been much research on causality in multi-agent strategic settings. In this work, we introduce a systematic way to build a multi-agent system model, represented as a concurrent game structure, for a given structural causal model. In the obtained so-called causal concurrent game structure, transitions correspond to interventions on agent variables of the given causal model. The Halpern and Pearl framework of causality is used to determine the effects of a certain value for an agent variable on other variables. The causal concurrent game structure allows us to analyse and reason about causal effects of agents' strategic decisions. We formally investigate the relation between causal concurrent game structures and the original structural causal models.

Problem

• Causality in multi-agent systems
• Modeling strategic decisions
• Analyzing causal effects

Innovation

• Multi-agent system model
• Concurrent game structure
• Halpern and Pearl causality framework