A Causal Model of Theory of Mind in Conflict for Artificial Intelligence

2026-06-15

AI Summary

Current AI systems lack mechanisms to dynamically determine when to engage theory of mind (ToM) in conflict scenarios, often leading to inefficient resource allocation or failure in social reasoning. This work formalizes, for the first time, the problem of ToM activation timing and introduces a resource-rational decision framework grounded in structural causal models (SCMs) and directed acyclic graphs (DAGs). The framework models the influence of situational and agent-specific conditions on ToM engagement through three distinct causal pathways, effectively decoupling social reasoning from behavioral policy. Empirical validation via simulations and human–AI collaboration tasks demonstrates an efficient and interpretable ToM activation mechanism that enhances both the efficiency and trustworthiness of AI’s social intelligence, laying a foundation for robust artificial social intelligence in conflict-laden environments.

Abstract

Theory of mind (ToM), the capacity to ascribe mental states to others and use those ascriptions for prediction and inference, is widely assumed to be essential for effective human-machine integration. Existing AI-ToM models address \emph{how} to mentalize, but leave the question of when largely unaddressed. The central question is: under what situational and agent-level conditions is ToM engagement causally warranted in conflict? This paper presents a structural causal model formalized as a directed acyclic graph (DAG), treating ToM as a mechanism activated by situational and agent-level conditions rather than as an always-on capacity. The model specifies four exogenous variables capturing situational and agent-level conditions, five endogenous mediators, and a mechanistic ToM node producing engagement states through three distinct causal pathways: a tractability pathway, a reasoning-depth pathway, and an enabling-cause pathway. The primary outcome is epistemic accuracy, which decouples social reasoning from behavioral policy and generalizes across social phenomena beyond conflict. The framework gives AI systems a principled, resource-rational decision procedure for mentalizing, with implications for efficiency, trust, and the development of robust artificial social intelligence. Simulation validation, empirical human-machine teaming studies, and ethical considerations arising from conflict-optimized mentalizing are discussed.

Problem

• Theory of Mind
• conflict
• causal model
• artificial intelligence
• mentalizing

Innovation

• causal model
• theory of mind
• resource-rational
• directed acyclic graph
• epistemic accuracy