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.

翻译：心智理论（ToM）是指将心理状态归因于他人并利用这些归因进行预测和推理的能力，通常被认为是实现有效人机融合的关键。现有的人工智能心智理论模型主要关注“如何”进行心智化，但很大程度上未涉及“何时”进行心智化的问题。核心问题是：在冲突情境下，ToM的参与在何种情境和智能体层面条件下具有因果合理性？本文提出一个形式化为有向无环图（DAG）的结构化因果模型，将ToM视为由情境和智能体层面条件激活的机制，而非始终开启的能力。该模型定义了四个捕捉情境和智能体层面条件的外生变量、五个内生中介变量，以及一个通过三条不同因果路径（可处理性路径、推理深度路径和致因路径）产生参与状态的机制性ToM节点。主要结果是认知准确性，它将社会推理与行为策略解耦，并推广到冲突之外的各种社会现象。该框架为人工智能系统提供了一个有原则的资源合理的心智化决策过程，对效率、信任以及鲁棒人工社会智能的发展具有重要意义。本文还讨论了模拟验证、实证人机组队研究以及由冲突优化心智化引发的伦理考量。