To make accurate inferences in an interactive setting, an agent must not confuse passive observation of events with having intervened to cause them. The $do$ operator formalises interventions so that we may reason about their effect. Yet there exist pareto optimal mathematical formalisms of general intelligence in an interactive setting which, presupposing no explicit representation of intervention, make maximally accurate inferences. We examine one such formalism. We show that in the absence of a $do$ operator, an intervention can be represented by a variable. We then argue that variables are abstractions, and that need to explicitly represent interventions in advance arises only because we presuppose these sorts of abstractions. The aforementioned formalism avoids this and so, initial conditions permitting, representations of relevant causal interventions will emerge through induction. These emergent abstractions function as representations of one`s self and of any other object, inasmuch as the interventions of those objects impact the satisfaction of goals. We argue that this explains how one might reason about one`s own identity and intent, those of others, of one`s own as perceived by others and so on. In a narrow sense this describes what it is to be aware, and is a mechanistic explanation of aspects of consciousness.

翻译：在交互环境中做出准确推断时，智能体必须避免将被动观察事件与主动干预事件相混淆。$do$算子将干预形式化，使我们能够推理其效果。然而，存在一种交互环境中通用智能的帕累托最优数学形式体系，该体系无需预设对干预的显式表征，即可做出最大限度的准确推断。我们审视了这一形式体系，并证明在缺乏$do$算子的情况下，干预可通过变量加以表征。继而论证变量即抽象，而需预先显式表征干预的根源恰在于对这些抽象形式的预设。前述形式体系规避了这一前提，因此在初始条件允许的情况下，相关因果干预的表征将通过归纳涌现。这些涌现的抽象表征的功能等同于对自我及其他客体的表征——只要这些客体的干预行为影响目标满意度。我们论证这解释了智能体如何推理自身及他人的身份与意图、他人如何感知自身等认知过程。在狭义上，这描述了察觉的实质，并构成了意识层面的机械论解释。