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：为了在交互环境中做出准确推断，智能体必须区分被动观察事件与主动干预导致事件。$do$算子形式化了干预行为，使我们能够推理其效果。然而，存在一类帕累托最优的通用智能数学形式体系，它们无需预设显式干预表征，即可做出最大程度准确的推断。我们考察了其中一种形式体系。我们证明，在没有$do$算子的情况下，干预可以通过一个变量来表征。随后我们论证，变量是抽象概念，而之所以需要预先显式表征干预，正是因为我们预设了这类抽象。前述形式体系避免了这一预设，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。这些涌现的抽象概念充当对自身及其他任何对象的表征——只要这些对象的干预影响到目标的实现。我们认为，这解释了人们如何推理自身身份与意图、他人身份与意图、以及他人如何看待自身等。从狭义上看，这描述了意识的本质，并提供了一种关于意识诸方面的机制性解释。