Affective Control under Uncertainty: Constitutive Viability and Self-Model Recruitment in Artificial Systems

Most AI systems pursue goals assigned to them from the outside. Change the objective, adjust the reward signal, and the system simply optimises for something else; nothing about its own functioning is at stake. This paper asks whether a different kind of architecture is possible: one where the conditions for remaining in control are built into the system itself, rather than specified from outside. Affective Control under Uncertainty (ACU) formalises what this would require: a continuous viability-monitoring signal that globally shapes how the system selects actions, and a conditional self-modelling process that activates only when uncertainty, stakes, and time pressure converge. The paper specifies the architecture, proposes a discrimination experiment, and remains deliberately neutral on consciousness. The goal is to make a genuinely architectural question more precise and testable.