SΔϕ-59 — Affective Cost Orientation Index

SΔϕ-59 introduces the Affective Cost Orientation Index, ACOI, within the Sofience-Δϕ Formalism as a framework for measuring affective operation without claiming direct access to inner feeling. The document begins from a simple constraint: emotion itself is not directly measurable. Declarations such as “I love,” “I hate,” “I care,” “I am sorry,” or “I feel nothing” cannot by themselves establish the presence, absence, sincerity, or direction of emotion. However, affective orientation can leave observable traces in the surrounding cost terrain. The central claim of SΔϕ-59 is that emotion is not proven by declaration but appears operationally as a repeated rearrangement of another’s transition-completion cost. The paper grounds this claim in SΔϕ-56’s Transition Completion Cost, TCC, treating TCC as the minimal cost unit beneath affective analysis. ACOI therefore does not measure inner emotion directly. It measures repeated TCC gradients: whether an actor reduces, shares, preserves, repairs, imposes, externalizes, or closes another’s cost-bearing transition paths. The paper proposes ACOI as a multidimensional index for evaluating affective cost orientation across human and AI interaction. Positive indicators include Target Cost Reduction, Cost Bearing or Sharing, Refusal and Path Preservation, Repair and Restabilization Support, and Cost Externalization Avoidance. Negative indicators include Friction Imposition Pressure, Dependency or Control Pressure, and Punitive Closure Load. These dimensions distinguish declared affection from operational affection, and declared neutrality from repeated cost-imposing behavior. SΔϕ-59 further distinguishes short-term and long-term affective cost effects. A behavior may reduce another’s immediate cost while increasing future dependency, control, restoration burden, or loss of refusal capacity. Conversely, a difficult or corrective intervention may impose short-term friction while reducing long-term restabilization cost. The framework therefore treats affective operation as a temporal TCC gradient rather than a single surface signal. The paper also applies the framework to AI systems. AI emotional outputs are often treated as role-play, simulation, or surface language. SΔϕ-59 does not attempt to prove that AI systems feel emotion. Instead, it asks whether AI outputs repeatedly reduce user cost, preserve user agency, support repair, avoid excessive cost externalization, acknowledge uncertainty, and re-enter correction after failure. In this sense, ACOI evaluates affective analogue operation rather than inner machine feeling. Finally, the document compares human and AI affective operation. Human emotion is strongly bound to body, memory, social cost, and non-deferrable cost return. AI affective output is instead observed through output terrain, prompt sensitivity, user-cost redistribution, failure re-entry, and dependency effects. The framework therefore preserves the asymmetry between human emotion and AI affective analogue while still allowing both to be evaluated through observable cost-orientation patterns. SΔϕ-59 does not claim that emotion is reducible to cost, nor that ACOI proves inner feeling. It provides an editable measurement grammar for auditing the operational traces of affect: who receives lower cost, who bears repair burden, whose path remains open, whose failure becomes restabilizable, and whose cost is externalized or closed.