Terrain-Contingent Gain in Adaptive Governance Systems: A Calibration Framework for Context-Dependent Affective Response

This paper introduces terrain-contingent gain as a calibration framework for context-dependent affective response in adaptive governance systems. It addresses the problem that identical events can produce different adaptive responses depending on environmental terrain, including friction, curvature, hostility, and resource scarcity. The paper defines a terrain vector L (t) = (ϕ (t), χ (t), η (t), ξ (t) ) L (t) = ( (t), (t), (t), (t) ) L (t) = (ϕ (t), χ (t), η (t), ξ (t) ), where each component captures a distinct environmental property that modulates affective gain. Instead of directly scaling response amplitude, terrain modifies the shape parameters of a Hill-type gain function g (E;L) g (E;L) g (E;L): friction and scarcity shift the half-activation threshold, curvature changes the slope, and hostility lowers the gain ceiling. The framework compares three nested models: constant gain, event-only Hill gain, and terrain-contingent gain. It provides calibration questions, falsification criteria, cross-substrate transfer tests, component ablations, and cell-card reporting protocols. The goal is to test whether terrain-conditioned gain improves prediction of adaptive response amplitude and collapse risk over terrain-agnostic alternatives. Synthetic calibration experiments provide strong support for the terrain-contingent model, while financial proxy experiments are reported as operationalisation-limited. The paper therefore presents terrain-contingent gain as a substrate-calibration framework rather than a universal validation claim.