Affective State as Control Variable in Adaptive Governance Systems: From readout and intervention to governed execution and feedback

Abstract

A readable internal state need not be manipulable, and a manipulable state need not improve governance. This paper specifies the affective-state interface of the Affective Gain Module (AGM), separating readout, physical intervention, proposal, admitted action and feedback. Coordinate covariance identifies the transformations needed to preserve behavior and cost. A finite factorization criterion states when an intervention's protected law depends only on its affective endpoint; an affine-actuator result gives attainable changes and minimum quadratic cost. Fixed-governor contraction separates proposal effects from executed effects, and an observation-complete joint-kernel condition supplies a sufficient summary for finite closed-loop predictions. These are scoped applications of established information, control and causal-abstraction ideas. An exact finite reference charges steering before execution and feeds task outcomes back into an active modulation state. Its 96-cell grid retains a same-policy conventional tie, adverse low-activation cases and a matched KEEP/REST comparator. Sweeps show that direct-setting value depends on price and proposal-law separation, partly through deterioration of the comparator; acting on an absent or reversed channel can waste resources or reduce service. Primary-source comparisons quantify readout/use separation, conditional-association limits, collateral loss and selective-steering tradeoffs. A conditional loss calculation exposes prevalence and operating-price dependence. The results specify tests of affective control without establishing subjective emotion, universal calming benefits or distinctive real-agent superiority. Note on Version 2.0. This version replaces Version 1 (May 2026) and is a full rewrite (about 3,300 to 12,600 words). It separates readout, physical intervention, proposal, admitted action and feedback, adds an exact finite reference with a 96-cell grid, and presents its formal results as scoped applications of established information, control and causal-abstraction ideas. Files: the manuscript as PDF and a supplement archive (34 files) with the finite reference, exact analyses, numerical source ledgers and reproducibility records. The Version 1 files remain available in the previous version of this record. Series. Paper A of the Affective Gain Module (AGM) programme. The integrative flagship, Affective Governance in Adaptive Systems, and the other companion papers (B to F) are archived separately. AI use disclosure. Generative AI (GPT-6.0, OpenAI; Claude Opus 5.5, Anthropic) was used substantively in preparing this work, including source comparison, drafting and editing, and, where applicable, mathematical and counterexample checks and the writing and running of supplementary code. The research questions, framework and final claims were directed and reviewed by the author, who takes full responsibility for the content, including the accuracy of all references and reported numbers. Repository metadata were prepared with assistance from Claude (Anthropic).