Engineering Transparency of Managerial Control. A Noocratic Approach to Intra-Firm Governance

This repository accompanies the white paper “Noocracy and IEKV: Engineering Transparency in Intra-Firm Governance” and provides a reference implementation of the numerical experiment described in the paper. The project explores how noocratic governance principles can be applied at the intra-firm level to make cognitive load, decision latency, and managerial constraints explicit, measurable, and causally linked to operational outcomes. Instead of optimizing performance or proposing new KPIs, the work introduces IEKV (Energy–Information–Cognition–Value) as a governance lens and accounting functional for decision-making costs. The included simulation implements a minimal production system with explicit managerial roles, bounded decision capacity, escalation mechanisms, and decision gating. Two governance regimes are compared under identical stress conditions: a baseline regime, where managerial latency is implicitly absorbed by operations; a noocracy-min regime, where operational actions are causally blocked until decisions are resolved. IEKV is computed using proxy-based formulations derived from observable operational indicators (decision overload, escalation rate, backlog tail, stockout exposure, downtime, throughput). These proxies are intended for early-stage adoption and experimental analysis rather than as canonical or fully calibrated measures. The code is provided for: reproducibility of the numerical experiment, inspection of causal assumptions, exploration of alternative governance configurations and stress scenarios. It is not intended as a production-ready system or an optimization framework. Key characteristics: explicit modeling of decision load and managerial WIP, separation of operational logic and governance logic, transparency of cognitive and managerial costs, compatibility with existing accounting and ERP data sources via conceptual mappings. The repository supports the central thesis of the paper:effective governance under complexity requires transparency and attribution of cognitive costs before optimization can be meaningfully pursued.