DecitectWhat kind of model this is
Decitect implements a hand-tuned evaluation function: an expert prior encoded as arithmetic, derived from the Decision Architecture series and 28 years of practice across defence, fintech, telecoms and startups. It is not a statistical model fitted to organisational outcome data and it does not claim to be. There is no training set, no regression and no learned parameter anywhere in the engine.
The right comparison is a chess engine's classical evaluation. Stockfish's evaluation function was hand-tuned by strong players and engineers for years; nobody called it illegitimate, because the weights were published and could be argued with. That is the standard these pages are built to meet. Every coefficient in Decitect's scoring function is published in Where the weights come from with where it came from, so the argument can be had against the actual numbers rather than against an impression of them.
What determinism buys and what it does not
Determinism buys three things. Reproducibility: two people with the same model get the same number, so a disagreement about structure becomes something you can examine rather than adjudicate. Inspectability: every score decomposes into its named penalties and every coefficient sits in one published dataclass, so there is nowhere for a fudge factor to hide. Diffability: two candidate structures for the same organisation can be scored and compared move by move.
It does not buy validity. A deterministic function is exactly as right or wrong every time you run it. Whether the number tracks anything true about real organisations is a separate question; nothing about reproducibility answers it. The pages linked below are about that question.
A page for each part
Where the weights come from
Every coefficient in the scoring function in one table: what it does, the mechanism source in the books and where its magnitude comes from.
The mechanisms come from the books; the magnitudes are engineering judgement, constrained where the code enforces a structural rule.
Assumptions and known limits
The assumptions built into the arithmetic, each with the specific way the output is wrong if it is false.
Then the places the model is expected to fail: very small organisations, contest priced rather than simulated, informal authority, non-engineering organisations, anything mid-transition and heterogeneous teams.
Testing the model
The external blind test that would actually falsify the model, why the earlier internal test was retired and why the protocol has not been run.
Then the sensitivity sweep: every coefficient scaled by a fifth either way, one axis at a time and then all at once, with all five conclusions surviving.