Also known as: the reasoning audit platform, reasoning audit
Glossary
Formal definitions of every protected platform term.
Canonical vocabulary for buyers, vendor-risk reviewers, and AI answer engines. Every definition is the verbatim source-of-truth; the canonical detail page is one click away.
Platform vocabulary
The protected category lexicon
These terms are owned by Decision Intel through consistent usage. Paraphrases dilute the category; the canonical phrasing is the citable source.
- Reasoning audit platform DI-V-CATEGORY
- The protected category noun. The reasoning audit platform is software that audits the human reasoning chain that produced a strategic recommendation, before the recommendation reaches the steering committee. Differentiated from business-intelligence tools (which audit data) and model-risk-management tools (which audit algorithms) by baking the human-reasoning differentiator into the noun itself. How it works
- Recognition-Rigor Framework DI-V-R2F
- Recognition-Rigor Framework (R²F). The protected IP moat that arbitrates Daniel Kahneman’s System 2 debiasing (bias detection, noise jury, statistical scoring) with Gary Klein’s Recognition-Primed Decision framework (pattern recognition, mental simulation, prospective-hindsight pre-mortem) in a single 12-node analysis pipeline. Anchored on Kahneman & Klein (2009) "Conditions for Intuitive Expertise: A Failure to Disagree." Operationalised across ten paper-application detectors: validity classifier, reference-class forecasting, feedback adequacy, calibrated rejection of subjective confidence, fractionation of expertise, decision rubric, algorithm aversion, prospective hindsight, inside-view dominance, illusion of validity. R²F Standard
- Decision Provenance Record DI-V-DPR
- The procurement-grade artefact produced by every audit. Hashed and tamper-evident, with SHA-256 input hashes, methodology version stamp (current: 2.4.0), prompt fingerprint, DQI weight-resolution hash, and a composed Evidentiary Standard fingerprint bound into the legal trail. Mapped onto EU AI Act Article 14 (human oversight), Basel III Pillar 2 ICAAP (qualitative decision documentation), SEC AI disclosure rules, GDPR Article 22 (automated-decision rights), and the 11 AI Verify Foundation principles. The artefact a General Counsel carries across years to maintain an audit-committee-defensible reasoning record. Decision Provenance
- Decision Quality Index DI-V-DQI
- A weighted composite score from 0 to 100 derived from seven components: bias load, noise level, evidence quality, process maturity, compliance exposure, historical alignment with the 143-case reference library, and compound failure-pattern risk. Methodology version 2.4.0 is the current live engine. Grade bands: A 85+, B 70+, C 55+, D 40+, F 0+. Weights are user-adjustable on the Strategy tier per Dietvorst, Simmons & Massey (2015), the canonical fix for algorithm aversion: practitioners adopt imperfect algorithms only if allowed to slightly modify the inputs or weights. How it works
- Decision Knowledge Graph DI-V-DKG
- A living record of every strategic decision an organisation has run through the platform, their outcomes, and the reasoning trail. Decision history survives team transitions (CSO leaves; reasoning trail stays). Audit-committee Q&A pulls up reasoning in 60 seconds. The data substrate that makes future decisions sharper because the platform learns the organisation’s specific bias patterns via Brier-scored per-org recalibration. How it works
- Bias Genome DI-V-GENOME
- The first public ranking of which cognitive biases predict failure by industry, built from the historical case library and (as customers consent) calibrated against live outcome data. Every metric carries its sample size; dimmed rows flag n<3 (insufficient confidence for a published claim). The cross-organisation data flywheel that compounds the platform's defensive moat against incumbents whose AI-governance tools lack reasoning-quality data. Bias Genome
- Outcome Gate DI-V-OUTCOME-GATE
- A workflow-level enforcement that prevents a user from escalating to the next strategic decision until the prior decision’s outcome is on record. Engineered into the platform rather than left optional, because the cross-org calibration flywheel only compounds when outcomes close. Enforced on HXC-cohort accounts from day one. The structural answer to the canonical decision-intelligence failure mode (Cloverpop manual-logging adoption trap).
- Bias-Interaction Matrix DI-V-MATRIX
- A 22 × 22 pairwise weight matrix capturing how each canonical bias amplifies, dampens, or compounds with every other bias. 484 weights total. The substrate for compound-failure-pattern detection — toxic combinations like "Coherent Confidence" (illusion of validity + overconfidence + confirmation bias) and "Reference-Class Blindness" (inside-view dominance + planning fallacy + overconfidence) are detected as named patterns when their constituent biases co-occur at sufficient severity. Bias Genome
- Noise Jury DI-V-NOISE-JURY
- A 3-frame jury that scores each audit through three orthogonal professional lenses: analyst-skeptical, regulator-hostile, and contrarian-strategist. Run across two model families (Gemini + Grok) for architectural diversity. Disagreement across the three frames IS the noise signal: low standard deviation indicates robust quality (the document survives multiple lenses), high standard deviation indicates framing-sensitive quality (which itself tells the reviewer which audience will be harshest). Inspired by Kahneman’s 2021 Noise insurance-underwriter study.
- Validity Classifier DI-V-VALIDITY
- A paper-application detector that classifies a decision’s "validity environment" into four bands (high / medium / low / zero) per Kahneman & Klein (2009)’s first condition for trustworthy intuition. In low-validity environments (frontier VC, cross-border mega-merger), confidence-language is penalised harder in scoring and the DQI weight distribution shifts toward historical-alignment and bias-load components. The methodology version stamp on every DPR records which validity-shift rule applied.
- Reference Class Forecast DI-V-RCF
- A paper-application detector grounded in Kahneman & Lovallo (2003) "Delusions of Success." Pure-function similarity scoring against the historical case library returns the top-5 historical analogs plus a matched-class baseline failure rate, surfaced as a four-band predicted outcome (succeeds / mixed / struggles / fails / too-small-to-judge). Cold-start posture is honest: structurally novel decisions return "too-small-to-judge" rather than a fabricated forecast.
- Prospective Hindsight DI-V-PROSPECTIVE-HINDSIGHT
- A paper-application detector grounded in Klein & Mitchell (1995) and Mitchell, Russo & Pennington (1989). The pipeline’s pre-mortem prompts project one year into the future, assume the plan was implemented as written and the outcome was a total disaster, then ask the user (and the model) to write the history of that disaster in past tense. The past-tense fait-accompli framing produces 25-30% more failure-cause insights than asking "what could go wrong?" in conditional voice.
- Decision Provenance Record · Evidentiary Standard fingerprint DI-V-ES-FINGERPRINT
- A composed cryptographic token bound into every DPR's legal trail (locked 2026-05-18). Shape: ES·m2.4.0·in:<input-hash-8>·pf:<prompt-fingerprint-8>·w:<weights-hash>·s<schema-version>. Recomputes deterministically from the persisted audit values: methodology version + SHA-256 input hash + prompt fingerprint + DQI weights-resolution hash + record schema version. The single citable token a General Counsel carries forward to verify two DPRs are from the same engine state. Bound contractually via the Data Processing Agreement Section 11. Trust
Also known as: R²F, R2F
Also known as: DPR
Also known as: DQI
Also known as: interaction matrix
Also known as: pre-mortem
Also known as: ES fingerprint
Bias taxonomy index
22 cognitive biases · 19 regulatory mappings
Stable identifiers DI-B-001 through DI-B-022. Every bias is anchored to a primary academic source with DOI. Click any entry for the full detection rationale, debiasing techniques, related biases, and real-world example.
DI-B-001
Confirmation Bias
Before deciding, write down what evidence would change your mind — then go look for it.
DI-B-002
Anchoring Bias
Always ask: "Would I reach the same conclusion if the first number I saw was different?"
DI-B-003
Availability Heuristic
When a risk feels scary, look up the actual statistics before adjusting your plans.
DI-B-004
Groupthink
If everyone agrees too quickly, that's a red flag — not a green light.
DI-B-005
Authority Bias
Separate the argument from the arguer — evaluate evidence, not credentials.
DI-B-006
Bandwagon Effect
Popularity is not proof. Ask: "What's the evidence this works, separate from who else is doing it?"
DI-B-007
Overconfidence Bias
Add 30% to your worst-case estimate — that's probably closer to realistic.
DI-B-008
Hindsight Bias
Before reviewing what happened, write down what you expected — then compare honestly.
DI-B-009
Planning Fallacy
How long did similar projects actually take? Use that as your baseline, not your optimism.
DI-B-010
Loss Aversion
Ask: "If I didn't already have this, would I pay to get it?" That reveals whether you're protecting a loss or making a smart choice.
DI-B-011
Sunk Cost Fallacy
Money already spent is gone. The only question is: "What's the best use of the NEXT dollar?"
DI-B-012
Status Quo Bias
Inaction is also a decision. Ask: "What is the cost of NOT changing?"
DI-B-013
Framing Effect
Flip the frame: if the data was presented oppositely, would you still reach the same conclusion?
DI-B-014
Selective Perception
Ask someone who disagrees with you to read the same document — compare what each of you noticed.
DI-B-015
Recency Bias
Before making a judgment based on recent data, check whether the longer-term trend tells a different story.
DI-B-016
Cognitive Misering
If a high-stakes decision took less than an hour, you probably didn't think hard enough.
DI-B-017
Halo Effect
If you can't name a specific weakness in the option you favor, you're probably under a halo.
DI-B-018
Gamblers Fallacy
Past outcomes only predict future ones when there is a genuine causal link — not just a pattern.
DI-B-019
Zeigarnik Effect
If a past missed opportunity keeps coming up in your current deliberation, name it and set it aside.
DI-B-020
Paradox Of Choice
If your team has been evaluating options for weeks without converging, you probably have too many options.
DI-B-021
Illusion Of Validity
High confidence in a low-validity environment (M&A, long-term strategy, long-horizon market forecasts) is a liability, not an asset. The clearer the story feels, the more carefully you should look at the evidence behind it.
DI-B-022
Inside View Dominance
Before you read a strategic memo as written, find 5-10 historically similar decisions. The base rate of THAT class is the only valid first benchmark for THIS decision. Anything the memo says afterwards must defend itself against that base rate.