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How is Decision Intel different from ChatGPT or a general AI assistant?

ChatGPT gives one opinion from one model: ungoverned, untraceable, unaudited. Decision Intel is the reasoning audit platform. Most tools audit your data; we audit your reasoning — and catch the fatal blind spots in strategic memos before the committee does. It measures the noise in your reasoning the same way Kahneman did in the insurance underwriter study, simulates an AI boardroom of CEO, CFO, and board personas, runs what-if interventions against a 143-case public reference library, and compounds every confirmed outcome back into a calibrated Decision Quality Index your audit committee can defend. Not a chatbot; a reasoning audit, checkable from memo to outcome.

Which cognitive biases caused U.S. Navy's collapse?

5 bias patterns are documented in this case, led by Confirmation Bias. The full set: Confirmation Bias, Availability Heuristic, Groupthink, Authority Bias, Framing Effect. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

What is Decision Intel's reasoning audit?

Decision Intel runs a 12-node Recognition-Rigor Framework (R²F) pipeline on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

All case studies

Catastrophic failureAerospace1988

U.S. Navy

USS Vincennes Iran Air Flight 655 Shoot-Down

Estimated impact: 290 civilian lives; severe diplomatic consequences

F14

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

USS Vincennes shot down Iran Air Flight 655, killing 290 civilians, after misidentifying the commercial Airbus A300 as an attacking F-14 fighter. The Aegis combat system correctly identified the aircraft, but crew members under stress interpreted the data to confirm the threat they expected.

Decision context

Whether to fire on an aircraft in a commercial air corridor that the Aegis system identified as climbing (not descending in attack profile) and on a civilian transponder frequency.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Yes

Time pressure

Present

Unanimous consensus

Yes

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

★Confirmation Bias

critical

Availability Heuristic

medium

Groupthink

high

Authority Bias

high

Framing Effect

medium

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

U.S. Navy · 5 biases, 2 toxic combinations

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

!Echo Chamber

Group members reinforce each other's views, systematically filtering out dissenting signals.

!Blind Sprint

Overconfidence combined with planning fallacy creates unstoppable momentum toward failure.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

Confirmation bias under combat stress: the crew expected an attack and interpreted ambiguous data to confirm that expectation
Availability heuristic: the recent USS Stark attack (1987) made an air attack seem more likely than it was
The Aegis system provided correct data, but human interpretation under stress filtered it through preexisting threat expectations

Source: Fogarty Investigation Report, U.S. DOD (1988); International Court of Justice proceedings (Post Mortem)

Pilot slots available

We caught these patterns in U.S. Navy's own record — before the outcome.

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Workflows that fire on decisions like U.S. Navy’s

The same Recognition-Rigor Framework that documents this case audits memos in the same shape — before the outcome forces the lesson.

For CSOs and Heads of Strategic Planning

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Lessons learned

Confirmation bias under combat stress: the crew expected an attack and interpreted ambiguous data to confirm that expectation

Availability heuristic: the recent USS Stark attack (1987) made an air attack seem more likely than it was

The Aegis system provided correct data, but human interpretation under stress filtered it through preexisting threat expectations