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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 Biogen's failure?

5 bias patterns are documented in this case, led by Optimism Bias. The full set: Authority Bias, Confirmation Bias, Optimism Bias, Anchoring Bias, Groupthink. 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

FailureHealthcare2021

Biogen

Biogen Aduhelm Approval Controversy

Estimated impact: $11B market cap loss when CMS restricted coverage; institutional credibility damage

F30

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

The FDA approved Biogen's Alzheimer's drug Aduhelm via accelerated approval despite an advisory committee voting nearly unanimously against it. The $56,000/year drug showed no clear clinical benefit. Three advisory committee members resigned in protest. CMS later restricted coverage.

Decision context

Whether the FDA should have approved a drug that failed two Phase III trials and whose advisory committee voted 10-0 (with 1 uncertain) against approval.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

Present

Unanimous consensus

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

Authority Bias

high

Confirmation Bias

high

★Optimism Bias

critical

Anchoring Bias

high

Groupthink

high

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

3D Decision Knowledge Graph

Biogen · 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

!Optimism Trap

Overconfidence and availability bias create a systematically positive outlook that ignores base rates.

!Yes Committee

Authority bias meets groupthink: the group converges on whatever leadership signals it wants.

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

Optimism bias in Alzheimer's research created pressure to approve any drug showing a biomarker effect regardless of clinical outcomes
Overriding an advisory committee's near-unanimous rejection damages the credibility of the regulatory process
Accelerated approval based on surrogate endpoints (amyloid reduction) when clinical benefit is unproven sets a dangerous precedent

Source: FDA Peripheral and Central Nervous System Drugs Advisory Committee meeting transcript (2020); STAT News investigation (2021) (FDA Action)

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Workflows that fire on decisions like Biogen’s

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

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Lessons learned

Optimism bias in Alzheimer's research created pressure to approve any drug showing a biomarker effect regardless of clinical outcomes

Overriding an advisory committee's near-unanimous rejection damages the credibility of the regulatory process

Accelerated approval based on surrogate endpoints (amyloid reduction) when clinical benefit is unproven sets a dangerous precedent