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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 Electric Reliability Council of Texas's collapse?

5 bias patterns are documented in this case, led by Recency Bias. The full set: Recency Bias, Availability Heuristic, Cognitive Misering, Loss Aversion, Hindsight Bias. 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 failureEnergy2021

Electric Reliability Council of Texas

Texas ERCOT Winter Storm Power Crisis

Estimated impact: $195B economic damage; 246 deaths

F14

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

Winter Storm Uri caused catastrophic power grid failure in Texas, leaving 4.5 million homes without power for days in freezing temperatures. 246 people died. ERCOT and Texas regulators had rejected winterization requirements after a similar 2011 event, citing costs.

Decision context

Whether to mandate and enforce winterization standards for power generation and natural gas infrastructure after the 2011 winter storm exposed the same vulnerabilities.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

None

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

★Recency Bias

critical

Availability Heuristic

medium

Cognitive Misering

medium

Loss Aversion

medium

Hindsight Bias

medium

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

3D Decision Knowledge Graph

Electric Reliability Council of Texas · 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

!Recency Spiral

Compound cognitive failure pattern identified in this decision.

!Status Quo Lock

Loss aversion combined with status quo bias makes even clearly beneficial change feel impossible.

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

Recency bias: a decade of mild winters after 2011 eroded the urgency of winterization mandates
The exact failure modes from 2011 recurred in 2021 because recommendations were advisory, not mandatory
Hindsight bias: the 2011 FERC/NERC report explicitly predicted the 2021 failure, but its recommendations were ignored

Source: FERC/NERC Joint Report on February 2021 Cold Weather Grid Operations (2021); Texas House Committee Investigation (Post Mortem)

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We caught these patterns in Electric Reliability Council of Texas's own record — before the outcome.

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Workflows that fire on decisions like Electric Reliability Council of Texas’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

Recency bias: a decade of mild winters after 2011 eroded the urgency of winterization mandates

The exact failure modes from 2011 recurred in 2021 because recommendations were advisory, not mandatory

Hindsight bias: the 2011 FERC/NERC report explicitly predicted the 2021 failure, but its recommendations were ignored