The constraints that fire while the deal sponsor is still drafting the thesis — not after the IC memo lands. Drawn from 143 real strategic decisions: 127 failures and 16 successes across 11 industries. Methodology open. Data cite-able.
Baseline failure rate in this dataset: 89%. Every “failure lift” below is multiplied against this baseline.
Four steps from a documented real-world decision to a ranked bias, with an outcome loop that recalibrates the weights as consenting customer orgs share results.
Real, documented calls drawn from SEC filings, NTSB reports, FDA actions, and academic post-mortems. Every case cites its primary source.
The Decision Intel taxonomy (DI-B-001 → DI-B-022) assigns biases to each case with an excerpt and an academic citation.
Every bias pair is scored against the others. Context amplifiers (stakes, dissent, time pressure) multiply the score; false-positive damping kicks in when a pattern fires but the outcome succeeds.
Each toxic pattern gets a named playbook (a devil’s advocate, a pre-mortem, a forced counterfactual). When real outcomes come back, the weights recalibrate on your own data.
Every bias plotted on two axes: how often it appears, how much it lifts the failure rate. The top-right quadrant is where your audit energy pays off.
Sorted by failure lift: how much more often a decision fails when this bias is present, relative to the baseline. Filter by industry to narrow the slice.
Named patterns where two biases compound. Detection in live memos is 8x worse than either bias alone. Includes the three M&A workflow-native patterns — Synergy Mirage, Conglomerate Fallacy, and Winner’s Curse — that account for the 70-90% of acquisitions that miss projected synergies (McKinsey + KPMG). This is the product category our toxic-combination engine was built for.
Confirmation bias amplified by unchallenged consensus. Teams hear what they already believe.
Favorable initial estimates become reference points; downside scenarios are discounted.
The fear of loss from any change outweighs the documented cost of inaction.
Deference to authority suppresses dissent; decisions ratified rather than debated.
Overconfidence meets systematic underestimation of time and complexity.
Past investment justifies continued commitment — the 'we're too deep to stop' pattern.
Far-adjacency acquisition justified by target growth and brand halo, with no answer to "why us as parent" — Porter parenting-advantage absent.
Synergy claims without a named operational mechanism, accountable executive, or 90-day milestone — the canonical M&A failure mode (70-90% of acquisitions miss projected synergies).
Auction-dynamic anchoring drives bids above intrinsic value; "strategic necessity" and "competitive process" language flag the deal-fever pattern.
Escalating commitment to a losing course to avoid realizing the loss.
McKinsey + KPMG track 70-90% of acquisitions missing their projected synergies. The patterns that drive most of those failures are nameable, repeatable, and detectable before the IC vote. Each column below shows the named pattern, its mechanism, and every deal in our 143-case library tagged with it.
Synergies without mechanism, owner, or 90-day milestone
Fires when projected synergies lack a NAMED OPERATIONAL MECHANISM, NAMED ACCOUNTABLE EXECUTIVE, and MEASURABLE 90-DAY MILESTONE. Per BCG integration best-practices, 70-90% of acquisitions miss projected synergies for exactly this gap.
Far-adjacency without parenting thesis
Fires when an acquisition is justified primarily by target growth and brand halo, with no answer to Porter’s "why us as the best parent" question. Anchor failures: AOL-Time Warner, Daimler-Chrysler, Bed Bath & Beyond + Container Store.
Auction-dynamic anchoring above intrinsic value
Fires on competitive-process language ("strategic necessity", "preempting competitor B", "we cannot let X get this asset") combined with monetaryStakes=high + timePressure=true. The deal-fever signal that pushed WeWork’s S-1 valuation and the post-2010 SPAC wave above defensible levels.
Coverage grows as the 143-case library expands. New patterns and anchors land in lockstep across the audit pipeline, the DPR, and this page. When a pattern’s case count shows fewer entries than you expect, that’s a tagging gap, not a detection gap — the engine catches the pattern; the library is still being annotated.
Two language signals that materially change how bias load translates to outcome. Detected automatically from the memo text, applied as a multiplier on the toxic-pattern detection score. The behavioral-finance literature on these is solid; we’re the only decision-intelligence platform we know of that operationalises them.
Success-streak language (“our last three deals closed,” “we haven’t missed in two years,” “the team is in flow”) amplifies overconfidence and disposition effects. The neuroendocrine literature links winning streaks to elevated testosterone and risk preference. We surface the signal so the audit committee sees what the memo’s authors were riding when they wrote it.
Crisis language (“urgent,” “regulator deadline,” “we have to move now,” “the window is closing”) amplifies System 1 decisions and shortens the deliberation horizon. Cortisol-driven cognition collapses the option set the memo writer considered before recommending. The multiplier surfaces that compression so the reader knows the recommendation came from a narrowed search, not an exhaustive one.
Implementation: detectWinnerEffect + detectStressSignals in the compound-scoring engine. Multipliers compound with the bias-load and noise terms, capped at 3.0× total to avoid runaway scores.
/api/intelligence/bias-genome will take over once n ≥ 3 consenting orgs have reported outcomes.Upload takes 60 seconds. Your data stays yours. Anonymized aggregation is opt-in.