Decision subassemblies provide structured processes for making high-quality choices under uncertainty. Each is built from bricks and tuned for a specific decision type.
Trigger: Fast-moving, high-stakes, information-incomplete situations
Origin: Col. John Boyd, USAF, developed for fighter pilot decision-making
Composed From: Level Shifting, Pattern Matching, Assumption Surfacing
Related Modules: Two-Way Door Test, Situation Recognition, Conflict Schema
Observe → Orient → Decide → Act → Loop
The OODA loop’s power is not in the individual steps. The insight is in two things:
First, the Orient step is the most critical. It is where mental models, experience, and prior conditioning filter raw observation into perception. Two people observing the same event orient very differently based on their models. Your orient step is your competitive advantage, or your blind spot.
Second, cycle speed. Boyd argued that the party cycling through OODA faster than an opponent could make the opponent’s decisions irrelevant; the world changed before they could act on their analysis. In competitive environments, decision velocity is often more valuable than decision perfection.
Known limits: Optimizing for loop speed can sacrifice accuracy. In non-competitive or non-time-pressured situations, the urgency framing is counterproductive.
Trigger: All consequential decisions, as a triage step before applying other tools
Origin: Jeff Bezos / Amazon decision framework
Composed From: Reversibility Check + Asymmetry Scan
A two-way door decision is reversible. Make it quickly, at the lowest appropriate level, with explicit permission to be wrong.
A one-way door decision is irreversible or near-irreversible. It deserves slow, careful, senior-level deliberation.
The key insight: most decisions are two-way doors, but organizations and individuals chronically treat them as one-way doors, over-processing them while simultaneously under-processing the genuinely irreversible ones.
Known limits: Some decisions feel like two-way doors but have hidden irreversibilities (time, compounding, reputation). Always check for hidden one-way-ness before classifying as reversible.
Trigger: Major life decisions; career crossroads; significant personal commitments
Origin: Jeff Bezos; his framework for deciding to leave D.E. Shaw to found Amazon
Composed From: Time Shifting + Role Shifting
Project yourself to age 80, looking back at your life. Which choice will you regret less?
This reframe accomplishes two things: (1) it moves the decision from the present emotional context, where urgency and fear dominate, to a long-term value context; (2) it converts an approach/avoidance decision into a regret-minimization optimization, which most people find easier to reason about clearly.
Bezos concluded he would not regret trying and failing, but he would regret not trying.
Known limits: Can rationalize large risks by minimizing imagined future regret. Pair with pre-mortem and asymmetry scan to maintain risk discipline.
Trigger: Any forecast, estimate, or prediction
Developed: By Daniel Kahneman and Amos Tversky; formalized by Bent Flyvbjerg
Composed From: Base Rates + Analogical Reasoning + Assumption Surfacing
1. Find the reference class, the set of cases most structurally similar to yours
2. Determine the base rate outcome distribution for that class
3. Anchor your forecast on the base rate; adjust only with specific, credible evidence about how your case differs
Research by Flyvbjerg on megaprojects found that forecasts based on reference classes consistently outperformed forecasts based on project-specific analysis, even by the project’s own experts.
Known limits: Requires honest selection of reference class. The most common error is selecting an overly favorable reference class to justify an optimistic forecast.
Trigger: Complex decisions with multiple competing criteria
Composed from: Decomposition + Variable Isolation + Operationalizing
1. List all relevant criteria for the decision
2. Assign weights to each criterion (must sum to 100)
3. Score each option against each criterion (e.g. 1–10)
4. Multiply scores by weights; sum for each option
5. Examine the ranking — then examine your reaction to it
The matrix’s most valuable output is often not the scores but the reaction to them. If the highest-scoring option feels wrong, that feeling contains information — either about a criterion you forgot to include, or about weights that don’t reflect your actual values.
Known limits: Garbage in, garbage out. The quality of the output depends entirely on the quality of the criteria and weights. Use to surface preferences, not to replace judgment.
Trigger: Before committing to any significant plan or investment
Origin: Gary Klein, prospective hindsight research
Composed From: Inversion + Scenario Modeling + Base Rates
At the subassembly level, the pre-mortem is a structured group process:
Present the plan
Announce: “It is one year later. The plan has failed. Badly.”
Each person independently writes the causes of failure
Share; compile; categorize
Use the list to redesign the plan before committing
Research shows prospective hindsight increases identification of failure causes by ~30% vs. direct risk analysis.