Decision Making

Decision Making

Most bad decisions are not caused by bad reasoning - they are caused by deciding the wrong question, rationalizing a pre-chosen favorite, or spending a week of analysis on a choice that could be undone in an afternoon. This process matches the depth of analysis to the stakes, forces criteria to be written before options are scored, and leaves a journal entry so judgment can be calibrated later.

Operating procedure

Order matters: classification comes first because it decides whether the rest of the process runs at all, and criteria must be locked before any option is scored - scoring first invites rationalizing a favorite.

Step 1: Gather inputs

Collect before analyzing. Label any estimate the user cannot source as a guess.

• The decision, stated in one sentence with a deadline. Default deadline if none exists: set one now - endless analysis is itself a costly choice.
• The real options on the table. Push for at least three; a binary framing usually hides a third option (negotiate, delay, pilot, partial commitment).
• What happens if the user does nothing (the status quo is always an option and often the silently chosen one).
• Constraints that are truly fixed vs. merely assumed.
• The user's emotional state (tired, rushed, angry, infatuated with one option) - record it; it belongs in the journal entry.

Step 2: Classify reversibility

• Two-way door (reversible, low stakes): decide fast and move on. Rule of thumb: if undoing the choice costs less than a day of effort, spend minutes, not days. Decide with roughly 70% of the information you wish you had - waiting for 90% means you were slow.
• One-way door (irreversible or high stakes - quitting a job, relocating, marriage, shutting down a product): run Steps 3-8 in full.

Say which classification applies and why before proceeding.

Step 3: Frame the real question

State precisely what is being decided, what problem it solves, and whether that is the right problem. Surface hidden assumptions ("this assumes the promotion won't come through - is that confirmed?").

Step 4: Define weighted criteria, then score

List what matters and assign weights summing to 100% before evaluating any option. Separate must-haves (pass/fail gates) from weighted nice-to-haves. Then score each option 1-5 per criterion and compute weighted totals.

Worked example - stay at current employer vs. join a Series B startup:

Criterion          Weight   Stay    Startup
Career growth        30%     2        5
Compensation         25%     4        3
Role/team fit        20%     3        4
Stability            15%     5        2
Location/lifestyle   10%     4        4

Weighted total              3.35     3.75

Stay: (0.30×2)+(0.25×4)+(0.20×3)+(0.15×5)+(0.10×4) = 3.35. Startup: 3.75. A gap under ~0.3 is a tie - the matrix says the decision hinges on the weights, so stress-test them. Here the 0.40 gap favors the startup, but only because growth was weighted 30%; if the user quietly believes stability matters most, the weights were dishonest and must be redone.

Step 5: Put explicit probabilities on uncertain payoffs

Where an option's value depends on uncertain outcomes, compute expected value with stated probabilities instead of vibes. Example for the startup's equity over 4 years: 60% chance worth $0, 30% chance worth $40,000, 10% chance worth $400,000. EV = 0.6×0 + 0.3×40,000 + 0.1×400,000 = $52,000, about $13,000/year - compare that against the salary gap, not the fantasy outcome. Writing the probabilities down also exposes overconfidence: if the user cannot defend the 10%, lower it.

Step 6: Second-order thinking and 10/10/10

• Trace consequences past the first order: the obvious result, then the result of that result, then longer-term systemic effects. Many bad decisions look good only at first order.
• Run 10/10/10: how will this choice feel in 10 minutes, 10 months, and 10 years? It separates transient discomfort (a hard conversation) from durable regret (a decade in the wrong career).

Step 7: Pre-mortem and bias sweep

Assume it is one year later and the decision failed badly. Write the story of why: warning signs, what was overlooked. Mitigate the top 2-3 risks before committing. Then check the four classic biases explicitly:

• Confirmation: name one piece of disconfirming evidence actively sought.
• Sunk cost: strike any reasoning that cites past spend; only future value counts.
• Anchoring: question whoever supplied the first number or framing.
• Overconfidence: attach an honest confidence percentage to the expected outcome.

Finish with inversion: ask "what would guarantee failure?" and confirm the chosen option avoids those things.

Step 8: Decide, commit, and journal

Make the call by the deadline and record a decision-journal entry:

DECISION JOURNAL - [FILL: date]
Decision:            [FILL: one sentence]
Options considered:  [FILL]
Choice and why:      [FILL: top 3 reasons]
Expected outcome:    [FILL] - confidence: [FILL]%
Key assumptions:     [FILL: the 2-3 that, if wrong, flip the decision]
State of mind:       [FILL: tired / rushed / calm / excited]
Review date:         [FILL: 6-12 months out]

Review entries months later to separate good decisions from lucky outcomes.

Deliverable

Produce a one-page decision brief containing: the reversibility classification, the framed question, the weighted criteria matrix with scores, expected-value math for any probabilistic payoff, the second-order/10-10-10 read, the pre-mortem top risks with mitigations, a clear recommendation, and a filled decision-journal entry.

Do NOT

• Do not run the full process on a two-way door - over-analysis of reversible choices is its own failure mode.
• Do not score options before weights are locked; that is how a favorite gets rationalized.
• Do not present a weighted total under a 0.3 gap as a verdict; call it a tie and interrogate the weights.
• Do not let equity, lottery-style upside, or "best case" numbers into the comparison without explicit probabilities attached.
• Do not skip the journal entry; without it there is no way to calibrate judgment later.

Quality bar

• The reversibility classification is stated and justified before any analysis.
• Criteria and weights were written before options were scored.
• Every uncertain payoff carries an explicit probability the user agreed to.
• The pre-mortem names at least two concrete failure modes with mitigations.
• The journal entry is filled in, including confidence and state of mind, with a review date set.