Decision Making

A decision is a commitment to one of several possible actions in the face of uncertainty about their consequences. Good decision-making is not the same as getting good outcomes — luck intervenes — but consistently good decisions produce better outcomes over time. This skill covers the structured methods decision scientists use to bring rigor to choices that matter.

Agent affinity: kahneman-ct (System 1 / System 2 allocation), tversky (expected value and biases), paul (integration with elements of reasoning)

Concept IDs: crit-decision-frameworks, crit-calibrated-confidence, crit-intellectual-humility

The Decision Toolbox at a Glance

#	Method	Purpose	When to use
1	Expected value calculation	Weigh probabilities and payoffs	Repeatable decisions with quantifiable outcomes
2	Decision trees	Map sequential choices and chance nodes	Multi-stage decisions with contingencies
3	Multi-criteria decision analysis (MCDA)	Weigh multiple incommensurable criteria	Choices involving trade-offs across dimensions
4	Pros and cons with weights	Simple MCDA for everyday decisions	Personal choices, not enough data for formal analysis
5	Pre-mortem	Imagine failure to surface risks	Before committing to a major plan
6	Reversibility check	Decide how much deliberation is needed	Every decision
7	Two-way door vs. one-way door	Distinguish easily-undoable from locked-in	Speed decisions for reversible, delay for irreversible
8	Minimax regret	Minimize worst-case regret instead of maximizing expected value	Extreme uncertainty; loss aversion is justified
9	Satisficing	Pick the first option meeting minimum criteria	When the cost of searching exceeds the benefit of finding the best
10	Stopping rules	Decide in advance when to stop deliberating	When analysis paralysis is a risk

The Fundamental Distinction — Good Decision vs. Good Outcome

A decision can be good even if the outcome is bad, and a decision can be bad even if the outcome is good. Confusing these is the root of most decision-making errors.

Good decision, bad outcome. You evaluated the options carefully, weighed the probabilities, chose the highest expected value action. The low-probability bad outcome happened anyway. This is not a decision error; it is luck. Recording it as a decision error would corrupt future decisions.

Bad decision, good outcome. You took a reckless action that had a high probability of failure. It worked out anyway. Do not learn "reckless action is good" from this. Over time, bad decisions produce bad outcomes on average.

Discipline. Evaluate decisions by the process at the time, not by the outcome in retrospect. Annie Duke calls this "resulting" — judging decisions by outcomes — and identifies it as a primary corruption of the decision-making process.

Method 1 — Expected Value

Pattern: For each option, compute the sum over outcomes of (probability of outcome) × (value of outcome). Choose the option with the highest expected value.

Formula. EV(option) = Σ P(outcome_i) × V(outcome_i)

Worked example. Deciding whether to accept a 50% chance of winning $1000 or a guaranteed $400.

• Option A (gamble): 0.5 × $1000 + 0.5 × $0 = $500
• Option B (guaranteed): 1.0 × $400 = $400

EV favors option A. But EV is only the right criterion when the decision repeats many times. For a one-shot decision, loss aversion and risk tolerance matter.

Limitations.

• Requires probability estimates that are often unavailable or unreliable.
• Treats all values as commensurable (all convertible to a single currency).
• Ignores risk aversion, which is a legitimate preference for one-shot high-stakes decisions.
• Ignores variance — two options with the same EV but different variance are not equivalent for most humans.

Method 2 — Decision Trees

Pattern: Draw a tree with choice nodes (where the decision-maker picks) and chance nodes (where the world picks). Compute expected value back from the leaves to the root.

Worked example. Should you enter a new market?

Enter market (cost $1M)
├── Market succeeds (p=0.4) → +$5M
│   └── Net: +$4M
└── Market fails (p=0.6) → $0
    └── Net: -$1M

Do not enter
└── Net: $0

EV(Enter) = 0.4 × $4M + 0.6 × (-$1M) = $1.6M - $0.6M = $1M EV(Do not enter) = $0

Expected value favors entering, but risk tolerance, capital at stake, and opportunity cost all modify the final decision.

When trees help. Multi-stage decisions with contingencies. The tree forces explicit statement of all probabilities and payoffs, which exposes hidden assumptions.

Method 3 — Multi-Criteria Decision Analysis

Pattern: When a decision involves multiple criteria that cannot be converted to a single number (money, time, quality, risk, ethics), use a structured weighting.

Steps:

1. List the criteria that matter.
2. Assign each a weight (either by importance ranking or by paired comparison).
3. For each option, score it on each criterion (0-10 scale or similar).
4. Compute weighted sum: score(option) = Σ weight_i × rating_i
5. Compare.

Worked example. Choosing a job.

Criterion	Weight	Job A	Job B	Job C
Salary	0.3	8	6	9
Growth	0.2	7	9	5
Work-life balance	0.2	5	8	3
Mission alignment	0.2	6	9	4
Commute	0.1	8	6	9

• Job A: 0.3(8) + 0.2(7) + 0.2(5) + 0.2(6) + 0.1(8) = 6.8
• Job B: 0.3(6) + 0.2(9) + 0.2(8) + 0.2(9) + 0.1(6) = 7.6
• Job C: 0.3(9) + 0.2(5) + 0.2(3) + 0.2(4) + 0.1(9) = 6.0

The process does not replace judgment — the weights and scores reflect subjective values. But the structure prevents one criterion from dominating the decision by loudness or salience.

Method 4 — Pre-Mortem

Pattern: Before committing to a decision, imagine the decision has been made and failed spectacularly. Ask the team: "What were the reasons for failure?" Then treat the reasons as risks to mitigate.

Why it works. Standard risk analysis asks "what could go wrong?" which triggers defensiveness and optimism bias. Pre-mortem asks "why did it go wrong?" which treats the failure as a fact and surfaces reasons that would otherwise be suppressed.

Worked example. Planning a product launch. Pre-mortem question: "It is six months from now and the launch failed. What happened?"

Surfaced reasons:

• Delays in manufacturing caused missed launch window
• Marketing messaging did not resonate with target demographic
• Competitor launched similar product two weeks earlier
• Initial reviews were negative due to Day 1 bug
• Key partnership fell through

Each surfaced reason becomes a risk mitigation. Pre-mortem typically generates 30-50% more risk identifications than standard risk reviews.

Method 5 — Reversibility and the Two-Way Door

Pattern: Distinguish decisions by whether they can be easily reversed. Spend deliberation in proportion to reversibility.

• Two-way door (reversible). You can change your mind later at low cost. Decide quickly. The cost of delay usually exceeds the cost of a wrong choice.
• One-way door (irreversible). Once taken, the decision cannot be undone or can be undone only at great cost. Deliberate thoroughly. The cost of a wrong choice exceeds the cost of delay.

Worked example. Deciding which text editor to use (two-way door) should take a few minutes at most. Deciding whether to move to another country for a job (one-way door for at least months, often years) warrants substantial deliberation.

Common error. Treating reversible decisions as irreversible. This produces analysis paralysis on low-stakes choices and leaves no time for the decisions that actually matter.

Method 6 — Minimax Regret

Pattern: Instead of maximizing expected value, minimize the maximum regret across scenarios. Useful when uncertainty is extreme and loss aversion is justified.

Worked example. Deciding whether to buy earthquake insurance in a low-probability zone.

• Option A (buy insurance): Cost $500/year. Regret if no earthquake: $500. Regret if earthquake: Small residual from deductibles.
• Option B (no insurance): Cost $0. Regret if no earthquake: $0. Regret if earthquake: Total loss, ~$500,000.

Max regret for Option A: $500. Max regret for Option B: $500,000. Minimax regret chooses A despite low probability of earthquake, because the asymmetry of regret justifies the premium.

Method 7 — Satisficing

Pattern: Instead of searching for the best option, set minimum criteria and take the first option that meets them. Simon's concept — bounded rationality accepts "good enough" because searching for "best" has costs.

When satisficing is appropriate.

• The cost of search (time, effort, opportunity cost) exceeds the marginal value of a better option.
• The option space is unbounded or poorly defined.
• Multiple options are roughly equivalent.
• The decision is not worth extensive deliberation.

Worked example. Choosing a restaurant for dinner. There are 200 restaurants in the city. Visiting all is impossible. Set minimum criteria (under $30, opens now, walking distance, positive reviews). Go to the first one that matches. Do not second-guess.

Common error. Maximizing on trivial decisions. Spending an hour on which brand of pasta to buy is a sign that the satisficing threshold is badly calibrated.

Method 8 — System 1 vs. System 2 Allocation

Pattern: Match the cognitive mode to the decision. System 1 is fast, intuitive, automatic. System 2 is slow, deliberate, effortful. Most decisions should use System 1, but high-stakes ones warrant System 2.

System 1 is appropriate when:

• The decision is routine.
• Expert pattern recognition applies.
• The stakes are low relative to decision cost.
• Speed is more valuable than precision.

System 2 is appropriate when:

• The decision is novel.
• Biases are likely to distort System 1 judgment.
• The stakes justify the effort.
• The decision is irreversible.

Discipline. System 1 cannot be fully disabled; the question is whether System 2 is engaged on top of it. For high-stakes decisions, actively slow down — write things out, sleep on it, consult others.

Method 9 — Pros and Cons with Weights

Pattern: For everyday decisions where formal MCDA is overkill, list pros and cons for each option and assign rough weights (high/medium/low). The output is more transparent than unstructured gut feel and faster than a full MCDA matrix.

Franklin's moral algebra. Benjamin Franklin's 1772 letter to Joseph Priestley describes the technique: write pros and cons in two columns, then strike out a pro and con of equal weight. The remaining items reveal the dominant side.

Method 10 — Stopping Rules

Pattern: Decide in advance when you will stop deliberating. Write the rule down before analysis begins.

Examples of stopping rules.

• "I will decide by Friday at 5pm regardless of whether I have more information."
• "If the top two options are within 10% on the weighted criteria, I will pick by flipping a coin."
• "I will gather three quotes, then decide."

Why it matters. Without a stopping rule, deliberation expands to fill available time (Parkinson's law for decisions). Analysis paralysis is not a personality trait; it is the absence of a stopping rule.

Standard Decision Procedure

When facing a decision that warrants deliberate thought:

1. State the decision. What choice are you actually making? What are the real alternatives?
2. Reversibility check. Two-way door or one-way door?
3. Clarify the criteria. What do you actually care about? Rank or weight.
4. Gather evidence proportionally. More evidence for irreversible decisions.
5. Set a stopping rule. When will you decide?
6. Pre-mortem. Imagine the decision failed. What went wrong?
7. Apply the appropriate method. EV for repeatable, MCDA for multi-criteria, satisficing for routine.
8. Sleep on it if the decision is non-urgent and reversible overnight.
9. Commit. Make the decision and move on.
10. Record the reasoning so the decision can be evaluated later on process, not outcome.

When to Use

• Choices where the stakes justify deliberation
• Decisions made by a group that need a shared structure
• Irreversible or high-consequence decisions
• When you suspect your intuition is being distorted by bias
• When you need to explain the decision to others afterward

When NOT to Use

• Routine decisions where intuition is reliable
• Trivial choices where the deliberation cost exceeds the value
• Emergency situations where speed matters more than precision
• Decisions about pure preferences where "correct" does not apply

Common Mistakes

Mistake	Why it fails	Fix
Resulting (judging by outcome)	Confuses luck with skill	Evaluate the process at decision time, not after
Treating reversible as irreversible	Wastes deliberation on low-stakes choices	Apply reversibility check first
Treating irreversible as reversible	Commits too quickly to binding choices	Slow down for one-way doors
Ignoring base rates	Miscalibrates probability estimates	Use historical frequencies as starting points
Letting one criterion dominate	Over-weights salient concerns	Use MCDA or weighted pros/cons
No stopping rule	Produces analysis paralysis	Write the rule at the start

Cross-References

• kahneman-ct agent: System 1 / System 2 framework and dual-process decision-making.
• tversky agent: Expected value, base rates, biases in decision-making.
• paul agent: Integration of decision-making into the elements of reasoning.
• dewey-ct agent: Reflective thinking as the meta-skill behind deliberate decision-making.
• argument-evaluation skill: Evaluating the reasons for a decision.
• cognitive-biases skill: Biases that corrupt even structured decisions.
• evidence-assessment skill: Assessing the evidence that feeds into decisions.

References

• Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
• Duke, A. (2018). Thinking in Bets: Making Smarter Decisions When You Don't Have All the Facts. Portfolio.
• Klein, G. (2007). "Performing a Project Premortem." Harvard Business Review, September.
• Simon, H. A. (1956). "Rational Choice and the Structure of the Environment." Psychological Review, 63, 129-138.
• Hammond, J. S., Keeney, R. L., & Raiffa, H. (1999). Smart Choices: A Practical Guide to Making Better Decisions. Harvard Business School Press.
• Franklin, B. (1772). Letter to Joseph Priestley, September 19. (Moral algebra.)