decision-making

# 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?" The