Decision Analysis

Decision analysis is the disciplined process, from operations research and decision science, for making a hard choice under uncertainty. It separates what you can choose (alternatives), what you cannot control (uncertainties), and what you want (objectives and preferences), then combines them so the logic of the choice is visible and checkable. The point is not to compute the answer by machine but to produce insight and a defensible, communicable decision.

Input required

• The decision problem and who owns it.
• Objectives — what the decision-maker is trying to achieve, and the trade-offs between them.
• Alternatives — current options, and room to generate better ones.
• Uncertainties — the unknowns that affect outcomes.
• Constraints and timing — budget, deadline, what can be learned before deciding.

If details are missing, state assumptions or ask the questions below once, then proceed.

Questions to ask first

Ask in one batch of at most five questions, skipping any already answered, then proceed:

1. What decision is on the table, who makes it, and by when?
2. What would a good outcome look like, and what trade-offs are you willing to make (cost vs. speed vs. risk)?
3. What are the options, and are there others not yet considered?
4. Which unknowns matter most, and what do you know about them?
5. Could you learn more before deciding, and at what cost?

Method

1. Frame the decision. Write the decision statement, its scope, what is already decided, what is decided later, and the deadline. A good frame prevents solving the wrong problem.
2. Define objectives. Separate fundamental objectives (what you ultimately want) from means. Turn each into a measure with a direction and range.
3. Generate alternatives. Go beyond the obvious two: combine, stage, delay, or hedge. Include a baseline of doing nothing.
4. Identify uncertainties. List the unknowns that could change the ranking; assign each a range or distribution, using data, base rates, and expert judgment. Note where estimates conflict.
5. Model the decision. Link alternatives, uncertainties, and outcomes with a Decision Tree or an influence diagram. Use Weighted Decision Matrix logic when there are several objectives, and Expected Value Analysis for the uncertainty.
6. Capture risk preference. Decide whether expected value is adequate or whether the downside needs weighting (see Prospect Theory for how people actually feel about loss).
7. Evaluate. Compute the value of each alternative and look at the full distribution, not only the average.
8. Run sensitivity analysis. Vary each uncertain input across its range (a tornado chart is the usual way to show it). Find the few inputs that change the decision.
9. Value information. For each key uncertainty, estimate the expected value of perfect information; if it exceeds the cost of a test or study, gather it first.
10. Decide and communicate. State the choice, why, the main risks, and what would change it. Plan the follow-up: milestones and tripwires.

Output format

1. Decision frame (3–5 lines)
2. Objectives and measures (short list)
3. Alternatives (list)
4. Key uncertainties with ranges
5. Model and results — a tree or table, with expected value and range per alternative
6. Sensitivity — the inputs that matter most
7. Value of information — worth buying or not
8. Recommendation, risks, and review points

Ground rules

• Frame before computing. A precise answer to the wrong question is worse than a rough answer to the right one.
• Separate facts, judgments, and preferences. Label each.
• Use ranges. Single-point estimates hide uncertainty.
• Keep the model as simple as the decision allows. Add detail only where sensitivity shows it matters.
• The analysis informs the decider; it doesn't replace them.

Self-check before answering

• Is the decision statement clear and the scope bounded?
• Are objectives separated from alternatives?
• Does the evaluation show spread and not only averages?
• Did sensitivity analysis identify what actually drives the choice?
• Is there a recommendation with conditions for changing it?

Common pitfalls

• Solving the wrong problem — a poor frame.
• Too few alternatives — a choice between two bad options.
• Anchoring on a favorite — and building the analysis to justify it.
• Overconfident estimates — narrow ranges that ignore surprise.
• Analysis paralysis — modeling long after the decision is clear.
• Ignoring the value of waiting or learning — decisions can often be staged.

Example prompt

We must decide whether to build or buy a new billing system. Do a decision analysis: frame it, define objectives, model the main uncertainties, run sensitivity, and tell me if a pilot is worth the cost before we commit.

Learn more

• Decision Tree and Expected Value Analysis for the core computations.
• Weighted Decision Matrix for multiple objectives.
• Game Theory when other parties respond strategically.
• Prospect Theory for how people evaluate risk and loss.
• Premortem and Tripwire for guarding the chosen course.
• Decision Journal for recording and later judging the decision.