Simple Decisions

All the machinery so far exists to serve one moment: the choice. This chapter turns beliefs and preferences into a defensible decision — and tells you when to pay for more certainty first.

Probabilistic reasoning describes the world; it does not tell you what to do about it. For that you need to fold in preferences — what outcomes are worth to you — and combine them with your beliefs. Simple decisions are the one-shot version of that problem, and they contain the ideas that make risk analysis quantitative rather than merely descriptive.

1From coherent preferences to a utility

Suppose your preferences over uncertain outcomes obey a few reasonable conditions — you can compare any two options, your preferences don’t form contradictory loops, and mixing in a little of a better option never makes things worse. A foundational result says that any such coherent preferences can be captured by a single number, a utility, assigned to each outcome, such that the rational choice is the one with the highest expected utility — the probability-weighted average of the utilities it might yield.

This is the quiet engine under all of decision analysis: don’t chase the best possible outcome, and don’t merely avoid the worst — weigh each outcome by how likely it is and how much it’s worth, and pick the option whose weighted total is highest.

2Why a rational manager is risk-averse

Crucially, utility is not money. For almost everyone the utility of money is concave: the first \$100k of a loss hurts more than the tenth \$100k, and a windfall’s tenth dollar means less than its first. Concave utility produces risk aversion as a logical consequence, not a character flaw.

That single fact explains behaviour that “expected monetary value” alone calls irrational. You will accept a certainty equivalent — a guaranteed outcome worth less than a gamble’s expected value — simply to be rid of the variance. The gap between the two is the risk premium: what you’ll rationally pay to avoid exposure. It is why firms carry contingency, buy insurance, and prefer a reliable \$1M to a coin-flip between \$0 and \$2.2M, even though the flip “wins” on average.

Figure 1. With a concave utility curve, a guaranteed outcome (on the curve) is worth more than a gamble with the same average money (on the dashed chord). The sure amount you’d swap the gamble for — the certainty equivalent — sits to the left of the expected value. The distance between them is the price of risk.

3Laying the decision out: decision trees

To structure a real choice, extend the network with two new kinds of node: a decision node for the levers you control, and a utility node for what you value. The unrolled picture is the familiar decision tree — square decisions branching into round chance events branching into outcomes. You solve it by working backwards: average the utilities at each chance point by their probabilities, then at each decision point keep the branch with the highest expected utility. What’s left is the optimal policy and its value.

Figure 2. Folding back a decision tree. Averaging each chance node by its probabilities gives “proceed now” an expected value of +40 and “de-risk first” +56 — so the disciplined move is to de-risk, even though proceeding has the higher upside. (A risk-averse decision-maker, valuing the smaller downside, would favour it by an even wider margin.)

4What is more certainty worth? Value of information

The most valuable idea in this chapter for a risk manager is the value of information. Before commissioning a geotechnical survey, a prototype, or a market study, you can compute how much better your decision could become if you knew the result in advance. That expected improvement is a hard ceiling on what the information is worth paying for.

Sometimes it’s large — the survey could flip your decision, and paying for it is obviously right. Sometimes it’s zero: if no possible result would change what you do, the study is worthless no matter how interesting, and buying it is theatre. Value of information turns “should we investigate first?” from an instinct into a calculation, and it is one of the highest-leverage moves in the entire discipline.

Key idea

Rational choice under uncertainty means maximising expected utility, not expected money. That one substitution makes risk aversion, contingency, insurance, and the price of information all fall out as consequences rather than exceptions.

Where Part I has taken us

Representation gave us a shared model of uncertainty; inference let us update it with evidence; parameter and structure learning let us build and calibrate it from data. Simple decisions closed the loop — turning all of it into a single, defensible choice. Everything in Part I, though, assumed the decision was a one-shot. Real projects unfold over time, where today’s choice reshapes tomorrow’s options.

What it means in practice

Score your options by probability-weighted value to the business, not by best case or worst case alone — and make your risk appetite explicit as a utility curve rather than leaving it to whoever is loudest in the room. Lay real decisions out as trees and fold them back; the discipline routinely overturns the gut answer. And before you spend on studies, surveys, or pilots, ask what result could actually change your decision. If none could, you already have your answer — keep your money.

Handbook application: from concept to controlled practice

Purpose. This expanded section turns the original page into a practical handbook. It preserves the supplied material and adds a repeatable way to apply, check and review Simple Decisions. It does not replace a contract, legislation, a controlled standard, competent engineering judgement or specialist advice.

The operating aim is to convert the subject into a governed decision, owned work, usable evidence and a reviewable outcome. Read the original explanation first, then use the workflow and checks below to convert knowledge into evidence.

Use Simple Decisions as a decision instrument rather than an administrative form. The subject terms—decision, utility, value, information, preferences—need an explicit connection to the project objective, business value and stakeholder commitments. Before completing the artefact, write one sentence stating who will use it, what decision it supports and when that decision is required.

Apply a disciplined information model. Separate facts supported by evidence, forecasts derived from a method, assumptions awaiting validation, constraints that limit choice, risks that may occur, issues that already exist and actions assigned to people. Each material entry should have an owner, date, status and next review point. Where probability or impact scores are used, define the scale so different reviewers interpret it consistently.

A baseline is useful only when changes are visible. Give the artefact an identifier, version, approval state and effective date. Define which changes require reapproval, how superseded versions are retained and where supporting evidence is stored. During reviews, focus on exceptions, decisions and trends rather than reading every field aloud. Record the decision and rationale, not merely that a meeting occurred.

Close the loop beyond delivery. Confirm acceptance criteria, unresolved items, transferred responsibilities and operational ownership. Where benefits are expected, identify the outcome measure, baseline, target, observation period and owner who remains accountable after the project team disbands. Lessons should describe the condition, consequence and reusable action; a generic statement such as “communicate better” cannot improve the next project.

Step-by-step operating method

Clarify the decision. Name the outcome, sponsor, affected stakeholders and decision that this work must enable.
Set boundaries. Record scope, assumptions, constraints, dependencies, tolerances and escalation conditions.
Plan the evidence. Define deliverables, measures, owners, due dates and acceptance criteria before execution.
Control delivery. Compare actual performance with the baseline, assess changes and manage risks and issues explicitly.
Close the loop. Confirm acceptance, transfer ownership, capture lessons and track benefits beyond handover.

Completion and governance protocol

Start with a short drafting workshop involving the accountable owner and the people who hold the evidence. Complete high-consequence fields first: objective, scope, owner, baseline, acceptance, dependencies and escalation. Mark unknowns as assumptions or actions rather than hiding them behind vague prose. Circulate a review draft, resolve conflicting interpretations, baseline the approved version and place the next review date in an owned schedule.

Information type	Minimum useful content	Review test
Outcome	Observable change and intended recipient	Not merely a deliverable or activity
Measure	Definition, baseline, target, frequency and source	Two reviewers would calculate it the same way
Ownership	One accountable role plus contributors and approver	Authority matches responsibility
Uncertainty	Assumption, risk or issue with response and trigger	Status reflects current reality
Control	Version, approval, review date and change rule	Current baseline is identifiable

Common failure modes and recovery actions

1. Watch for

Producing a document with no named decision or accountable owner.

Recovery: Return to the governing definition or requirement and restate the decision in one sentence.

2. Watch for

Mixing risks, current issues, assumptions and actions in one unstructured list.

Recovery: Separate evidence from assumption, assign an owner and set a date for validation.

3. Watch for

Measuring activity or output while leaving the intended outcome undefined.

Recovery: Run a small counterexample, boundary test, pilot or independent check before proceeding.

4. Watch for

Accepting changes without evaluating effects on value, scope, schedule, cost and risk.

Recovery: Record the consequence, decision and rationale, then update the controlled baseline.

5. Watch for

Closing the project at delivery even though benefit ownership has not transferred.

Recovery: Escalate when the issue affects safety, compliance, acceptance, material value or an agreed tolerance.