Representation

Before you can manage a risk, you have to write it down in a form you can actually reason with. That form is probability.

Every project decision worth making is made in a fog. Will the ground conditions hold? Will the long-lead item arrive? Will the client sign off on time? A risk register full of adjectives — “high”, “likely”, “severe” — feels like a handle on that fog, but adjectives don’t combine. You cannot add “high” to “moderate”. Representation is the discipline of encoding what you believe in a form that does combine: numbers that obey consistent rules, arranged so a team can compute with them.

1Belief as a number

A probability is a degree of belief — a number between 0 (certain it won’t happen) and 1 (certain it will). It doesn’t claim the world is random; it measures your confidence given what you know. Two rules keep beliefs coherent: the probabilities of a complete set of mutually exclusive outcomes sum to 1, and nothing sits outside that range. That is almost the entire foundation. Everything else is bookkeeping built on top of it.

A probability distribution spreads belief across the values a variable can take. For a discrete variable — will the permit clear this month: yes or no — it’s a small table. For a continuous one — how many days late will delivery be — it’s a curve (a density) such as the familiar bell-shaped Gaussian, described compactly by a mean and a spread.

2The trap of the joint distribution

Real risk lives in the interactions. Rain matters because it stalls the subcontractor, which matters because it slips the schedule, which matters because it triggers a penalty. To capture all of that at once you’d write a joint distribution: the probability of every combination of every variable together.

This is where naïve modelling collapses. Ten yes/no risk factors have 2¹⁰ = 1,024 combinations; twenty have over a million; thirty, over a billion. You could never fill that table in from experience, and no one could read it. The joint distribution is the honest, complete object — and it is hopeless to work with directly.

3The lever: conditional independence

The escape is an observation about how influence actually flows. A conditional probability, written P(slip | rain), asks: given that it rained, how likely is a slip? The chain rule lets any joint distribution be rebuilt as a product of such conditionals. On its own that doesn’t save you — but most factors don’t depend on most others. Once you know the schedule slipped, the cost overrun barely cares whether the cause was weather or labour. That is conditional independence: once you know the intervening cause, the earlier ones tell you nothing more.

Conditional independence lets you delete terms from the factorization. Each variable needs to be described only in relation to the few things that directly influence it — not the whole project at once.

4The Bayesian network

A Bayesian network makes this concrete. Draw each uncertain factor as a node; draw an arrow from a direct cause to its effect; forbid cycles. Each node carries a small conditional probability table giving its likelihood for each combination of its parents. The full joint distribution is then just the product, node by node, of these local tables — the graph is the set of independence assumptions you’re willing to make.

Figure 1. A project-delay network. Two root causes feed a common effect (schedule slip), which in turn drives two downstream consequences. Instead of one intractable table over all five factors, each node needs only a small table describing its direct parents.

The payoff is enormous. The exponential joint over five factors becomes five small, local tables — each of which a scheduler or estimator can actually populate from experience. Better still, the structure is now explicit and auditable: anyone can look at the diagram and challenge a specific arrow (“does rain really only act through the subcontractor?”) rather than arguing about a vague overall feeling.

Key idea

A Bayesian network trades one impossible table for many small, local ones — and in doing so turns a private hunch about risk into a shared, inspectable map of cause and effect.

5What this buys a risk manager

A well-built risk model is not a longer register; it is a causal one. The moment you commit to a network you have to state which factors drive which — and that conversation alone surfaces disagreements that a colour-coded heat map hides. The structure also becomes the scaffolding for everything that follows in this series: updating beliefs when evidence arrives (Chapter 2), calibrating the tables from data (Chapter 3), and even learning the structure itself when you’re unsure of it (Chapter 4).

What it means in practice

Replace the adjectives with a small diagram of causes and effects, and put a defensible number on each direct link. You don’t need a probability for every combination of everything — only for how each risk relates to its immediate drivers. That single reframing makes your risk model something a team can question, combine, and compute with, instead of merely nod at.

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 Representation. 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 Representation as a decision instrument rather than an administrative form. The subject terms—risk, bayesian, representation, probability, networks—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.