methodatlas
Method guideDecision Making

Decision Tree

A practical method guide: clarify prerequisites and materials, understand the complete process, and look up outputs, pitfalls, and stop criteria.

ComplexityMedium
Time30-90 min
Participants1-6
FormatWorkshop + async
MaturityCanonical
Plan the work
01

Prerequisite

What needs to be finished first

Complete firstClear working frame

Write the decision, options, external events, consequences and decision criteria.

Without a bounded question or checkable evidence, the result will be unclear.
02

Preparation

What needs to be ready before start

Materials

Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

People / roles

Decision owner · subject experts on uncertainty · data specialist for quantitative evaluation.

Pre-read

Choose materials, data access and participants to fit the research or decision question.

Time needed

30–90 minutes for a small tree; data collection and valuation are separate.

Setup

Open the method-specific structure. Fictional examples are for orientation only; keep the blank worksheet separate from actual findings.

03

Core question

The one question this method answers

Which actions and uncertainties lead to which outcomes, and what assumptions support the choice?

04

Flow

Phases with goal, actions, output and pitfall

A decision tree represents choices, uncertain events, and consequences as branches. It clarifies options and assumptions; numeric probabilities belong only where they are justified.

Decision Tree working record

Fictional example

Method-specific prompts and a fictional worked example; template prompts remain empty.

Frame decision and outcomes

Name the purpose and decision owner.

**Decision node:** Add a desk at a new rental branch? Options: add / do not add.

Distinguish decision, chance and terminal nodes

Label every branch and node type.

**Chance node after each option:** Peak demand high or low; these events are outside direct control.

Add evidence and probabilities only when justified

Leave unsupported probabilities unknown.

**Terminal nodes:** Describe wait time, operating cost and customer experience qualitatively. Do not add numbers without evidence.

Compare paths and document choice

Test whether plausible assumptions change the choice.

**Next step:** Collect peak-period data, then assess conditional probabilities per path and document their source.

OpenLearn explains decision-analysis branches and chance probabilities; IBM provides separate ML tree terminology. This guide distinguishes decision, chance and terminal nodes and uses no unsupported probabilities.

Decision trees and dealing with uncertainty | Open University OpenLearn

  1. 1

    Question

    Frame decision and outcomes

    Goal

    Name the purpose and decision owner.

    Inputs for this step

    • Write the decision, options, external events, consequences and decision criteria.
    • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

    Actions

    • State decision, alternatives, consequences and criteria; distinguish a decision diagram from an ML model.
    • Record Decision question and criteria with its evidence status, source or open verification need.

    Output

    Decision question and criteria

    Decision question and criteria

    Quality check

    Is the tree purpose clear?

    Pitfall

    A flowchart is confused with predictive machine learning. State model type and data basis.

    Timing: About 10–20 minutes; schedule data or clarification work separately.

  2. 2

    Nodes

    Distinguish decision, chance and terminal nodes

    Goal

    Label every branch and node type.

    Inputs for this step

    • Decision question and criteria
    • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

    Actions

    • Use decision nodes for controlled choices, chance nodes for uncertain events and terminal nodes for outcomes.
    • Record Tree with clear node semantics with its evidence status, source or open verification need.

    Output

    Tree with clear node semantics

    Tree with clear node semantics

    Quality check

    Are options separate from uncertain events?

    Pitfall

    Chance event is shown as a controllable choice. Separate decision maker from external uncertainty.

    Timing: About 10–20 minutes; schedule data or clarification work separately.

  3. 3

    Evidence

    Add evidence and probabilities only when justified

    Goal

    Leave unsupported probabilities unknown.

    Inputs for this step

    • Tree with clear node semantics
    • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

    Actions

    • Add conditional probabilities at chance nodes only with evidence; branches at each node must sum to 1. Cite source and time basis for values.
    • Record Traceable branch assumptions with its evidence status, source or open verification need.

    Output

    Traceable branch assumptions

    Traceable branch assumptions

    Quality check

    Are conditional probabilities evidenced and complete?

    Pitfall

    Unsupported percentages create false precision. Mark unknown or test a range without fake values.

    Timing: About 10–20 minutes; schedule data or clarification work separately.

  4. 4

    Choice

    Compare paths and document choice

    Goal

    Test whether plausible assumptions change the choice.

    Inputs for this step

    • Traceable branch assumptions
    • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

    Actions

    • Review consequences, sensitivity and qualitative criteria; record rationale, owner and revision signal.
    • Record Reasoned choice and review date with its evidence status, source or open verification need.

    Output

    Reasoned choice and review date

    Reasoned choice and review date

    Quality check

    Is the choice justified without invented weights?

    Pitfall

    Diagram replaces ownership or value judgment. Keep uncertainty visible and schedule reassessment.

    Timing: About 10–20 minutes; schedule data or clarification work separately.

05

Artifact

What comes out at the end

Sketch: what the artifact looks like

Decision Tree · Optionen und FolgenAus einer Entscheidungsfrage Optionen, Bedingungen, Risiken und Outcomes als nachvollziehbaren Baum ableiten
Decision TreeDas Visual zeigt eine Entscheidungsfrage mit Optionen, Bedingungen und möglichen Outcomes als verzweigten Baum.Entscheidungspfade und Konsequenzen verzweigenOptionen werden mit Bedingungen verbunden, damit Outcomes und Annahmen nachvollziehbar werden.Option AOption Bwenn jawenn jahochunklarpasstLückeEntscheidungBuild oder Buy?Buildeigene LösungBuyStandardproduktSkaliert?Kapazität vorhandenFit?passt zu ProzessenCustom PathDifferenzierung hochPilotAnnahmen testenVendor PathTime-to-market kurzHybridLücke gezielt bauen
Form

Versioned record of question, evidence, result, uncertainty, owners and next review date.

Versioning / ownership

Track date, scope, sources, assumptions and changes; assign an owner and review for ongoing practice or initiatives.

Tool alternatives
  • Paper and pens
  • Whiteboard or simple table

decision-tree-working-template.md

Plan a session or initiative to match the actual scope of work.

Decision Tree: Worksheet

Question

To be confirmed

Desired outcome

To be confirmed

Scope

Prepare the full method Time: 30–90 minutes for a small tree; data collection and valuation are separate.

Complete this worksheet on paper or in your own document during the work.

Preparation for this scope

Method setup

  • Materials: Whiteboard, decision evidence, probability/value sources, criteria and decision owner.
  • Roles: Decision owner · subject experts on uncertainty · data specialist for quantitative evaluation.
  • Advance information: Choose materials, data access and participants to fit the research or decision question.
  • Overall time needed: 30–90 minutes for a small tree; data collection and valuation are separate.
  • Setup: Open the method-specific structure. Fictional examples are for orientation only; keep the blank worksheet separate from actual findings.

Prepare for the work steps

Question: Frame decision and outcomes

  • Write the decision, options, external events, consequences and decision criteria.
  • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

Nodes: Distinguish decision, chance and terminal nodes

  • Decision question and criteria
  • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

Evidence: Add evidence and probabilities only when justified

  • Tree with clear node semantics
  • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

Choice: Compare paths and document choice

  • Traceable branch assumptions
  • Whiteboard, decision evidence, probability/value sources, criteria and decision owner.

Decision Tree working record

Frame decision and outcomes

Name the purpose and decision owner.

...

Distinguish decision, chance and terminal nodes

Label every branch and node type.

...

Add evidence and probabilities only when justified

Leave unsupported probabilities unknown.

...

Compare paths and document choice

Test whether plausible assumptions change the choice.

...

Blank worksheet contains prompts only, with no example data.

Decision trees and dealing with uncertainty | Open University OpenLearn

Question: Frame decision and outcomes

Expected artifact: Decision question and criteria

Decision question and criteria

Entry:

...

  • Is the tree purpose clear?

Nodes: Distinguish decision, chance and terminal nodes

Expected artifact: Tree with clear node semantics

Tree with clear node semantics

Entry:

...

  • Are options separate from uncertain events?

Evidence: Add evidence and probabilities only when justified

Expected artifact: Traceable branch assumptions

Traceable branch assumptions

Entry:

...

  • Are conditional probabilities evidenced and complete?

Choice: Compare paths and document choice

Expected artifact: Reasoned choice and review date

Reasoned choice and review date

Entry:

...

  • Is the choice justified without invented weights?

Open questions and next steps

...

Method guide: https://methodatlas.meierhoff-systems.de/en/methods/decision-tree/run-sheet

06

Example output

Concrete filled scenario, fictional example

Fictional example for orientation. Counts and timings describe this case and are not universal requirements.

decision-tree-beispiel.md

Concrete filled scenario, fictional example

Fictional example: additional check-in desk

Decision node: Add a desk at a new rental branch? Options: add / do not add.

Chance node after each option: Peak demand high or low; these events are outside direct control.

Terminal nodes: Describe wait time, operating cost and customer experience qualitatively. Do not add numbers without evidence.

Next step: Collect peak-period data, then assess conditional probabilities per path and document their source.

Decision: Choose after evidence review and set a seasonal reassessment.

07

Pitfalls

Recognize symptoms and steer against them

Quick overview, the full text is in the matching phase in the flow

  1. Frame decision and outcomesA flowchart is confused with predictive machine learning.View in phase
  2. Distinguish decision, chance and terminal nodesChance event is shown as a controllable choice.View in phase
  3. Add evidence and probabilities only when justifiedUnsupported percentages create false precision.View in phase
  4. Compare paths and document choiceDiagram replaces ownership or value judgment.View in phase
08

Stop criteria

When to pause, resolve prerequisites, or choose another approach

Do not invent probabilities to make a tree calculable. If outcomes are incomplete, clarify the decision; if probabilities matter, collect evidence or use defensible ranges.

Where to go next?

Paper illustration for Trade-off Analysis.
Decision Making
Trade-off Analysis

Also compares the chosen paths against accepted trade-offs.

Paper illustration of an assumption matrix with prioritized test cards
Decision Making
Assumption Surfacing

Makes the assumption behind the identified data gap explicit and plans a test step.

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