Slideshow 4 - Uncertainty Decision Analysis and Heuristics

What is Decision Analysis (DA)?

A structured, social–technical process for making big, complex decisions using people + data + analysis.

When is DA used?

When decisions are hard, important, and uncertain — with multiple stakeholders, conflicting objectives, high uncertainty, complex alternatives, and high stakes.

What is the social purpose of DA?

To give credible, understandable, and timely insights to decision makers and stakeholders.

What is the technical purpose of DA?

To use probability, value, and utility theory and analytics to compare complex options under uncertainty.

What makes a “good decision”?

Logic, structure, clear objectives, good analysis, best available info, and consideration of uncertainty.

Why can good decisions have bad outcomes?

Outcomes depend on external events and chance, not just the decision process.

What’s the key difference between a good decision and a good outcome?

Good decision = good process; Good outcome = actual result (can be lucky or unlucky).

What are the three conditions required for DA?

Urgency, Importance, Difficulty.

What is the 1% rule?

You should spend ~1% of the resources involved in the decision to ensure the decision is made properly.

What is content complexity?

Whether the necessary information is available or accessible.

What is analytic complexity?

How difficult the information is to analyze.

What is organizational complexity?

Whether you have the facilitation and resources needed for the DA process.

What are the two parts of Parnell’s decision model?

Engagement and Technical Products.

Who are Decision Makers (DMs)?

People with authority to approve objectives, alternatives, and final decisions.

Who are Stakeholders (SHs)?

People affected by the decision who provide input, concerns, and priorities.

What does the Project Team do?

Gathers info, builds models, runs analyses, organizes meetings.

What are SMEs?

Subject Matter Experts who provide specialized knowledge and data.

What are process diagrams?

Visual maps showing decision steps, objectives, relationships, and alternatives.

What are models in DA?

Formal representations such as mathematical models, decision trees, and simulations.

What are analytic outputs?

Rankings, risk assessments, probability results, and value scores.

What is Dialogue Decision Making?

Two teams (Decision Board + Project Team) alternating tasks over 2–4 months.

What is Decision Conferencing?

A 2–3 day intensive workshop with DMs, SHs, and facilitators.

What is a Strictly Analytical Process (flawed)?

Analysts and DMs barely interact, leading to technically good but socially bad decisions.

What is an Advocacy Process (flawed)?

One person pitches a preferred solution; alternatives ignored; biased.

What are the 10 steps of the DA process?

• Select decision process

• Frame decision

• Craft objectives

• Design alternatives

• Deterministic analysis

• Quantify uncertainty

• Probabilistic analysis (trees/simulations)

• Allocate resources

• Communicate insights

• Enable implementation

What are heuristics?

Rule-of-thumb shortcuts for making decisions with limited information.

Heuristics relate to which theories?

Bounded rationality + Prospect theory.

Risk vs uncertainty — what’s the difference?

Risk = known outcomes + probabilities;
Uncertainty = unknown probabilities (or unknown outcomes).

Why are heuristics useful under uncertainty?

They use limited info, avoid overfitting, and often outperform complex models.

What is ecological rationality?

A heuristic works well when it matches the structure of the environment.

What is the Gaze Heuristic?

Keep eyes on a ball and run so the angle stays constant — simple rule beats physics.

How is intuition related to heuristics?

Intuition is unconscious heuristics — fast, experience-based, effective under uncertainty.

When do heuristics work best?

When problems are complex, info is incomplete, time is limited, or prediction is impossible.

What is Design Thinking?

A problem-solving approach that blends analysis + intuition to create innovation.

Why is both analysis and intuition needed?

Analysis improves existing ideas; intuition creates new ones. Together → innovation.

What are the three Roger Martin stages?

Mystery → Heuristic → Algorithm

What is “Mystery”?

Problem unclear; causes and solutions unknown.

What is a “Heuristic” in this model?

A guiding rule that narrows possibilities and gives direction.

What is an “Algorithm”?

A repeatable, scalable, teachable solution that can be coded or formalized.

Why does this progression matter?

Strategy = turning heuristics into algorithms so solutions can scale.

What does design thinking aim for?

Innovation, not small improvements.

What is abductive reasoning?

Making the best possible guess based on incomplete clues — creative hypothesis formation.