Chapter 8: Decision Making Under Uncertainty

Learning Objectives

By the end of this chapter, you should be able to:

distinguish risk from uncertainty

identify assumptions that affect a decision

evaluate multiple possible outcomes

make decisions when information is incomplete

use scenarios and sensitivity analysis

build recommendations that remain resilient when assumptions change

Why This Matters

Case competitions rarely give you perfect information. You may not know:

exactly how customers will respond;

how competitors will react;

whether a market will grow;

whether implementation will proceed as planned;

whether your financial assumptions will hold.

Yet you still have to make a recommendation. That is the nature of business. Leaders rarely make decisions with complete information. The goal isn't to eliminate uncertainty. It is to make a good decision despite uncertainty.

Discover Your Mad Skills Principle

You don’t need certainty to make a decision. You need a defensible decision and a plan for managing uncertainty.

This is one of the most important lessons in case competitions. Students often try to make their recommendation sound certain. Experienced decision makers recognise uncertainty and manage it.

Deciphering Case Characteristics

The importance of uncertainty depends on the case. A decision involving a proven technology may have relatively low uncertainty. A new product entering an emerging market may have tremendous uncertainty. A startup case may involve uncertainty around almost every major assumption. A market-entry case may involve uncertainty around:

demand;

competition;

regulation;

pricing;

customer adoption.

Recognising the level of uncertainty should influence both your analysis and your recommendation.

Risk vs Uncertainty

A useful distinction is:

Risk: You can identify possible outcomes and estimate their probabilities.

Uncertainty: You cannot confidently estimate the probability of outcomes.

For example:

If historical data suggests a 20% chance of a particular outcome, you may be dealing with risk.

If the company is entering a completely new market with no reliable historical data, you may be dealing with uncertainty.

Both require judgment.

The Assumption Test

Every recommendation contains assumptions. Identify the three to five assumptions that matter most. For each, ask: What happens if this assumption is wrong? This simple question can reveal the vulnerabilities in your recommendation.

Scenario Analysis

Instead of creating one forecast, consider several.

Base Case: What happens if things develop as expected?

Upside Case: What happens if conditions are better than expected?

Downside Case: What happens if important assumptions fail?

The purpose isn't to predict the future. It is to understand how resilient the recommendation is.

Sensitivity Analysis

Sensitivity analysis asks: Which assumptions matter most to our decision?

For example, if project success depends heavily on customer adoption, test different adoption rates. If the recommendation remains attractive across reasonable scenarios, confidence increases. If a small change makes the project unattractive, that assumption deserves attention.

Decision Trees

Video: Decision Trees Visually Show Your Strategic Choices and Why You Chose Them

Decision trees can help when:

decisions happen in stages;

different outcomes lead to different next steps;

probabilities can be estimated;

the Value of future choices matters.

They help visualise:

Decision → Outcome → Decision → Outcome

Expected Value

When probabilities are available, expected Value can help compare uncertain alternatives.

A simplified version is: Expected Value = Probability × Outcome

For multiple outcomes: Expected Value = Σ (Probability × Outcome)

The calculation doesn't decide for you. It provides another piece of evidence.

Real Options Thinking

Sometimes the best decision isn't to make a large commitment immediately.

You may be able to:

run a pilot;

test the market;

launch in one region;

conduct customer research;

create a minimum viable product.

These actions generate information. That information has Value. A staged investment can therefore be a strategic response to uncertainty.

Coach's Lens

One of the strongest phrases a team can use in a competition is: "Our recommendation depends on three critical assumptions." That statement shows awareness.

Even better: "We have designed the implementation to test those assumptions before making the full investment." Now you are not simply acknowledging uncertainty. You are managing it. That is what business leaders do.

Common Mistakes

Pretending Uncertainty Doesn't Exist

Every forecast contains assumptions.

Using Fake Precision

A projection of "$4,283,721" may imply more certainty than the analysis supports.

Building Only One Scenario

One forecast is not a strategy for uncertainty.

Overanalysing Every Possibility

You cannot model every possible future.

Focus on uncertainties that could materially change the decision.

Failing to Build Flexibility into Implementation

A good strategy can adapt as new information becomes available.

Competition Example

A company is considering entering a new international market. Your model predicts:

$10 million revenue;

15% margin;

three-year payback.

The team could present these numbers. A stronger team asks:

What if adoption is 20% lower?

What if customer acquisition costs are 30% higher?

What if the competitor responds aggressively?

What if regulatory approval takes six months longer?

The analysis might reveal that the strategy is attractive under most conditions but fails under a particular scenario. That insight changes the recommendation. Perhaps the team now recommends:

A phased market entry beginning with a pilot in one region, with expansion contingent on predefined customer adoption and profitability thresholds.

The uncertainty hasn't disappeared. But it has been managed.

Mad Skills Drill

Take one recommendation from a previous case. Identify:

Three Critical Assumptions: What must be true for your recommendation to succeed?

Three Risks: What could go wrong?

Three Scenarios: What happens if conditions are better, expected, or worse?

One Trigger: What evidence would cause you to change your strategy?

Reflection Questions

Which assumption in your last recommendation was most uncertain?

Did your team test it?

What would happen if it were wrong?

Could your implementation have been designed to learn more before committing significant resources?

Chapter Summary

Business leaders rarely have perfect information. Neither do case-solving competitors.

The objective is not to eliminate uncertainty. It is to:

Identify it.

Measure it where possible.

Understand its impact.

Manage it.

Build flexibility into the decision.

A strong recommendation doesn't say: "We know exactly what will happen."

It says: "Based on what we know, this is the best decision—and here is how we will manage what we don't know."

Key Takeaways

✓ Separate risk from uncertainty.

✓ Identify the assumptions that matter most.

✓ Test critical assumptions.

✓ Use scenarios and sensitivity analysis.

✓ Consider staged decisions when uncertainty is high.

✓ Build trigger points into implementation.

✓ Don't confuse precision with certainty.