Chapter 13

Chapter 13: Decision Making Under Uncertainty - Making Strong Decisions When the Future Cannot Be Predicted With Confidence

Learning Objectives

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

• distinguish risk from uncertainty

• recognise different types of uncertainty in a case

• identify the genuinely unknown variables

• use expected value to evaluate uncertain outcomes

• understand probability-weighted decision making

•   build decision trees

• compare decisions across multiple possible futures

• consider the value of information

• recognise the importance of flexibility and real options

• incorporate risk into investment decisions

• design decisions that preserve flexibility

• communicate uncertainty confidently to judges

• make a recommendation even when the future is unclear

Why This Matters

Business decisions are rarely made with perfect information.

A company may be considering:

• entering a new market

• launching a new product

• acquiring another company

• building a new facility

• investing in technology

• expanding internationally

• changing its business model

Management may have forecasts.

• It may have historical data.
• It may have industry benchmarks.
• It may have consultants and analysts.

But nobody knows exactly what will happen. 

• The market may grow faster than expected. Or slower.
• Customers may adopt the product. Or they may not.
• A competitor may respond aggressively. Or do nothing.
• A regulation may change. Or remain unchanged.
• Interest rates may rise. Or fall.

This creates a fundamental challenge: How do you make a good decision when you cannot know the future with certainty? This is one of the most important skills in case solving.

Discover Your Mad Skills Principle

Good decision making is not about predicting the future perfectly. It is about making a decision that remains intelligent across plausible futures.

Risk vs. Uncertainty

These terms are often used interchangeably. They are not the same.

Risk

Risk exists when you can identify possible outcomes and assign reasonable probabilities to them. For example:

• There is a 60% probability that sales will be strong.
• And a 40% probability that sales will be weak.

You can model those outcomes.

Uncertainty

Uncertainty exists when the possible outcomes—or their probabilities—are much less certain. For example:

• A company is entering an entirely new market.
• There may be little historical information.
• Customer behaviour is unknown.
• Competitor reactions are difficult to predict.

You cannot confidently say: "There is a 65% probability of success." The information simply isn't strong enough.

A Useful Distinction

Think of the spectrum this way: Known --> Risk --> Uncertainty --> Unknown

As you move to the right, prediction becomes more difficult. Your decision-making approach needs to adapt.

The Problem With False Precision

One of the biggest mistakes in case competitions is assigning probabilities simply because the model requires them. For example:

• 70% probability of success
• 20% probability of moderate success
• 10% probability of failure

The numbers look sophisticated. But where did they come from? If the case provides no evidence for those probabilities, you may simply be creating false precision.

Discover Your Mad Skills Principle

A probability is only useful when you can explain why you believe it.

If you cannot defend the probability, don't pretend it is more precise than it is.

Part 1: Identify the Uncertainty

Before trying to calculate uncertainty, identify it. Ask: What don't we know? Typical areas include:

Market

• market size

• growth

• customer adoption

• competitive response

Financial

• revenue

• costs

• investment requirements

• interest rates

Operational

• implementation timing

• capacity

• productivity

• supply chain

Strategic

• competitor behaviour

• technological change

• regulatory changes

Human

• employee adoption

• management capability

• customer switching behaviour

Build an Uncertainty Map

A simple way to organise uncertainty is:

Uncertainty	Impact	Ability to Influence
Customer adoption	High	Moderate
Competitor response	High	Low
Implementation cost	Medium	High
Regulatory change	High	Low
Employee training	Medium	High

This immediately creates an important distinction. Some uncertainties are outside our control. Others can be actively managed. That distinction should influence the recommendation.

Control What You Can Control

Suppose customer demand is uncertain. You cannot force customers to buy. But you can:

• run a pilot

• test pricing

• conduct customer research

• launch in one market first

• create a minimum viable product

Similarly, if implementation costs are uncertain, you can:

• obtain supplier bids

• negotiate contracts

• stage the investment

• establish spending limits

The goal is not to eliminate uncertainty. It is to reduce the uncertainty that matters most.

Part 2: Probability-Weighted Decisions

When probabilities are reasonably defensible, expected value can be useful. The basic concept is: Expected Value = Σ (Probability × Outcome). For example:

Outcome	Probability	NPV
Strong market	30%	$8M
Base market	50%	$4M
Weak market	20%	-$2M

Expected NPV: (30% × $8M) + (50% × $4M) + (20% × -$2M) = $4.0M

The expected NPV is therefore: $4.0M

What Expected Value Does and Doesn't Tell You

Expected value provides a useful mathematical summary. But it does not mean: "We will make exactly $4 million." There may be no actual outcome of $4 million.

The organisation might make: $8M, $4M or -$2M. The expected value represents the probability-weighted average.

Discover Your Mad Skills Principle

Expected value is a decision tool—not a prediction of what will actually happen.

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Expected Value and Risk

Now compare two investments.

Investment A

Outcome	Probability	NPV
High	50%	$8M
Low	50%	$0M

Expected NPV: $4M

Investment B

Outcome	Probability	NPV
High	50%	$5M
Low	50%	$3M

Expected NPV: $4M

Both have the same expected value. But they are very different decisions.

Investment A has: higher upside and greater downside risk.

Investment B has: lower upside but greater stability.

Which is better? That depends on the organisation's:

• risk tolerance

• financial capacity

• strategic priorities

• ability to absorb losses

Risk Tolerance Matters

Not every organisation should make the same decision.

• A startup with substantial growth ambitions may accept greater risk.
• A mature company with tight cash constraints may prioritise stability.
• A family-owned business may have different risk preferences than a publicly traded company.
• A company facing financial distress may prioritise cash preservation over long-term upside.

The financial result is therefore only part of the decision.

Part 3: Decision Trees

Decision trees are particularly useful when a decision involves:

• multiple possible outcomes

• sequential decisions

• probabilities

• future choices

A simplified structure looks like: Decision --> Market Outcome.

                                                                                  ↙ ↓ ↘

                                                                        Strong Base Weak

                                                                                    ↓

                                                                         Financial Result

The important idea is that the organisation may not have to make every decision today.

Example: Market Entry

Imagine a company is considering entering a new market. It has two choices.

• Option A: Launch immediately.
• Option B: Conduct a $1M pilot first.

The pilot provides information about customer demand. The decision tree might look like:

Pilot --> Strong demand → Expand | Weak demand → Stop

This changes the nature of the decision. Instead of asking: "Should we invest $10 million?" we ask: "Should we spend $1 million to learn whether the $10 million investment is worthwhile?" That is a much more sophisticated decision.

Discover Your Mad Skills Principle

Sometimes the best first investment is not the final investment. It is the investment that gives you better information.

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Part 4: The Value of Information

Information has value when it can change a decision. Suppose you are considering a $10M investment. You are uncertain about market demand. You could spend $500,000 on a market test.

• If the test tells you that demand is weak, you avoid the larger investment.
• If the test shows strong demand, you proceed with greater confidence.

The $500,000 is not simply a research expense. It may be an insurance policy against a much bigger mistake.

When Is Information Worth Buying?

Information is valuable when:

1. the decision is significant

2. uncertainty is material

3. the information can reduce uncertainty

4. the information can change the decision

5. the cost of obtaining the information is less than its potential benefit

This creates an important question: What would we need to know to make this decision with greater confidence?

The Value of a Pilot

A pilot can provide information about:

• customer demand

• willingness to pay

• operational feasibility

• employee adoption

• implementation costs

• technical performance

For example: Instead of a $10M full rollout, consider a $1M pilot

• Measure:

  • adoption

  • cost

  • customer satisfaction

  • operational performance

• Then decide: Scale or Stop

This creates flexibility.

Part 5: Real Options and Flexibility

Some decisions have value because they preserve future choices. Consider: "Should we build a $20 million facility immediately?" versus: "Should we lease temporary capacity while we test demand?"

The second option may not have the highest immediate NPV. But it preserves flexibility.

• If demand increases: Expand.
• If demand disappoints: Exit.

That flexibility has value.

Option Thinking

A decision can create future options such as:

• expand

• delay

• abandon

• scale

• acquire

• partner

• switch suppliers

• change pricing

• enter another market

When uncertainty is high, ask: Which decision gives us the most valuable future choices?

Discover Your Mad Skills Principle

Under uncertainty, flexibility can be worth more than maximizing the base-case forecast.

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Part 6: Reversible vs. Irreversible Decisions

This is another useful way to think about uncertainty.

Reversible Decision

The company can change direction relatively easily. Examples:

• pilot program

• temporary staffing

• limited marketing campaign

• short-term supplier agreement

These decisions can often be made with less information.

Irreversible Decision

The company commits substantial resources that are difficult to recover. Examples:

• major factory

• acquisition

• long-term infrastructure

• large specialised technology investment

These decisions deserve much greater analysis.

The Uncertainty/Commitment Matrix

Think about decisions using two dimensions:

• Level of Uncertainty
  
• Size of Commitment

Low Uncertainty + Low Commitment: Move quickly.	Low Uncertainty + High Commitment: Analyse carefully.
High Uncertainty + Low Commitment: Experiment.	High Uncertainty + High Commitment: Slow down, gather information, and stage the investment.

This is a powerful framework for case solving.

Part 7: Scenario Planning

When probabilities are difficult to establish, scenarios may be more appropriate. Instead of saying: "There is a 60% chance of high growth," you might develop:

Scenario 1: Rapid market growth

Scenario 2: Moderate market growth

Scenario 3: Market stagnation

Then ask: How does our strategy perform under each scenario?

Robust Strategies

Imagine three strategies.

Strategy	High Growth	Moderate Growth	Low Growth
A	$10M	$5M	-$2M
B	$8M	$6M	$3M
C	$6M	$5M	$4M

• Strategy A has the highest upside.
• Strategy C has the strongest downside protection.
• Strategy B performs reasonably well across all three futures.

Now ask: Which strategy is most robust? That may be Strategy B.

The Robustness Test

A robust strategy doesn't have to be the absolute best in every scenario. Instead, it performs acceptably across a broad range of plausible futures. This is particularly valuable when:

• uncertainty is high

• the decision is difficult to reverse

• downside risk is significant

• information is limited

Deciphering Cases

When you encounter a case with substantial uncertainty, ask five questions:

• What do we know? Separate facts from assumptions.
• What don't we know? Identify the major uncertainties.
• What could change the decision? Focus on material uncertainties.
• Can we learn more? Look for:

  • research

  • pilots

  • experiments

  • customer testing

  • partnerships

• Can we preserve flexibility? Consider:

  • staging

  • delaying

  • scaling

  • exiting

  • partnering

This turns uncertainty into something you can manage.

Part 8: Decision Making Under Uncertainty in a Case Competition

You rarely have enough time in a case competition to conduct perfect analysis. That is okay. You don't need perfect information. You need a defensible decision process. A practical approach is:

• Step 1: Identify the key uncertainty.
• Step 2: Estimate the range of plausible outcomes.
• Step 3: Determine the financial impact.
• Step 4: Identify the downside risk.
• Step 5: Determine whether more information could change the decision.
• Step 6: Identify a way to reduce the risk.
• Step 7: Build flexibility into the implementation.
• Step 8: Make the decision.

A Case Example

Imagine an airline is considering launching a new international route.

The company estimates: Investment = $5M. But passenger demand is uncertain.

• High Demand: NPV = $6M
• Moderate Demand: NPV = $2M
• Low Demand: NPV = -$3M

The team could recommend: "Launch immediately." A stronger team might ask: "Can we test demand before making the full investment?" Perhaps the airline can:

• begin with limited seasonal service
• use existing aircraft capacity
• partner with another carrier
• test promotional pricing
• measure advance bookings

Now the strategy reduces uncertainty. The recommendation might become: "Proceed with a six-month pilot using existing capacity. Expand to a permanent route only if advance bookings exceed the required threshold." That is a much stronger recommendation.

Turning Uncertainty Into Implementation

Notice what happened. The financial analysis identified:

High uncertainty --> Sensitivity analysis identified: Demand as the key driver --> Decision analysis identified: Information has value --> Strategy identified: Pilot first --> Implementation created: A decision threshold --> Recommendation became: Conditional but actionable

This is sophisticated case solving.

Decision Thresholds

A threshold tells management when to continue, change, or stop. Examples:

• Expand if customer adoption exceeds 10%.
• Continue if gross margin remains above 35%.
• Proceed to Phase 2 if NPV remains positive.
• Exit if monthly losses exceed $500,000.

Thresholds make uncertainty manageable.

They also make implementation measurable.

Discover Your Mad Skills Principle

Don't simply predict what will happen. Define what you will do when it happens.

Part 9:  Communicating Uncertainty to Judges

Teams sometimes avoid discussing uncertainty because they fear it will make the recommendation look weak.

Don't.

Executives understand uncertainty. What they dislike is unmanaged uncertainty. Compare:

• Weak: "There is some risk that adoption could be lower."
• Better: "Customer adoption is our primary risk."
• Strong: "Customer adoption is the primary risk. If adoption falls below 7%, the investment no longer creates value. We therefore recommend a pilot with a 7% adoption threshold before full rollout."

The third answer demonstrates:

• awareness
• analysis
• quantification
• mitigation
• decision discipline

The Executive Uncertainty Statement

A useful communication structure is: "Our key uncertainty is ________. If ________ happens, the impact is ________. Therefore, we will ________."

For example: "Our key uncertainty is customer adoption. If adoption falls below 7%, the investment becomes value-destroying. Therefore, we will launch in two pilot markets first and proceed to full rollout only if adoption exceeds 7%." This is concise and powerful.

Common Mistakes

• Pretending the future is certain. Forecasts are estimates.
• Making up probabilities. Don't create precise probabilities without a defensible basis.
• Treating all uncertainty equally. Focus on uncertainty that could change the decision.
• Overanalysing uncertainty. The goal is not to eliminate uncertainty. The goal is to decide it.
• Ignoring flexibility. A staged decision may be better than a binary yes/no decision.
• Ignoring information value. Sometimes the best next step is to learn.
• Ignoring downside. High upside does not automatically make a good investment.
• Failing to define thresholds. If the strategy depends on certain conditions, specify them.
• Giving an "it depends" recommendation. Executives still need a decision. Your recommendation should be clear even when conditions matter.

Mad Skills Drill

Take a case with a major uncertain decision.

Complete the following:

• Decision: What are we deciding? 
• Key Uncertainty: What don't we know? 
• Key Driver: What assumption matters most? 
• Downside: What happens if we're wrong?
• Information: What could we learn?
• Flexibility: How could we stage the decision?
• Threshold: What condition determines whether we continue?
• Recommendation: What should management do?

Advanced Mad Skills Drill

Now challenge your recommendation. Ask: What would have to be true for our recommendation to be wrong? List three conditions.

• Condition 1
• Condition 2
• Condition 3

Now ask: Can we monitor those conditions? If yes, create a metric. If no, ask whether you can reduce the uncertainty before committing significant resources. This is how you move from forecasting to active risk management.

Building the Full Financial Decision Story

At this point, your financial analysis should connect the entire Part IV toolkit.

ROI: How attractive is the return? --> NPV: How much value does it create? --> IRR: What rate of return does it generate? --> Comparison: How does it compare with alternatives? --> Sensitivity: How robust is the result? --> Scenario Analysis: What happens under different futures? --> Uncertainty: What don't we know? --> Decision Design: How can we reduce risk and preserve flexibility? --> Recommendation: What should management do?

This is the complete financial decision-making process.

The Financial Decision Framework

When facing an investment decision, use:

Calculate: ROI / NPV / IRR --> Compare: Alternatives --> Test: Sensitivity / Scenarios --> Identify: Uncertainty / Risk --> Decide: Invest / Stage / Test / Wait / Don't Invest --> Protect: Thresholds / Mitigation / Flexibility

This framework provides a practical structure for case competition financial decisions.

What Judges Are Really Looking For

Judges are rarely impressed simply because a team knows how to calculate XNPV. They are looking for evidence that the team understands:

• what drives the economics
• what could go wrong
• what matters most
• what the organisation can control
• what the organisation cannot control
• what information is missing
• what decision should be made
• how the decision can be protected

In other words: They are evaluating your judgment. The spreadsheet is evidence of that judgment. It is not the judgment itself.

Coach's Lens

When coaching teams, I often find that students want certainty before making a recommendation. That is understandable. But business leaders rarely get certainty. They get:

• incomplete information
• competing priorities
• imperfect forecasts
• limited resources
• time pressure

The skill is not waiting until uncertainty disappears. The skill is learning how to make a defensible decision because of how you manage uncertainty.

Chapter Summary

The future is uncertain. Financial models cannot eliminate that uncertainty. But they can help us understand it.

• Sensitivity analysis shows us: How much does the outcome change when assumptions change?
• Scenario analysis shows us: What happens under different plausible futures?
• Decision analysis goes one step further: What should we do given what we know—and what we don't know?

The strongest decision makers don't attempt to predict everything.

• They identify what matters.
• They test what they can.
• They learn where possible.
• They preserve flexibility.
• They establish thresholds.

And then they decide.

Key Takeaways

✓ Risk and uncertainty are related but different.

✓ Identify what you know and what you don't know.

✓ Focus on uncertainties that could change the decision.

✓ Don't create false precision with unsupported probabilities.

✓ Use expected value when probabilities are defensible.

✓ Recognise that two investments with the same expected value can have very different risk profiles.

✓ Consider the organisation's risk tolerance.

✓ Use decision trees when decisions occur sequentially.

✓ Recognise the value of information.

✓ Use pilots and experiments to reduce uncertainty.

✓ Consider the value of flexibility.

✓ Distinguish reversible from irreversible decisions.

✓ Stage investments when uncertainty is high.

✓ Build decision thresholds into implementation.

✓ Design strategies that remain effective across plausible futures.

✓ Don't simply predict what might happen—decide what you will do if it happens.

✓ The strongest recommendation is not the one that assumes the future will cooperate. It is the one that is designed to succeed even when the future doesn't.

Part IV Closing

Financial analysis is ultimately not about Excel.

• Excel is a tool.
• ROI is a tool.
• NPV is a tool.
• IRR is a tool.
• Sensitivity analysis is a tool.
• Scenario analysis is a tool.

Their real purpose is to help you answer one question: "What should we do?" A strong case team can take a large amount of financial information and turn it into a clear decision. They can explain:

• What the organisation can afford.
• What creates value.
• What the alternatives are.
• What could go wrong.
• What assumptions matter.
• How robust the recommendation is.
• How the organisation can protect itself.

And ultimately: What management should do next. That is the difference between financial analysis and financial decision-making. You are not trying to prove that your spreadsheet is correct. You are trying to demonstrate that your decision is defensible.

Discover Your Mad Skills

The deeper lesson of Part IV is simple: Numbers should reduce uncertainty—not create the illusion of certainty.

• Use the numbers to understand.
• Use the analysis to compare.
• Use the scenarios to test.
• Use your judgment to decide.

And use your implementation plan to manage what happens next.

Looking Ahead

You now have the financial tools to support a case recommendation. But a great recommendation still has to be communicated. The judges need to understand:

• what you recommend
• why you recommend it
• why it is financially credible
• what risks you considered
• how you will implement it

The next challenge is therefore no longer: "Can we prove that the recommendation works financially?" It is: "Can we make the judges believe this is the right decision?" That takes us from financial decision-making into the next stage of case mastery: From Analysis to Action. Building the recommendation, implementation plan, and business case that turns financial insight into a decision.