IB Business Management SL | Business Management Toolkit

BMT 6 Decision Trees | IB Business Management SL

Decision trees are quantitative decision-making tools that help businesses compare options under uncertainty. They combine decision choices, chance outcomes, probabilities and financial payoffs to calculate expected values. For IB Business Management SL, the key skill is not only calculation. Students must also interpret results, evaluate assumptions and explain why the highest expected value may not always be the best strategic decision.

• Decision nodes
• Chance nodes
• Branches
• Probabilities
• Payoffs
• Expected value
• Net payoff
• Risk
• Sensitivity analysis

What Is a Decision Tree?

A decision tree is a visual and quantitative tool used to map out business decisions. It shows the options available to managers, the uncertain outcomes that may follow each option, the probability of each outcome and the financial value of each outcome. By combining probabilities and payoffs, managers can calculate the expected value of each option and compare alternatives more systematically.

Decision trees are useful when decisions involve uncertainty. A business may not know whether demand will be high or low, whether a new product will succeed or fail, whether a market will grow or decline, or whether a research project will produce a successful result. The decision tree does not remove uncertainty, but it makes uncertainty visible and measurable.

In IB Business Management SL, decision trees are often used for product launch decisions, investment decisions, market expansion, capacity choices, research and development decisions and marketing campaign choices. These decisions often involve costs now and uncertain benefits later. Decision trees help compare options using expected value, also called expected monetary value when the outcomes are financial.

A decision tree should not be treated as a perfect answer. It is only as reliable as the probabilities and payoffs used. If the probabilities are guessed, biased or outdated, the expected value may be misleading. If the payoffs ignore qualitative factors, the decision may be financially logical but strategically weak. Strong IB answers calculate accurately and then evaluate the result.

Components of a Decision Tree

Decision Nodes

A decision node shows a point where management must choose between options. It is usually drawn as a square. The branches coming out of a decision node represent the options under management control. For example, a business might choose to launch a new product, delay the launch or reject the project. A decision node is about choice, not chance.

Chance Nodes

A chance node shows uncertain outcomes that are not directly controlled by management. It is usually drawn as a circle. The branches coming out of a chance node represent possible outcomes such as high demand, medium demand and low demand. Each outcome is assigned a probability. Probabilities from the same chance node must add to 1.0, or 100 percent.

Branches

Branches are the lines that connect nodes and show decision paths. From a decision node, branches show alternative choices. From a chance node, branches show possible outcomes. A clear decision tree labels every branch so the reader can follow the logic from left to right.

Probabilities

Probabilities show the likelihood of uncertain outcomes. They may be written as decimals, fractions or percentages. In IB exams, decimals are common. For example, a probability of 0.7 means a 70 percent chance. If a chance node has two outcomes, such as success and failure, and success has probability 0.7, failure must have probability 0.3.

Payoffs

Payoffs are the financial outcomes at the end of the branches. They may be revenue, profit, cost saving, net present value or net payoff after costs. In exam questions, students must read carefully to see whether the payoff already includes costs. If the question gives revenue and investment cost separately, the net payoff may need to be calculated before expected value.

Expected Values

Expected value is the weighted average of possible outcomes. Each outcome is multiplied by its probability, and the results are added. Expected value helps compare options, but it does not guarantee the actual outcome. A project with an expected value of $100,000 may still produce a loss if the unfavorable outcome occurs.

Expected Value Formula

The expected value formula is:

For two outcomes, the formula can be written as:

If there are three outcomes, add the third probability multiplied by the third payoff. If there are four outcomes, add the fourth. The same logic applies to any number of outcomes. The important rule is that all probabilities from a single chance node must add to 1.0.

Expected monetary value, or EMV, is expected value expressed in money. In many IB Business Management questions, EV and EMV are used in similar ways because the outcomes are financial. However, students should still explain units. If the payoffs are in dollars, the expected value is in dollars. If the payoffs are in thousands of dollars, the expected value is in thousands of dollars.

Simple Expected Value Example

A business is considering a new product launch. If the product succeeds, the payoff will be $300,000. The probability of success is 0.6. If the product fails, the payoff will be a loss of $50,000. The probability of failure is 0.4.

The expected value is $160,000. This means that, on average, the decision has a positive expected monetary value when probabilities are considered. It does not mean the business will definitely earn $160,000. The actual result will be either the success payoff or the failure payoff. Expected value is a decision guide, not a prediction of a guaranteed result.

A strong answer would interpret the result. A positive expected value suggests the launch may be financially attractive, but the business still faces a 0.4 probability of losing $50,000. If the business has weak cash flow or cannot afford the loss, it may choose not to launch even though expected value is positive.

Net Payoff vs Gross Payoff

One of the most common exam problems is deciding whether to subtract costs before or after calculating expected value. A gross payoff is a value before deducting an investment cost. A net payoff is the value after deducting the relevant cost. Exam questions may give either. Students must read the wording carefully.

If a question says the outcome is "profit," the cost may already be included. If it says the outcome is "revenue" and separately gives an investment cost, the cost must usually be subtracted. If a launch costs $80,000 and success creates revenue of $300,000, the net payoff is $220,000. If failure creates revenue of $20,000, the net payoff is -$60,000 after subtracting the same $80,000 cost.

Sometimes the cost is subtracted after calculating expected revenue. This gives the same result if the cost applies regardless of the outcome. For example, if expected revenue is $200,000 and the fixed launch cost is $80,000, net expected value is $120,000. If the cost differs by branch, subtract the relevant cost on each branch.

How to Construct a Decision Tree

Decision trees are normally drawn from left to right. The initial decision appears on the left. The possible outcomes appear on the right. Calculations are then completed from right to left. This may feel unusual at first, but it is logical because later outcomes must be valued before earlier choices can be compared.

1. Start with the decision node on the left.
2. Draw one branch for each decision option.
3. Label each decision branch clearly.
4. Add chance nodes where outcomes are uncertain.
5. Draw outcome branches from each chance node.
6. Add probabilities to outcome branches.
7. Add payoffs at the end of final branches.
8. Check that probabilities from each chance node add to 1.0.
9. Calculate expected values at chance nodes.
10. Work backwards and choose the option with the highest expected value, unless evaluation suggests otherwise.

A clear decision tree is not only about correct calculations. It should be easy to read. Use consistent labels, show money units, show probabilities and show the final decision. If an option has a cost, show where the cost is included or subtracted.

Working Backwards

Working backwards means calculating values from the right side of the tree back toward the initial decision node. Start with the final payoffs. At each chance node, multiply payoffs by probabilities and add them. At each decision node, compare the expected values of the options and choose the best financial option.

This process is sometimes called rolling back the tree. It is important because a decision tree may have several stages. A business may first decide whether to conduct market research, then decide whether to launch a product, then face high or low demand outcomes. The later expected values must be calculated first so earlier decisions can be evaluated.

In exams, show enough working for each chance node. A final answer without working may lose marks if the examiner cannot see how it was calculated. Write formulas clearly, substitute values and include the final expected value with units.

Comprehensive Example: Product Launch Decision

A business is deciding whether to launch a new product or do nothing. Launching costs $100,000. If demand is high, revenue will be $500,000. The probability of high demand is 0.7. If demand is low, revenue will be $150,000. The probability of low demand is 0.3. Doing nothing has a payoff of $0.

First calculate net payoffs. High demand net payoff is $500,000 - $100,000 = $400,000. Low demand net payoff is $150,000 - $100,000 = $50,000.

The expected value of launching is $295,000, which is higher than doing nothing. The financial decision suggested by the decision tree is to launch. However, the business should still evaluate whether the probabilities are reliable and whether the product fits the business's objectives, brand and operations.

This example also shows why net payoff matters. If the $100,000 launch cost were ignored, the expected value would be overstated. The cost is paid whether demand is high or low, so it must be included in the financial decision.

Complex Example: Market Research Option

A business is considering entering a new market. It can enter immediately, conduct market research first, or reject the expansion. Entering immediately costs $200,000. If the market is successful, net payoff will be $600,000. If it fails, net payoff will be -$120,000. The probability of success without research is 0.55 and failure is 0.45.

Market research costs $40,000. If research is positive, the business can enter with a higher estimated probability of success. If research is negative, the business can avoid entry. Suppose research has a 0.6 probability of positive results and a 0.4 probability of negative results. If positive and the business enters, the expected net value after entry is $420,000 before subtracting the research cost. If negative and the business avoids entry, the payoff is $0 before subtracting the research cost.

First calculate immediate entry:

Next calculate the research option. Expected value before research cost is:

Doing nothing gives $0. Based only on expected value, immediate entry is best at $276,000. Research gives $212,000 after research cost. However, research may reduce the risk of a large loss by allowing the business to avoid entry after negative results. A risk-averse business may prefer research if it cannot afford failure, even though expected value is lower.

Interpreting Decision Tree Results

Interpreting a decision tree means explaining what the expected value suggests and what it does not show. A higher expected value usually suggests the financially preferable option. However, expected value is an average based on probabilities. It does not show certainty, timing, cash flow pressure, strategic fit or stakeholder reaction.

A decision with a high expected value may still involve a large possible loss. If a business has limited cash reserves, it may reject a high expected value project because one bad outcome could threaten survival. A larger business with strong finance may accept the same risk. This means the best decision depends on risk tolerance and financial position.

Decision trees also require careful interpretation of probabilities. A probability of 0.8 does not guarantee success. It means success is estimated to be more likely than failure. If the probability is based on weak research, the expected value may not be reliable. Strong IB answers mention the quality of the probability estimates.

Risk Attitude and Decision Trees

Decision trees assume rational financial comparison, but businesses differ in risk attitude. A risk-averse business prefers safer outcomes and may avoid options with large possible losses. A risk-neutral business focuses mainly on expected value. A risk-seeking business may accept high uncertainty for a chance of very high returns.

Risk attitude depends on context. A start-up with limited cash may be risk-averse because one failed project could close the business. A large multinational may accept more risk because it can absorb losses. A business in a declining market may take risks because doing nothing is also dangerous. A social enterprise may avoid financially attractive options that conflict with its mission.

In IB evaluation, risk attitude is a useful point. The option with the highest expected value is not always the most suitable if the downside risk is too large, the business lacks finance, or the decision conflicts with objectives. This is one reason decision trees should support, not replace, managerial judgement.

Sensitivity Analysis

Sensitivity analysis asks how the decision would change if key assumptions changed. In a decision tree, the most important assumptions are usually probabilities and payoffs. If a small change in probability changes the recommended decision, the decision is sensitive and risky. If the recommendation stays the same across reasonable changes, the decision is more robust.

For example, a product launch may have the highest expected value only if the probability of high demand is above 0.6. If market research is uncertain and the true probability may be 0.45, the decision becomes less secure. Managers may conduct more research, reduce investment cost, launch on a smaller scale or delay the decision.

Sensitivity analysis improves decision trees because it tests reliability. It also helps managers identify which assumptions matter most. If expected value is highly sensitive to price, the business should research customer willingness to pay. If it is sensitive to demand, the business should test demand before full launch.

Qualitative Factors in Decision Trees

Decision trees focus on quantitative financial outcomes, but many business decisions also involve qualitative factors. These include brand image, customer loyalty, employee morale, ethical concerns, environmental impact, strategic fit, competitor reaction, management experience and long-term objectives.

For example, a decision tree may show that outsourcing production has the highest expected value because costs are lower. However, outsourcing may reduce quality control, damage employee morale or create ethical concerns about supplier labor conditions. A business may choose a lower expected value option if it better protects reputation and stakeholder relationships.

Qualitative factors are especially important in IB evaluation questions. A calculation answer may identify the financial option, but the evaluation should explain whether the financial result is enough to justify the decision. The best answers combine calculation with judgement.

Decision Trees and Other IB Business Tools

Decision trees work well with market research. Market research can improve probability estimates and payoffs. Without research, probabilities may be guesses. Primary research, secondary research, test marketing and pilot schemes can all improve the reliability of a decision tree.

Decision trees also connect to investment appraisal. A decision tree can compare uncertain outcomes, while investment appraisal can assess cash flows over time. If a project has long-term cash flows, managers may also need payback, average rate of return or net present value analysis.

Decision trees connect to SWOT and STEEPLE analysis. SWOT can identify strengths, weaknesses, opportunities and threats that affect the probabilities of success. STEEPLE can identify external risks such as economic downturn, legal change or technological disruption. These tools provide context for the numbers in the decision tree.

Decision trees connect to business plans. A business plan may include decisions about product launch, expansion, finance or marketing. Decision trees can support these decisions by showing expected values and risks. They can also support contingency planning by showing possible outcomes.

Advantages of Decision Trees

The first advantage is visual clarity. Decision trees show choices, outcomes and probabilities in a structured diagram. This helps managers understand complex decisions more easily than a long paragraph of data.

The second advantage is that decision trees include uncertainty. Many business tools assume one forecast, but decision trees can show multiple possible outcomes. This encourages managers to think about risk rather than only expected success.

The third advantage is quantitative comparison. Expected value allows different options to be compared using a common financial measure. This can reduce emotional or biased decision making.

The fourth advantage is that decision trees support discussion. Managers can challenge the probabilities, payoffs and assumptions. This can improve planning and reveal where more research is needed.

The fifth advantage is that decision trees can be updated. If new information changes probabilities or payoffs, the expected value can be recalculated. This makes the tool useful during planning and review.

Limitations of Decision Trees

The first limitation is that probabilities are estimates. If probability estimates are inaccurate, the expected value will also be inaccurate. In new markets or innovative products, probabilities may be especially difficult to estimate.

The second limitation is that payoffs are estimates. Future revenue, costs, profit and losses may differ from forecasts. Inflation, competitor response, supply problems and customer behavior may change the actual outcome.

The third limitation is that decision trees may oversimplify decisions. Real business decisions may involve many outcomes, changing probabilities, competitor reactions and repeated decisions. A simple tree may not capture this complexity.

The fourth limitation is that expected value ignores risk attitude. A project with high expected value may include a possible loss the business cannot afford. Expected value alone does not show the worst-case scenario's impact on survival.

The fifth limitation is that decision trees can ignore qualitative factors. Brand image, ethics, employee morale and stakeholder relationships may be important even if they are hard to quantify.

Quality of Probability Estimates

The reliability of a decision tree depends heavily on the quality of its probability estimates. A probability should ideally be based on evidence, such as market research, past sales data, test marketing, industry statistics, expert judgement or historical experience. If a probability is guessed, the expected value may look precise but be weak in reality.

For example, a business may estimate that a new product has a 0.7 probability of high demand. If this probability comes from a large customer survey, competitor analysis and trial sales, it may be reasonably reliable. If it comes from the owner's optimism, it is much less reliable. The calculation may be mathematically correct but strategically misleading.

Probability estimates can also be biased. Entrepreneurs may overestimate the chance of success because they are emotionally attached to an idea. Managers may underestimate risk because they want approval for a project. Marketing teams may overstate demand to secure budgets. External consultants may provide more balanced estimates, but they may still rely on incomplete information.

In IB evaluation, probability quality is one of the strongest points to discuss. A decision tree is useful because it forces managers to assign probabilities, but those probabilities must be questioned. A good answer might say that the recommendation is only reliable if the probability of high demand is based on valid market research. If not, the business should conduct further research or use sensitivity analysis before committing resources.

Quality of Payoff Estimates

Payoffs are also estimates. They may depend on future sales volume, price, costs, exchange rates, competitor response, production efficiency and customer behavior. If payoffs are inaccurate, the expected value will be inaccurate. A business may calculate a high expected value because it overestimates revenue or underestimates costs.

Payoff estimates should be linked to other business tools. Sales forecasts can support revenue estimates. Break-even analysis can test how much must be sold to cover costs. Cash flow forecasts can show whether the business can survive before revenue arrives. Investment appraisal can assess projects with long-term cash flows. Market research can test customer willingness to pay.

It is also important to identify whether payoffs are short-term or long-term. A product launch may lose money in year one but become profitable in later years. A decision tree using only first-year profit may underestimate the value of the project. Alternatively, a project may look profitable in year one but damage brand reputation or create future costs. Payoff estimates should match the decision's time horizon.

In exam answers, students can evaluate payoffs by asking what has been included and excluded. Have marketing costs been included? Has the investment cost been deducted? Has possible price competition been considered? Have training, legal compliance or after-sales service costs been included? This kind of evaluation shows business judgement beyond calculation.

Decision Trees and Cash Flow

Decision trees often focus on expected profit or payoff, but cash flow matters. A project may have a positive expected value but require a large cash outflow at the start. If the business lacks working capital or access to finance, it may not be able to choose the option even if the expected value is attractive.

For example, a manufacturer may have a decision tree showing that expanding capacity has the highest expected value. However, the expansion may require immediate spending on machinery, staff training and premises. If cash inflows arrive months later, the business may face liquidity problems. A high expected value does not remove the need for cash flow planning.

Decision trees also do not automatically show timing. A payoff of $500,000 next month is not the same as $500,000 in three years. For longer-term projects, investment appraisal methods such as net present value may be needed. This is why decision trees should be used with finance tools rather than in isolation.

Multi-Stage Decision Trees

Some decision trees involve more than one stage. A business may first decide whether to conduct research, then decide whether to launch, then face demand outcomes. A pharmaceutical company may decide whether to fund research, then whether to continue after trial results, then whether to launch. A retailer may decide whether to open one test store before expanding nationally.

Multi-stage decision trees are useful because they show flexibility. A business does not always have to make one final decision immediately. It can gather information, test demand, reduce uncertainty and then make another decision. This is often more realistic than choosing between full launch and no launch.

The key skill is still working backwards. Calculate the expected values at the final chance nodes first. Then compare later decision options. Then use those values to calculate earlier options. This is why complex decision trees can seem difficult: each later calculation feeds into earlier choices.

In IB exams, multi-stage trees may not be extremely complex, but students should understand the principle. If the business has an option to gather information before committing, the value of that information should be compared with its cost. Research may lower expected value if it is expensive, but it may still be useful if it reduces the risk of a large loss.

Expected Value vs Worst-Case Outcome

Expected value averages outcomes, but managers also need to consider the worst-case outcome. A decision may have a high expected value because the upside is very large, but it may also include a serious possible loss. If the worst-case outcome would threaten business survival, the option may be unsuitable.

For example, suppose Option A has an expected value of $200,000 but a possible loss of $500,000. Option B has an expected value of $150,000 and no possible loss. A large, financially secure business may choose Option A. A small business with limited cash may choose Option B because it cannot survive the possible loss. This is a rational decision even though Option B has a lower expected value.

This distinction is important in IB evaluation. Students should not write that a business "must" choose the highest expected value. It is better to say the highest expected value option is financially attractive, but final choice depends on risk tolerance, cash reserves, objectives and qualitative factors.

Stakeholder Impact of Decision Tree Choices

Decision tree outcomes affect stakeholders. Owners may focus on expected profit and risk. Managers may focus on implementation feasibility. Employees may be affected by expansion, closure, outsourcing or new technology. Customers may be affected by product availability, price, quality and service. Suppliers may gain or lose contracts depending on the decision.

A decision tree may show that closing a product line has the highest expected value because it avoids losses. However, employees may lose jobs, customers may be disappointed and the brand may be damaged. A decision tree may show that outsourcing has a higher expected value, but local communities and employees may be harmed. These stakeholder impacts may not appear in the financial payoffs.

Stakeholder analysis improves decision tree evaluation. A business may choose a lower expected value option because it protects long-term reputation, employee morale or customer loyalty. Alternatively, it may choose the higher expected value option but add measures to reduce stakeholder harm, such as retraining employees or communicating clearly with customers.

Decision Trees in Different Business Functions

In marketing, decision trees can compare product launches, advertising campaigns, pricing strategies and market entry options. A marketing manager may compare a large national campaign with a smaller targeted campaign. The uncertain outcomes may be high response, moderate response or low response. Payoffs may include additional contribution or profit.

In operations, decision trees can compare capacity expansion, supplier choices, production methods and location decisions. A manufacturer may compare buying new machinery with outsourcing production. The uncertain outcomes may be strong demand, weak demand, supplier failure or cost increases. Operations decisions often involve large fixed costs, so downside risk matters.

In finance, decision trees can support investment decisions and risk analysis. A finance manager may compare projects with different expected returns and possible losses. The tool can support capital budgeting, but it should be combined with cash flow forecasts and investment appraisal.

In human resource management, decision trees can compare training options, recruitment strategies or restructuring decisions. Payoffs may be harder to quantify because motivation, productivity and culture are partly qualitative. This shows a limitation: not every important business outcome is easy to convert into money.

Mini Case: Technology Investment Decision

A retail business is deciding whether to invest $250,000 in a new online ordering system. If customer adoption is high, the system is expected to generate net benefits of $600,000 after costs. If adoption is moderate, net benefits will be $180,000 after costs. If adoption is low, the business will lose $100,000 after costs. The estimated probabilities are 0.35 for high adoption, 0.45 for moderate adoption and 0.20 for low adoption.

The expected value is positive, so the investment appears financially attractive. However, the business should evaluate whether the probabilities are reliable. If customers are older and less comfortable with digital ordering, the low adoption probability may be higher. The business should also consider training staff, cybersecurity, maintenance costs and customer support.

A balanced recommendation might be to pilot the system in a few stores before full rollout. This reduces risk and provides better data. If the pilot shows strong adoption, the business can expand. If adoption is weak, the business can revise the system or stop before spending the full amount. This shows how decision trees can support staged decision making.

Mini Case: Ethical Supplier Decision

A clothing business is choosing between a low-cost supplier and an ethical certified supplier. The low-cost supplier has a higher expected monetary value because unit costs are lower. However, there is a probability of negative publicity if poor working conditions are exposed. The ethical supplier has lower expected financial value in the short term because costs are higher, but it may improve brand reputation and customer loyalty.

A decision tree can include estimated costs of negative publicity, lost sales and supplier switching. However, not all ethical issues are easy to quantify. Employee values, customer trust and long-term brand identity may be difficult to put into a single payoff. The business may choose the ethical supplier even if expected value is lower because it fits its values and reduces reputational risk.

This example is useful for IB evaluation because it shows that decision trees are not purely mechanical. Quantitative tools are helpful, but business decisions also involve ethics, stakeholders and long-term strategy.

Common Student Mistakes

The first mistake is forgetting that probabilities from a chance node must add to 1.0. If probabilities do not add to 1.0, the expected value calculation is invalid.

The second mistake is using gross payoffs instead of net payoffs. If the question gives investment costs separately, students must subtract costs when required.

The third mistake is choosing the highest single payoff rather than the highest expected value. A high reward with a low probability may have a lower expected value than a smaller but more likely reward.

The fourth mistake is calculating from left to right. Decision trees should be rolled back from right to left after the tree has been drawn.

The fifth mistake is ignoring evaluation. In longer IB answers, calculation alone is not enough. Students should discuss probability reliability, risk, qualitative factors and business context.

IB Exam Technique for Decision Trees

For definition questions, define a decision tree as a visual quantitative tool that maps decision options, uncertain outcomes, probabilities and payoffs to calculate expected values. Mention that it supports decision making under uncertainty.

For calculation questions, draw or interpret the tree carefully. Label decision nodes and chance nodes. Check probabilities. Calculate net payoffs if needed. Multiply each payoff by its probability. Add the results. Subtract relevant costs if they are not already included. Compare expected values and state the decision.

For analysis questions, explain what the expected values mean for the business. Do not simply state the number. Link the result to risk, investment, finance, objectives and the case context. If one option has a higher expected value but also a large possible loss, explain that trade-off.

For evaluation questions, discuss advantages and limitations. Decision trees are useful because they are visual, structured and quantitative. They are limited because probabilities and payoffs are estimates, qualitative factors may be ignored and risk attitude is not automatically included. Finish with a balanced judgement.

Practice Exercise: Restaurant Expansion

A restaurant is deciding whether to expand into a larger premises. Expansion costs $120,000. If demand is high, annual profit after expansion will be $320,000. If demand is low, annual profit after expansion will be $80,000. The probability of high demand is 0.55 and low demand is 0.45. If the restaurant does not expand, expected profit is $110,000.

First calculate net payoffs for expansion. High demand net payoff is $320,000 - $120,000 = $200,000. Low demand net payoff is $80,000 - $120,000 = -$40,000.

The decision tree suggests not expanding because $110,000 is higher than $92,000. However, a manager may still investigate whether the expansion could become more attractive by reducing rent, improving promotion, increasing capacity utilization or testing demand with pop-up events. This shows how decision trees can guide further analysis rather than end discussion.

Decision Tree Checklist

• Have all decision options been identified?
• Are decision nodes shown as management choices?
• Are chance nodes shown as uncertain outcomes?
• Do probabilities from each chance node add to 1.0?
• Are payoffs clearly labelled with units?
• Have investment costs been subtracted correctly?
• Has expected value been calculated at each chance node?
• Has the tree been worked backwards from right to left?
• Has the option with the highest expected value been identified?
• Have risk, assumptions and qualitative factors been evaluated?

Revision Checklist

• Can you define a decision tree accurately?
• Can you explain decision nodes, chance nodes, branches, probabilities and payoffs?
• Can you calculate expected value using probability x payoff?
• Can you calculate net payoffs when costs are given separately?
• Can you work backwards through a decision tree?
• Can you interpret expected value in business context?
• Can you explain why the highest expected value may not always be chosen?
• Can you evaluate the reliability of probabilities and payoffs?
• Can you discuss risk attitude and qualitative factors?
• Can you connect decision trees to market research, SWOT, STEEPLE and business plans?

Frequently Asked Questions

What is a decision tree?

A decision tree is a visual quantitative tool that maps decision options, uncertain outcomes, probabilities and financial payoffs to calculate expected values.

What is expected value?

Expected value is the weighted average of possible outcomes. It is calculated by multiplying each payoff by its probability and adding the results.

What is a decision node?

A decision node shows a choice controlled by management, such as launching a product or rejecting a project. It is usually shown as a square.

What is a chance node?

A chance node shows uncertain outcomes with probabilities, such as high demand or low demand. It is usually shown as a circle.

Why do probabilities need to add to 1.0?

Probabilities from a single chance node must represent all possible outcomes from that node, so they must total 1.0 or 100 percent.

Why might a business reject the option with the highest expected value?

A business may reject it if the downside risk is too large, probabilities are unreliable, qualitative factors are negative or the option conflicts with objectives and stakeholders.

What is the main limitation of decision trees?

The main limitation is that decision trees depend on estimated probabilities and payoffs and may ignore qualitative factors, risk attitudes and strategic issues.

Final Summary