3.3.3 Decision Trees

• Decision Trees are graphical tools used to map out and evaluate the possible outcomes of a decision
• Decision trees estimate the expected monetary value (EMV) of different options.
• Decision trees aid in risk assessment by factoring in the probabilities of success or failure
• The formula for EMV is: EMV = (Probability of Success * Profit) + (Probability of Failure * Cost)</latex>
• Match the decision tree component with its description:

  Decision nodes ↔️ Points where a decision is made

  Chance nodes ↔️ Outcomes determined by chance

  Outcomes ↔️ Results of decisions or events
• Outcomes in a decision tree are represented by the ends of the branches.
• Probabilities in a decision tree are used to calculate the expected monetary value
• Steps to construct a decision tree

  1️⃣ Identify the decision

  2️⃣ Map the alternatives

  3️⃣ Draw decision nodes

  4️⃣ Add chance nodes

  5️⃣ Specify outcomes

  6️⃣ Calculate EMV

  7️⃣ Choose the best option
• Decision nodes in a decision tree are represented by squares
• Chance nodes in a decision tree are represented by circles.
• Probabilities are assigned to chance nodes in a decision tree.
• The outcomes in a decision tree represent the ends of the branches
• Probabilities in a decision tree are assigned to chance nodes.
• Decision nodes in a decision tree are represented by squares
• Chance nodes in a decision tree indicate points where outcomes are determined by chance.
• A decision node in a decision tree is a point where a decision is made.
• All nodes in a decision tree must have associated probabilities.

  False
• A chance node in a decision tree is represented by a circle
• The steps for constructing a decision tree include identifying decision nodes, chance nodes, outcomes, and probabilities.
• An outcome in a decision tree is the result of a decision or chance event.
• A probability in a decision tree is a numerical value representing the likelihood of an outcome occurring.
• Decision trees are graphical tools used to map out and evaluate the possible outcomes of a decision.
• Decision trees help businesses make choices by considering different paths and associated probabilities.
• Decision nodes in a decision tree are represented by squares.
• Chance nodes in a decision tree are represented by circles.
• The expected monetary value (EMV) in a decision tree is calculated using probabilities and outcomes.
• Steps to construct a decision tree:

  1️⃣ Identify the decision nodes (squares) where a decision is made

  2️⃣ Identify the chance nodes (circles) where outcomes are determined by chance

  3️⃣ Determine the outcomes (ends of branches) representing different results

  4️⃣ Assign probabilities to the chance nodes
• A decision node in a decision tree is represented by a square
• A chance node in a decision tree is represented by a circle
• What is an outcome in a decision tree?

  End of a branch
• What does a numerical value assigned to a chance node represent?

  Probability
• The formula to calculate Expected Monetary Value (EMV) is EMV = (Probability of Success × Profit) + (Probability of Failure × Cost)</latex>
• When interpreting a decision tree, you should start by identifying the key components such as decision nodes and outcomes
• Match the decision tree components with their descriptions:

  Decision Node ↔️ Point where a decision is made

  Chance Node ↔️ Outcomes determined by chance

  Outcome ↔️ Result of a decision or event

  Probability ↔️ Likelihood of an outcome

  EMV ↔️ Expected Monetary Value
• In the example given, which advertising strategy has a higher EMV for a new product launch?

  High Advertising
• One advantage of decision trees is their clarity
• Decision trees can aid in risk assessment by calculating Expected Monetary Value (EMV)
• What is one limitation of decision trees regarding data accuracy?

  Data Dependency
• Decision trees help in evaluating options by calculating the EMV