A decision tree is a graphical representation of possible solutions to a decision based on certain conditions. It is a powerful tool used in decision-making to visualize the outcomes, costs, and consequences of different choices. Decision trees help in breaking down complex decisions into simpler, more manageable parts.

• Nodes: Points in the tree where decisions are made.
  • Decision Nodes: Represented by squares, these nodes indicate a decision to be made.
  • Chance Nodes: Represented by circles, these nodes indicate uncertain outcomes.
  • End Nodes: Represented by triangles, these nodes indicate the final outcomes of the decision paths.
• Branches: Lines connecting nodes, representing the decision paths or possible outcomes.
• Leaves: The end points of the tree, representing the final decision outcomes.

• Clarity: Provides a clear visual representation of decisions and their possible consequences.
• Structure: Helps in organizing thoughts and options systematically.
• Analysis: Facilitates the analysis of different scenarios and their potential impacts.
• Communication: Enhances communication of decision processes to stakeholders.

1. Identify the Decision: Clearly define the decision that needs to be made.
2. List Possible Alternatives: Enumerate all possible options or actions.
3. Determine Outcomes and Probabilities: Identify possible outcomes for each alternative and assign probabilities to these outcomes.
4. Calculate Payoffs: Determine the payoff or value associated with each outcome.
5. Draw the Tree: Start with the decision node, add branches for each alternative, and continue adding nodes and branches until all possible outcomes are represented.
6. Analyze the Tree: Evaluate the tree to identify the best decision based on the expected values.

Let's consider a simple example where a company needs to decide whether to launch a new product.

• Decision: Launch a new product or not.
• Alternatives: Launch or Do Not Launch.
• Outcomes: Success or Failure for the launch option.
• Probabilities:
  • Success: 70%
  • Failure: 30%
• Payoffs:
  • Success: $100,000 profit
  • Failure: $50,000 loss

• Launch:

  • Expected Value = (0.7 * $100,000) + (0.3 * -$50,000)
  • Expected Value = $70,000 - $15,000
  • Expected Value = $55,000

• Do Not Launch:

  • Expected Value = $0

Based on the expected values, the company should choose to Launch the Product as it has a higher expected value ($55,000) compared to not launching ($0).

Scenario: You are deciding whether to invest in a new software tool for your team.

• Decision: Invest in the software tool or not.
• Alternatives: Invest or Do Not Invest.
• Outcomes: Increased productivity or No significant change for the invest option.
• Probabilities:
  • Increased productivity: 60%
  • No significant change: 40%
• Payoffs:
  • Increased productivity: $20,000 benefit
  • No significant change: $5,000 cost

Task: Draw the decision tree and calculate the expected values for each alternative.

1. Draw the Tree:

2. Calculate Expected Values:

• Invest:

  • Expected Value = (0.6 * $20,000) + (0.4 * -$5,000)
  • Expected Value = $12,000 - $2,000
  • Expected Value = $10,000

• Do Not Invest:

  • Expected Value = $0

Conclusion: Based on the expected values, you should choose to Invest in the Software Tool as it has a higher expected value ($10,000) compared to not investing ($0).

• Overlooking Probabilities: Ensure that the probabilities of all outcomes for a decision node sum up to 1.
• Ignoring Costs: Always consider both the benefits and costs associated with each outcome.
• Complex Trees: For complex decisions, break down the decision tree into smaller, manageable parts.

In this section, we have learned about decision trees, their importance, and how to create and analyze them. Decision trees are valuable tools for visualizing and evaluating different decision paths and their potential outcomes. By practicing with real-life scenarios, you can enhance your decision-making skills and make more informed choices.