Business & Management

Decision tree

1 year ago
Last Update on June 4, 2025
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Decision tree....Everyday business decisions in any department involve risks. This can be because the business has limited information on which to base the decisions....
Decision tree

Decision trees are a pivotal tool in the realm of business decision-making, providing a structured and systematic approach to evaluating the consequences of various actions under conditions of uncertainty. This analytical framework aids businesses in navigating the complexities of decision-making by quantifying potential outcomes, incorporating the likelihood of their occurrence, and calculating expected values to guide strategic choices. This detailed exposition aims to elucidate the components and application of decision trees in business, enriched with an industry example, to offer IB Business & Management students comprehensive insights into this critical decision-making tool.

Components of Decision Trees

Decision Points

Overview: Decision points are junctures at which a business must choose between multiple courses of action. These are graphically represented by squares and signify the start of different branches that illustrate potential strategies or actions.

Chance Nodes

Overview: Following a decision point, the tree branches out into chance nodes, depicted by circles. These nodes represent the uncertain outcomes associated with each decision. Each branch from a chance node illustrates a possible result of the decision made at the preceding decision point.

Probability or Chance

Overview: Each outcome branch extending from a chance node is assigned a probability value, indicating the likelihood of that particular outcome occurring. Probabilities are expressed in decimal form, and the sum of probabilities for all possible outcomes of a decision or chance node must equal 1, ensuring that all potential scenarios are accounted for.

Expected Values

Overview: Expected values quantify the financial outcomes associated with each decision under consideration. They are calculated by multiplying the financial value of each outcome by its probability and summing these products for all outcomes associated with a decision. Expected values enable decision-makers to compare the potential financial implications of different actions quantitatively.

Application of Decision Trees in Business Decision-Making

Decision trees are utilized across various business domains, including marketing, finance, operations, and strategic planning, to evaluate decisions involving investments, product launches, market entry, and other scenarios characterized by uncertainty.

Industry Example: Pharmaceutical Company’s Drug Development Decision

Consider a pharmaceutical company faced with the decision of whether to proceed with the development of a new drug. The development process involves significant investment and uncertainty regarding regulatory approval and market acceptance.

Decision Points:

  1. Proceed with development.
  2. Halt development.

Chance Nodes and Probabilities:

  • If the company proceeds, there are two possible outcomes: regulatory approval (probability of 0.6) and regulatory rejection (probability of 0.4).
  • If approved, there are further outcomes based on market acceptance: high market acceptance (probability of 0.7) and low market acceptance (probability of 0.3).

Expected Values Calculation:

  • The expected financial return from proceeding with development and achieving regulatory approval and high market acceptance might be calculated as follows:
    • Investment cost: -$100 million.
    • Revenue in the case of high market acceptance: $300 million.
    • Expected value of proceeding with development: (0.6 probability of approval) * [(0.7 probability of high acceptance * $300 million revenue) + (0.3 probability of low acceptance * $100 million revenue)] – $100 million investment cost.

This simplified example illustrates how decision trees enable businesses to break down complex decisions into manageable components, assess the financial implications of various scenarios, and make informed choices by considering both the probabilities of different outcomes and their associated financial impacts.

Steps to completing a decision tree

  1. Based on the information given to you, draw the layout of the tree diagram.
  2. Fill in the nodes with numbers.
  3. Write the forecasted costs on the first branch.
  4. Write the successful revenue and the unsuccessful revenue on each final branch.
  5. Write the probability of success or not success on each final branch.
  6. Complete the calculations for each strand:
    (probability of success × forecast revenue if successful) +
    (probability of not success × forecast revenue if not successful) − forecast costs.
  7. The strand with the highest expected earnings is the one that yields the highest forecasted returns.
  8. Based on your choice, put a line through the strands not taken.
  9. Draw a key, this is necessary for full marks.
  10. Label your decision tree.

Advantages

Visualization of Decision Paths and Outcomes

One of the foremost benefits of decision trees is their ability to visually map out various courses of action and their potential outcomes. This graphical representation can be instrumental in revealing strategic alternatives that may not have been initially considered by decision-makers.

Advantages:

  • Clarity in Decision-Making: The visual layout of a decision tree simplifies complex decision scenarios, making it easier for management to understand the sequence of decisions and potential outcomes.
  • Identification of New Opportunities: By mapping out different paths, decision trees can highlight alternative strategies or actions that hadn’t been previously contemplated, offering a broader perspective on strategic options.

Numerical and Financial Modeling

Decision trees go beyond mere visual aids by incorporating numerical values into the decision-making process. This quantitative aspect involves assigning probabilities to different outcomes and attaching financial values to each path, leading to a more objective and financially sound decision-making process.

Advantages:

  • Quantitative Analysis: Attaching numerical values to decisions and outcomes allows for a more objective analysis, reducing the subjectivity that often clouds complex decision-making.
  • Profit Maximization: By calculating the expected values of different courses of action, decision trees help ensure that the most financially advantageous path is chosen, aligning decisions with the goal of profit maximization.

Industry Example: ABC Ltd.’s Advertising Campaign Decision

Consider ABC Ltd., a company facing a decision regarding its advertising strategy. The management is deliberating whether to continue with the current advertising campaign, launch a new campaign, or modify the existing one.

  • Decision Points: Continue with the current campaign, launch a new campaign, or modify the existing campaign.
  • Chance Nodes: For each decision point, there are outcomes related to market reception, ranging from high acceptance to low acceptance, with assigned probabilities.
  • Expected Values: Financial returns are estimated for each outcome, allowing ABC Ltd. to quantify the expected profitability of each advertising strategy.

Through constructing a decision tree, ABC Ltd.’s management visualizes the different strategies and their outcomes. This process reveals the option of modifying the current campaign as a potential course of action, previously unconsidered. By attaching probabilities and financial estimates to each outcome, the decision tree enables ABC Ltd. to quantitatively assess the profitability of continuing, launching a new, or modifying the advertising campaign. Ultimately, the decision tree guides ABC Ltd. toward the most profitable choice, demonstrating the decision tree’s utility in combining visual strategy mapping with numerical analysis to enhance business decision-making.

Disadvantages

Reliance on Estimated Figures

Overview: Decision trees often require input data in the form of estimated figures for probabilities, costs, and revenues associated with different outcomes. These estimates, while necessary for quantitative analysis, introduce a level of uncertainty that can affect the accuracy of the decision-making process.

Challenges:

  • Subjectivity in Estimates: The process of estimating probabilities and financial figures can be highly subjective, influenced by the decision-maker’s biases or incomplete information.
  • Risk of Misestimation: Incorrect or overly optimistic/pessimistic estimates can lead to misleading conclusions, potentially guiding businesses toward suboptimal decisions.

Inability to Account for Dynamic Business Environments

Overview: Decision trees, by their nature, provide a static analysis based on the information available at the time of their creation. They may not effectively capture the dynamic changes in the business environment, such as economic fluctuations, market trends, or competitive actions, which can significantly impact the validity of the decision analysis.

Challenges:

  • Lack of Flexibility: Once a decision tree is constructed, it does not automatically adjust to reflect changes in the external environment, such as economic downturns or shifts in consumer behavior.
  • Need for Continuous Revision: To remain relevant, decision trees require frequent updates and revisions, which can be time-consuming and may not be feasible for fast-paced business decisions.

Industry Example: The Impact of COVID-19 on the Hospitality Industry

Consider the case of a global hotel chain planning its expansion strategy using a decision tree analysis. The decision tree might have outlined various expansion options, such as opening new hotels in targeted cities, renovating existing properties, or enhancing digital marketing efforts to attract more guests, with estimated probabilities and financial outcomes attached to each option.

However, the sudden onset of the COVID-19 pandemic and the resulting travel restrictions and economic downturn rendered many of these estimates obsolete:

  • Demand Shift: The decrease in global travel demand drastically affected the hotel chain’s occupancy rates and revenue projections, invalidating the original financial estimates used in the decision tree.
  • Economic Climate Change: The economic crisis required a complete reassessment of the expansion strategy, taking into account the reduced demand, increased operational costs due to health and safety measures, and the uncertain duration of the pandemic’s impact.

This scenario illustrates how decision trees, while useful for initial planning, might fail to account for sudden, significant changes in the economic climate, necessitating the creation of a new decision tree based on revised estimates and assumptions.

Example:

Decision tree
Decision tree
Best option: 2b, buy new machinery with training to produce more of the current products.

Frequently Asked Questions About Decision Trees

What is a Decision Tree?
A Decision Tree is a type of flowchart-like structure where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a value (in the case of regression). It's a graphical representation of possible solutions to a decision based on certain conditions. In machine learning and data mining, it's a predictive model used for both classification and regression tasks.
Who created or developed the concept of Decision Trees?
While tree-like structures for decision making have been used in various fields for a long time, the specific algorithms for constructing decision trees from data for machine learning and data mining were pioneered by researchers like J. Ross Quinlan (with algorithms like ID3 and C4.5) and Breiman, Friedman, Olshen, and Stone (with CART - Classification and Regression Trees) in the 1980s and 1990s. So, it wasn't one single inventor but rather a key development in AI and statistics.
Why are Decision Trees used, especially in Machine Learning/Data Mining?
Decision Trees are popular for several reasons:
  • Interpretability: They are easy to understand and visualize, making the decision-making process transparent.
  • Handles Different Data Types: Can handle both numerical and categorical data.
  • Require Little Data Preprocessing: Don't require feature scaling or normalization.
  • Non-linear Relationships: Can capture non-linear relationships between features and the target variable.
  • Feature Selection: Implicitly perform feature selection, highlighting the most important variables.
They provide a straightforward path from input features to a predicted outcome.
When should you use a Decision Tree?
Decision Trees are suitable for:
  • Problems where you need a clear, rule-based explanation for the prediction.
  • Classification and regression tasks.
  • Decision analysis in various fields (business, healthcare, finance, etc.).
  • As a baseline model due to their simplicity.
  • As the base estimator in ensemble methods like Random Forests or Gradient Boosting.
Why is pruning important for Decision Trees? Why prune?
Decision Trees built without constraints can grow very deep and complex, capturing noise in the training data. This phenomenon is called **overfitting**. Overfitted trees perform well on the training data but poorly on unseen data. Pruning is the process of removing branches from the tree that are not significantly contributing to the prediction or are likely based on noise. This simplifies the tree and improves its ability to generalize to new data, reducing overfitting.
How do Decision Trees compare to other models like Random Forest or SVM?
Decision Trees have strengths (interpretability, ease of use) but also weaknesses, particularly overfitting and instability (small changes in data can lead to different trees).
  • Random Forest: An ensemble method that builds multiple decision trees and combines their predictions. It typically offers much higher accuracy and is less prone to overfitting than a single decision tree, but it loses the interpretability of a single tree.
  • SVM (Support Vector Machines): Powerful for classification, especially with clear margins between classes, but often less interpretable than decision trees and can be sensitive to feature scaling.
  • Naive Bayes: A simple probabilistic classifier, fast and efficient, but makes strong independence assumptions that may not hold in real data.
The "better" model depends on the specific problem, data characteristics, and whether interpretability is a priority over maximum accuracy.
What information can you get from a Decision Tree analysis?
A Decision Tree provides insights into:
  • Predictive Rules: The paths from the root to the leaves represent clear IF-THEN rules for classification or prediction.
  • Feature Importance: The features used higher up in the tree (closer to the root) are generally considered more important for making the prediction.
  • Decision Boundaries: How the model splits the data space based on feature values.
  • Potential Subgroups: Leaf nodes represent segments of the data with similar characteristics and outcomes.
It visually maps out the sequence of decisions based on data attributes.
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