🧠Business Cognitive Bias Unit 8 – Decision-Making Models & Strategies

Key Concepts

• Decision-making models provide structured approaches to analyze and solve problems
• Cognitive biases are systematic errors in thinking that influence decision-making
• Rational decision-making involves logical analysis of available information to make optimal choices
• Intuitive decision-making relies on gut feelings, past experiences, and instincts
• Decision-making strategies include satisficing, maximizing, and optimizing
  • Satisficing involves choosing the first acceptable option rather than seeking the best
  • Maximizing aims to find the best possible solution by evaluating all alternatives
  • Optimizing balances the benefits and costs to find the most efficient solution
• Heuristics are mental shortcuts used to simplify complex decisions (rule of thumb)
• Bounded rationality recognizes the limitations of human cognitive abilities in decision-making

Types of Decision-Making Models

• Normative models prescribe how decisions should be made based on rational principles
• Descriptive models explain how people actually make decisions in real-world situations
• Prescriptive models combine normative and descriptive approaches to guide decision-making
• Rational choice model assumes individuals make decisions based on maximizing utility
• Bounded rationality model acknowledges cognitive limitations and the use of heuristics
• Prospect theory explains how people make decisions under risk and uncertainty
  • Emphasizes the importance of framing effects and reference points
• Garbage can model describes decision-making in ambiguous and complex situations

Cognitive Biases in Decision-Making

• Confirmation bias leads individuals to seek information that confirms their existing beliefs
• Anchoring bias occurs when initial information disproportionately influences subsequent judgments
• Availability bias overestimates the likelihood of events that are easily remembered or imagined
• Framing effect shows how presenting information in different ways influences decisions
• Sunk cost fallacy is the tendency to continue investing in a decision due to past investments
• Overconfidence bias leads individuals to overestimate their abilities and knowledge
• Hindsight bias is the tendency to perceive past events as more predictable than they actually were
  • Leads to overestimating the ability to have foreseen outcomes

Rational vs. Intuitive Decision-Making

• Rational decision-making involves systematic analysis of alternatives based on predefined criteria
  • Emphasizes the use of logic, data, and evidence to make optimal choices
  • Suitable for complex decisions with clear objectives and available information
• Intuitive decision-making relies on instincts, gut feelings, and past experiences
  • Useful in situations requiring quick decisions or when information is limited
  • Influenced by emotions, biases, and heuristics
• Effective decision-making often involves a combination of rational and intuitive approaches
• Intuition can provide valuable insights, but should be balanced with rational analysis
• Rational decision-making can be time-consuming and may not always lead to the best outcomes

Decision-Making Strategies

• Elimination by aspects involves progressively eliminating alternatives based on essential criteria
• Lexicographic strategy chooses the alternative that performs best on the most important criterion
• Satisficing selects the first option that meets the minimum acceptable criteria
• Maximizing exhaustively searches for the best possible alternative
• Optimizing balances the benefits and costs to find the most efficient solution
• Decision trees visually represent the structure of a decision problem and its potential outcomes
• Influence diagrams combine decision trees with probabilistic relationships between variables

Tools and Techniques

• SWOT analysis evaluates the strengths, weaknesses, opportunities, and threats of a decision
• Cost-benefit analysis compares the expected costs and benefits of different alternatives
• Decision matrices organize and evaluate alternatives based on weighted criteria
• Sensitivity analysis assesses how changes in key variables affect the outcome of a decision
• Scenario planning explores different possible future scenarios to inform decision-making
• Monte Carlo simulation models the probability of different outcomes in uncertain situations
• Multi-criteria decision analysis (MCDA) systematically evaluates alternatives based on multiple criteria
  • Analytic Hierarchy Process (AHP) is a popular MCDA method that uses pairwise comparisons

Real-World Applications

• Business strategy development and resource allocation decisions
• Investment portfolio management and financial decision-making
• Medical diagnosis and treatment planning
• Public policy formulation and evaluation
• Project management and risk assessment
• Consumer behavior and marketing strategies
• Operations management and supply chain optimization
• Human resource management and employee selection

Challenges and Limitations

• Incomplete or inaccurate information can lead to suboptimal decisions
• Time constraints and pressure may hinder the ability to make well-informed decisions
• Cognitive biases and heuristics can distort decision-making processes
• Complex and dynamic environments make it difficult to predict outcomes accurately
• Conflicting objectives and stakeholder interests can complicate decision-making
• Overreliance on quantitative models may overlook important qualitative factors
• Resistance to change and organizational inertia can impede the implementation of decisions
• Ethical considerations and value judgments may not be adequately captured by decision models