Decision Making

Introduction to Judgment and Decision-Making

  • Judgment: Involves assessing the likelihood or probability of various events based on incomplete information.

  • Decision-Making: The process of selecting one option from multiple alternatives.

  • Importance of Accuracy: The accuracy of judgments is crucial, as it influences decision outcomes.

  • Consequences Matter: The significance of the decision affects the factors involved in the decision-making process.

  • Distinction from Problem Solving: Unlike problem-solving, which requires generating solutions, decision-making involves choosing from presented options.

Pathways to Judgment

  • Moral Judgment: Evaluations (good vs. bad), norm judgements (permissible vs. forbidden), and blame judgements (who/what to blame).

  • Three Major Pathways to Judgment:

    1. Use of Heuristics: Simple rules of thumb and cognitive biases.

    2. Use of Probability: Statistics and likelihood assessments.

    3. Use of Logic: Applying logical reasoning and evidence-based calculations.

Cognitive Biases & Heuristics

  • Cognitive Biases: Systematic errors in judgment and decision-making due to:

    • Cognitive limitations.

    • Motivational factors.

    • Adaptations to limitations.

  • Decisional Heuristics: Simple mental operations that guide assessments of probabilities and frequencies.

  • Examples of Cognitive Biases:

    • Saliency Bias: Preference for prominent information, regardless of relevance.

    • Confirmation Bias: Tendency to favor information that supports existing beliefs while dismissing contradictory evidence.

      • Requires more cognitive effort to deal with negative information

    • Fundamental Attribution Error: Tendency to blame others’ disposition when things go wrong.

    • Overconfidence Bias: Overestimation of one's abilities and confidence in decisions.

    • Gambler's Fallacy: Misbelief that past events influence future probabilities.

    • Adopting a Psychological Set: Reliance on familiar strategies, resisting new approaches.

Heuristics

  • Definition: Mental shortcuts that simplify decision-making processes.

  • Examples of Heuristics:

    • Take-the-Best Heuristic: Making decisions based on the most valid cue.

      • Recognition Heuristic: Inferring higher value for recognized items.

    • Representativeness Heuristic: Judging based on similarity to a prototype.

    • Availability Heuristic: Assessing probabilities based on how easily instances come to mind.

  • Common Features of Heuristics

    • Heuristics reduce cognitive effort and speed up decision-making.

    • They generally lead to satisfactory decisions but can introduce biases and systematic errors.

    • Individuals are often unaware of their reliance on heuristics.

Bayesian Theorem

  • Definition: The assessment of probability of something happening changing when updated with new information.

  • Prior beliefs should be updated as new data is acquired.

Dual-Process Model

  • Kahneman (2003) probability judgement depends on processing within two systems:

    • System 1: For simple decisions producing fast, intuitive, automatic, immediate, effortless thinking.

    • System 2: For complex decisions that require slower, analytical, rule-based thinking that requires greater cognitive effort and conscious monitoring.

Decision-Making Under Risk

  • Utility Theory: Calculated as the probability of an outcome multiplied by the subjective value attached to that outcome.

  • Decisions are often treated as gambles, with individuals ranking choices based on personal preferences.

  • Expected Utility = (probability of given outcome) x (subjective assessment or value attached to that outcome).

  • Prospect Theory: Explains decision-making behaviors under risk and uncertainty.

  • Key Concepts:

    • Loss Aversion: Individuals weigh pain of losses more heavily than the pleasure of gains.

    • Editing Phase: Simplifying complex decisions into manageable parts.

    • Evaluation Phase: Choosing between edited options based on value and probability weights.

  • Framing Effect: The way a decision is presented can influence risk behavior (risk-averse vs. risk-seeking).

  • Sunk Cost Effect: Tendency for individual to pursue course of action even after proved to be suboptimal.

Complex Decision-Making

  • Elimination by Aspects: A method where options are filtered based on relevant attributes.

  • Detailed Comparisons: Patterns of attributes of the retained options are conducted.

  • Pattern Recognition: Experts use past experiences to categorize and make rapid decisions in familiar situations.

  • Bounded Rationality

    • Herbert Simon: Decision-makers are limited by cognitive constraints and environmental factors.

  • Satisficing Heuristic: Choosing the first satisfactory option rather than seeking the optimal solution.

Artificial Intelligence (AI) & Decision Making

  • Role of AI: AI systems support decision-making through algorithms but can lack transparency.

  • Explainable AI: Importance of understanding AI decisions to ensure accountability.

  • AI's Advantages: Improved efficiency and accuracy in processing large data sets for decision-making.