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In-Depth Exam Notes on Heuristics, Biases, and Decision-Making.

Key Concepts and Ideas

Heuristics and Cognitive Biases

  • Definition: Heuristics are mental shortcuts that simplify decision-making processes, often leading to cognitive biases.
  • Anchoring Effect: Initial information (like a fake number) influences subsequent judgments, leading to biased decision-making.
    • Example: When estimating the number of countries, initial biases skew the estimates.

Regression to the Mean

  • Explanation: Extreme values will tend to be closer to the mean upon subsequent measurements due to random variability.
  • Example: In education programs aimed at underperforming students, test scores are likely to revert to the average after intervention, making it hard to determine the effectiveness of the program.

Bounded Rationality

  • Concept: Decisions are made considering environmental and cognitive constraints, leading to 'satisficing' behavior (choosing a solution that is good enough).
  • Example: Time pressure during decision-making can push individuals to rely on quick, gut responses rather than deliberate reasoning.

Types of Decision-Making

  1. Perceptual Decisions: Based on objective criteria, like identifying direction of moving dots (right or left).
  2. Value-Based Decisions: Subjective and based on personal preference (cake or ice cream).
  3. Decisions Under Risk: Choices made when outcomes are uncertain; often blending objective and subjective aspects.

Risk Profiles

  • Types:
    • Risk Averse: Prefers safe options when uncertain.
    • Risk Seeking: Willing to take risks for potential gains.
    • Risk Neutral: Indifferent to risk, valuing certainty and expected gain.
  • Risk Premium: The difference in value that one may require to choose risky versus certain options.

Expected Value vs. Actual Behavior

  • Expected Value: Rational decision-making suggests choosing options maximizing expected value (probability multiplied by potential reward).
  • Framing Effect: People's decisions can change based on how a scenario is presented (as a gain or loss).
    • Example: Program A saves 200 lives vs. a 33% chance of saving 600 lives versus 66% chance of no lives saved.

Prospect Theory

  • Overview: Developed by Daniel Kahneman and Amos Tversky, it describes how actual human decision-making deviates from rational models.
  • Utility Function: People value gains and losses differently; losses loom larger than gains (loss aversion).
    • Example: Losing $1 feels worse than earning $1 feels good.
  • Probability Weighting: People misinterpret probabilities, overvaluing rare events and undervaluing common occurrences.

Dual Process Theory

  • System 1: Fast, automatic, emotional decisions (system prone to biases).
  • System 2: Slow, deliberate, logical decisions (more likely to be rational).
  • Impact of Affect: Emotional states influence the risk decisions made; negative moods may lead to riskier choices.

Prediction Errors

  • Definition: The difference between expected outcomes and the actual results influences emotional states, thereby affecting decision-making.
  • Example: Positive prediction errors improve mood, increasing likelihood of engaging in riskier decisions (like gambling).