Comparing Two Types of Decision-making: When experts are better -- or not

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This brief presentation explains the difference between naturalistic decision-making and the heuristics and biases perspective; briefly explains how the two are synthesized.

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  • Comparing Two Types of Decision-making: When experts are better -- or not

    1. 1. Conditions for Intuitive Expertise A Failure to Disagree Naturalistic Decision-making v Heuristics & Biases Gary Klein v Daniel Kahneman
    2. 2. Two Approaches• Naturalistic Decision-making (NDM) - Gary Klein • Experts make good “intuitive” decisions • Recognition-Primed Decision-making • Pattern-matching • Non- or pre-conscious • “I can’t explain it, I just knew.”
    3. 3. Two Approaches• Heuristics and Biases (H&B) - Daniel Kahneman • Experts are prone to mistakes • Cognitive short-cuts result in errors • Experts may fail to see what novices see • We don’t know what we don’t know
    4. 4. How The Approaches Agree• Intuitive judgments can arise from deep skill - e.g., 10, 000 hours of double-loop learning (The Talent Code) • Not all professions, situations admit of accurate “intuitive” judgments • When the environment and rules change, experience is less valuable
    5. 5. NDM-friendly• Highly stable environments • Stable relationships between cues and criterion • e.g., Driving a car. The same actions produce the same results in the same car consistently
    6. 6. NDM-friendly• Environments that permit learning • Environmental stability • Availability of feedback • Large data/ experience sets • Engineering, chess, insurance, sport
    7. 7. Intuition-aversive• Unstable, fractionated environments • Weak regularities are often accompanied by • Practitioner overconfidence... • Medicine • College • Stock market admissions • Weather • Courts of law
    8. 8. Naturalistic Decision-Making Systems 1 and 2• System 1 • Experience, intuition present possibilities for an expert to evaluate (firefighter)• System 2 • Effortful mental processing of those possibilities
    9. 9. Biases & Heuristics• Anchoring Bias • Our judgment of numerical values is influenced by irrelevant numbers we might see at random• Attribute substitution • A parent take the age at which a child learned to read to be a predictor of future GPA
    10. 10. Biases & Heuristics• Illusion of confidence • We take others’ confidence to be evidence of their accuracy• Illusion of knowledge • For how many complete tasks can you list the steps for doing? • How does your toilet work?
    11. 11. Algorithms• Algorithms often outperform humans in decision tasks• In meta-analysis, computers beat humans in 50% of studies and the rest showed no significant difference • Statistical approaches can pick out weak or highly complex patterns
    12. 12. Overcoming Overconfidence• The Pre-Mortem • Before embarking on a project • Pretend the project has already failed • Generate the reasons for the failure • Then generate safe-guards
    13. 13. Summing Up• NDM and H&B• Differ in assumptions of the role of expertise• Can be synthesized by System 1 and System 2 working together• Precautions to avoid or overcome biases and heuristics

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