Judgment under Uncertainty: Heuristics and Biases

Judgment under Uncertainty: Heuristics and Biases

Authors: Amos Tversky and Daniel Kahneman
Published: Sep 27, 1974, in Science
Key Concepts:

• Judgment under uncertainty: Many decisions depend on beliefs about uncertain events, like elections or financial outcomes.
• Heuristics: Simple, efficient rules people use to form judgments, which can lead to biases.
• Judgment of probability: Assessment can resemble judgments of physical quantities and relies on data with limited validity.

Heuristics and Their Biases

1. Representativeness

• Assessing probability based on how similar something is to a stereotype.
• Example: Determining whether Steve is a librarian based on his traits being similar to the librarian stereotype rather than using base-rate statistics.
• Biases:
  • Neglect of base rates: People often ignore probability distributions when focusing on representativeness.
  • Example: When judging if an individual is an engineer or lawyer, people only focus on the individual’s characteristics without considering how many engineers vs. lawyers exist.
  • Insensitivity to sample size: Predictions based on small samples (like 10 men) do not consider the size’s impact on probability.

2. Availability

• Judging frequency or probability based on how easily examples come to mind.
• Biases:
  • Retrievability of instances: Classes of events whose instances are easily recalled seem more numerous.
  • Salience: More memorable events (like a recent news story) inflate perceived frequency.
  • Imaginability: Easier to imagine scenarios affect how likely they are considered.

3. Adjustment and Anchoring

• Making estimates starting from an initial value (anchor) that is adjusted to arrive at a final estimate.
• Examples and Biases:
  • Insufficient adjustment: Estimates can remain too close to the initial anchor due to limited adjustments.
  • Example: Estimating the number of African countries in the UN based on a random number generated nearby.
  • Evaluating conjunctive and disjunctive events: Probability of combinations is often misjudged based on basic probabilities of the events rather than their actual combined probabilities.

Implications and Conclusion

• Understanding these heuristics can enable better decision-making under uncertainty.
• Heuristics are beneficial and lead to efficient judgments, but recognizing resulting biases is crucial for improving prediction and assessment skills in complex situations.
• Awareness of cognitive biases is essential not just for laypeople but also for experienced professionals in various fields.

Key Takeaways

• Decisions are often made based on simplified mental models (heuristics).
• Common biases include neglect of base rates, overconfidence, and using irrelevant anchors, which can skew judgments.
• A systematic approach to identify and correct biases can improve judgment reliability in uncertain conditions.

References:

1. Kahneman, Daniel, & Tversky, Amos. Psychology of Judgment and Decision Making.
2. Edwards, William. Formal Representation of Human Judgment.
3. Slovic, Paul & Lichtenstein, Sarah. Decision Analysis: A Behavioral Approach.