Econ 440: Lecture 3 - Biases and Heuristics

Biases and Heuristics

• Confirmation bias involves favoring information that supports one’s existing beliefs while disregarding contradictory evidence.
• Bayes’ Rule illustrates how prior beliefs (P(B)) and new evidence (P(A|B)) update beliefs (P(B|A)), leading to differing interpretations of evidence between individuals with strong prior beliefs.
• Examples include climate deniers and scientists having differing views on climate change despite seeing the same evidence due to their strong priors.

Kahneman and Tversky (1974)

• Probability assessments impact everyday decisions, such as forecasting inflation or job prospects.
• Questions arise about how individuals assess probabilities and whether these assessments adhere to traditional probability theory.
• Traditional economic research relied on the assumption that people can easily calculate probabilities, as per Bayes' Rule and expected utility theory.
• Kahneman and Tversky emphasized subjective probability assessments and highlighted heuristics over classical probability methods.

Heuristics

• Heuristics are simplified decision-making rules that can be rational or biased, depending on the context.
• Three key heuristics identified by Kahneman and Tversky are representativeness, availability, and anchoring.

Representativeness

• The representativeness heuristic judges probabilities based on how typical or representative an individual is of a category.
• An experiment showed that people ignore base rates (e.g., proportion of engineers vs. lawyers) and instead rely on how representative a description matches a stereotype, leading to base rate neglect.
• The Linda problem illustrated that people erroneously assess the likelihood of a more specific scenario (active feminist bank teller) as more probable than a general one (bank teller), defying probability theory.

Availability Bias

• The availability heuristic assesses event probabilities based on how easily examples come to mind, which can sometimes lead to distorted perceptions.
• An example demonstrated that people misjudge the likelihood of a word selection from a dictionary based on their recollection rather than actual frequency (beginning vs. third letter).
• Evidence from purchasing behaviors showed effects of weather on buying habits, suggesting that current conditions distort memory recalls and decisions (e.g., increased sales of convertible cars in warm weather).

Projection Bias

• Connections between abnormal weather and large purchases (cars and houses) indicate that people’s current states influence future preferences.
• In a study, results indicated that warmer temperatures heightened the sale of convertibles and snowstorms influenced all-wheel-drive vehicle purchases.
• A utility function describes consumer preferences, with the aim to maximize utility from different options.

Overconfidence

• Overconfidence was illustrated through a study where college students inaccurately rated their driving skills and safety, with most rating themselves above average—a statistical improbability.
• Possible causes for overconfidence include a reluctance to reveal weaknesses and differing interpretations of what constitutes driving proficiency.

Explore the key concepts of biases and heuristics as discussed in Econ 440. This lecture dives into the nature of confirmation bias and its implications on our understanding of evidence, particularly in relation to climate change. Gain insights into how these cognitive biases affect decision-making processes.