Availability and Representativeness

Essential Questions

• What are the availability and representativeness heuristics?
• How do these heuristics lead to base-rate neglect and misestimated risks?
• What strategies correct or harness these shortcuts in policy and business?

Overview

A news story about a plane crash dominates headlines, and suddenly people fear flying more than driving. Investors chase "hot" stocks because recent performance feels representative of future returns. Availability and representativeness are heuristics that help us reason quickly but often mislead.

In this lesson, you will explore experiments that reveal these biases, compute Bayesian updates to show errors, and analyze how companies and policymakers adjust communication to counter heuristic-driven misperceptions.

Availability Heuristic

Availability means judging frequency by ease of recall. Tversky and Kahneman asked participants whether English words more often start with "K" or have "K" as the third letter. Because words starting with "K" are easier to retrieve, most chose that option even though the third-letter occurrence is higher. Risk perception studies show similar patterns: after hurricanes, flood insurance purchases spike but decay as memories fade.

Quantitatively, availability can be modeled by scaling probabilities with a memory weight $m(t)$ that decays over time: $\tilde{p}_t = m(t) p$, where $m(t) = e^{-\lambda t}$. Shortly after a salient event, $m(0) = 1$, so perceived probability equals true probability. As time passes, $m(t)$ shrinks, leading to underestimation of recurring risks.

Representativeness and Base-Rate Neglect

Representativeness leads people to judge probability by similarity to stereotypes. In the famous "Linda problem," participants read a description of Linda as bright and concerned with social justice. Asked whether she is a bank teller or a bank teller active in the feminist movement, many choose the conjunction, violating probability rules. Base rates are ignored.

To see the math, consider a medical test with sensitivity 90% and specificity 90% for a disease affecting 1% of the population. A positive test's true probability of disease is $P(D|+) = \frac{0.9 \times 0.01}{0.9 \times 0.01 + 0.1 \times 0.99} \approx 0.083$. Representativeness causes people to focus on the 90% match and neglect the base rate, vastly overestimating the chance of illness.

Managing Heuristics

Communicators counter availability bias by providing comparative statistics and visualizations. Insurance firms send reminders timed with anniversaries of disasters to keep risk salient. Weather agencies display long-term probability charts to anchor perception.

To mitigate representativeness, educators teach Bayesian reasoning using frequency formats: "Out of 1,000 people, 10 have the disease; of those, 9 test positive. Of the 990 without disease, 99 test positive." This concrete framing improves accuracy. In hiring, structured interviews that score candidates on objective criteria reduce the sway of stereotypes.

Understanding these heuristics helps you predict market overreactions, design better warning systems, and foster critical thinking.

Further Reading

• Availability: A Heuristic for Judging Frequency
• Hertwig and Gigerenzer on Frequency Formats
• Weber on Risk Perception