Daniel Kahneman is one of the most studied and accomplished economists of the second half of the twentieth century, based off his groundbreaking integration of economics and psychological research surrounding human decisions in the face of uncertainty. His work with Amos Tversky earned him a Nobel Prize in economics in 2002.
Prior to Kahneman’s research in this field, economists had found a fundamental issue with how humans estimate the probabilities of certain outcomes under uncertainty. In theory, facing uncertainty, some people should underestimate the probability of an outcome, while others should overestimate the probability, leading to the results cancelling out statistically. However, Kahneman’s findings proved this theory incorrect: in practice, most people incorrectly estimate the probability of an outcome. The two economists found two important biases people have when trying to determine the probability of an outcome.
The first bias they found was the incorrect importance people place on small sample sizes, a term they coined “the law of small numbers.” Take the example of drug use in patients. If a drug is 80% effective, people assume that if 5 patients are treated, the drug will work in 4 of those cases. However, this is not true. It is statistically unlikely that if 5 people are treated, the drug will work on 4 people. Kahneman discovered he fell victim to this bias in his early work as a military psychologist. After observing officer candidates in a short period of time, he was expecting those who performed well in the training period to perform at the same level throughout their career. He said later on the law of small numbers, “As I understood clearly only when I taught statistics, the idea that predictions should be less extreme than the information on which they are based is deeply counterintuitive.”
A second bias present in Kahneman’s findings was “availability”: if people are exposed to certain examples, they are more likely to incorrectly estimate the probabilities of those examples. For example, people are more likely to overestimate the probability of shark attack if they know someone who was attacked.
Besides the biases present under uncertainty, Kahneman is also known for his introduction of “prospect theory.” Essentially, they found the circumstances surrounding decisions matter. Take for example the case of a person choosing between two identically performing funds. The advisor of the first firm tells the customer they have 25% returns over the last 5 years. The second advisor tells the same person they have returned well above the market over the past 25 years, but recently have fallen off slightly. Even if they still had 25% returns over the past 5 years, the customer is more likely to put his money in the first fund, based off the circumstances and framing of the decision.
How is Kahneman relevant to crypto? If a person bought into bitcoin when it was at $18,000, then proceeded to lose much of their investment, they are more likely to stay away from crypto, as the small interaction they had cost them their hard-earned money. Likewise, if a person either bought into Bitcoin early, or knows many people who did, the small sample they have relating to crypto would lead them to overestimate the probability of crypto currencies succeeding. Small sample sizes and personal examples drive many people’s decisions under uncertainty, which in the case of crypto, means sentiment drives much of the market value.
As for Kahneman's influence on Ampleforth, you may have noticed a chapter in our Red Book named Thinking Fast & Slow. One of Kahneman's most popular works is a book he wrote with that title.