>***“Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.”*** > \- Daniel Kahneman > **Heuristics are simple, efficient rules which people often use to form rapid judgments and make decisions.** They are mental shortcuts where people focus one aspect of a complex problem while ignoring other more complex aspects. Heuristics of thinking under a certain level of uncertainty will lead to bias in judgment and lead to poor decision making. Learn more here: [Heuristics in judgment and decision-making](Heuristics in judgment and decision-making) > ***"Misconceptions of change are not limited to naïve subjects."*** Heuristics and biases are not only experienced by naïve people. Researchers who are aware of this theory and familiar with these heuristics still fail to notice their own intrinsic biases. Most people don't detect their own biases when they make decisions. > ***"Payoffs for accuracy did not reduce the anchoring effect"*** Anchoring is such a strong bias that it is often used in daily situations such as salary negotiations, buying a car or rent negotiations. How do we arrive at 5.44? "even when the description was uninformative". That's dangerous. Does the society gain overall by having heuristics, despite occasional severe and systematic errors? In other words, is having heuristics better than not having them at all? Forming of this stereotype may be another heuristic in itself, because that seems like an amorphous process. Amos Tversky and Daniel Kahneman were friends collaborators for more than 10 years. They were the first to introduce the notions of cognitive biases and cognitive illusions for common human errors and developed prospect theory. They were very interested in irrational human economic choices and their findings are considered seminal works of behavioral economics. Daniel Kahneman was awarded Nobel Memorial Prize in Economic Sciences in 2002 (Tversky did not receive the prize because it is not awarded posthumously). Learn more here: - [Amos Tversky](https://en.wikipedia.org/wiki/Amos_Tversky) - [Daniel Kahneman](https://en.wikipedia.org/wiki/Daniel_Kahneman)  ### Heuristic 2: Availability When people assess the frequency of an event or the likelihood of a development they use the availability heuristic. People tend to judge the frequency of events based on how easy it is for such events to be brought to mind at the time of inquiry. - **Bias 1 - Biases due to the retrievability of instances:** Consider two groups with equal size, A and B. If A has more instances that are more familiar than B then group A will seem bigger than B. - **Bias 2 - Biases due to the effectiveness of a search set:** When people need to retrieve information to solve a problem they will choose the solution that is the easiest to remember/retrieve rather than doing a more thorough search. - **Bias 3 - Biases of imaginability:** When people need to imagine events in order to judge on their frequency, they will resort to the event that are easier for them to imagine at the time of the inquiry. - **Bias 4 - Illusory correlation:** When the association of two events is strong people are more likely to conclude that they co-occur more frequently. Learn more here: [Availability heuristic](https://en.wikipedia.org/wiki/Availability_heuristic) ### Heuristic 3: Adjustment and Anchoring Heuristic People tend to rely on the first piece of information offered when making decisions, the so-called anchor. People will heavily stick to the anchor when trying to adjust from the initial value to find the final value. - **Bias 1 - Insufficient adjustment.** People have a strong tendency to adjust from the initial value. The adjustment tend to be small and stay close to the initial anchor value. - **Bias 2 - Biases in the evaluation of conjunctive and disjunctive events.** People tend to overestimate the probability in conjunctive problems and underestimated in disjunctive problems. - **Bias 3 - Anchoring in the assessment of subjective probability distributions.** When people form subjective probability distributions they tend to be too tight in relation to the actual probability distributions because of anchoring. Learn more here: [Anchoring](https://en.wikipedia.org/wiki/Anchoring) ### TL;DR This paper discusses three heuristics that are employed in making judgments under uncertainty: - **Representativeness:** which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; - **Availability:** of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and - **Adjustment from an anchor:** which is usually employed in numerical prediction when a relevant value is available. These heuristics are usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases they lead to can improve judgments and decisions in situations of uncertainty. ### Heurisitc 1: Representativeness/Similarity When using the representativeness or similarity heuristic, people make judgments about probability based on how similar something is to a stereotype they are familiar with. This heuristic is used when one needs to judge the probability that an object or event belongs to a specific class or process. One tens to consider the probability to be higher the closer it resembles a given stereotype. - **Bias 1 - Insensitivity to prior probability of outcomes:** People tend ignore prior probabilities and base rate frequencies when they evaluate probability by representativeness (example: likelihood of someone being a farmer vs a libraria). If people are not judging by representativeness they will consider prior probabilities and base rate frequencies. - **Bias 2 - Insensitivity to sample size:** People believe that small samples and big samples hold the same characteristics (ex: boys born is smaller vs. bigger hospitals). Plus people then to overlook sample sizes even when they are emphasized. - **Bias 3 - Misconceptions of chance:** People tend to fall in the "Gambler’s Fallacy". They believe that deviations from the mean will be corrected. Deviations are not corrected they are diluted. - **Bias 4 - Insensitivity to predictability:** People tend to feel overconfident when making predictions even when they are based on poor/insufficient information. People are insensitive to the reliability of the evidence presented and to the accuracy of their prediction. - **Bias 5 - Illusion of validity:** People are often confident in predictions that are quite likely to be off the mark without considering factors that limit their predictability. The illusion persists even when the judge is aware of factors that limit the accuracy of his predictions - **Bias 6 - Misconceptions of regression:** People overlook the effects of regressions to the mean (example: selecting students who scored above and below average). Learn more here: [Representativeness heuristic](https://en.wikipedia.org/wiki/Representativeness_heuristic)