Heuristics came to public attention when Daniel Kahneman published his best-selling book “Thinking, Fast and Slow”.
In it, Kahneman summarized the findings from his decades-long collaboration with his research partner Amos Tversky and got people interested in heuristics and their applications to decision-making, relationships, business, and more.
What is a Heuristic?
Definition
A heuristic is a mental shortcut that our brains use that allows us to make decisions quickly without having all the relevant information. They can be thought of as rules of thumb that allow us to make a decision that has a high probability of being correct without having to think everything through.
Explanation
When you see a person with their hood up in a dark alley and you decide to subtly walk past a bit faster, your brain has probably used a heuristic to evaluate the situation instead of a full thought-out deliberation process.
Heuristics don’t always lead to an optimal outcome, but they work well in situations where speed matters more than precision. In the alley-way example, your brain is relying on an instinctive negative fear response to judge that the situation might be dangerous. They are also useful in cases where the individual has to make a decision while experiencing a high cognitive load.
Simple heuristics are often developed by professionals who have to function in high-stress, high-uncertainty environments (soldiers, firefighters, health care workers, etc.). Firefighters, for example, may have an instinctive sense for when a burning building might collapse: a mental heuristic that they have developed through lots of experience.
Heuristics appear to be an evolutionary adaptation that simplifies problem-solving and makes it easier for us to navigate the world. After all, our cognition is limited, so it makes sense to use them to reduce the mental effort required to make a decision. And heuristics work… Most of the time at least.
Heuristics and Biases
Our brains are wonderful things, and automatic mental shortcuts are extremely useful and indeed required to allow humans to function – it just isn’t possible for a human to survive in an environment without having ways to simplify complex decision making problems.
However, when heuristics misfire or lead to systematic errors – that isn’t so great. And when heuristics do that, we have another name for them – biases.
For example, confirmation bias is the tendency to search for and interpret information in a way that confirms our preconceptions. Think of the person who thinks their housemate is lazy and doesn’t do their fair share of chores. Confirmation bias leads this person to pay lots of attention and notice all the times their housemate doesn’t do the dishes, but subtly ignore and forget the times when their housemate does clean up.
Confirmation bias is an unfortunate consequence of the way our brains process information – it’s a result of the heuristics our human brains use. But in this case it can lead to us not seeking out objective facts and having a distorted picture of the world
Heuristics vs. Algorithms
We can also draw a distinction between heuristic decision making and algorithmic decision making.
In the original psychological sense, a heuristic is an automatic mental behaviour. But in wider use, the term heuristic has come to mean any rule of thumb for decision making. For example, if you are looking for a specific item in the British Museum, you can use the heuristic of first searching the room with an exposition on a related subject.
Meanwhile, an algorithm is a more rigidly defined process that is guaranteed to produce a certain outcome. For example, if you are looking for a specific item in the British Museum, you can use the algorithm of starting on the ground floor and moving through each room in a clockwise direction, which will eventually lead to finding the item you were looking for.
Choosing between using a heuristic and using a more drawn out decision making process, like an algorithm, involves a trade-off between speed and certainty. Applying a heuristic takes less time, but it may not produce the outcome you want (e.g. the item you thought was in a particular room in the British Museum may not be there). Meanwhile, applying a more rigorous process might take more time, but in return it may be more likely to produce the desired outcome (e.g. going through every item in every room in the British Museum might take ages, but you will eventually find what you were looking for).
Examples of Heuristics
Let’s take a look at some important heuristics:
Availability Heuristic
The availability heuristic is our tendency to rely on information that is easy to recall when making decisions. Conversely, something that is hard to remember doesn’t get fed into the decision-making process as much.
The most famous study of the availability heuristic was conducted by Kahneman and Tversky back in the 1970s.
They asked the subjects:
“If a random word is taken from an English text, is it more likely that the word starts with a K, or that K is the third letter?”
Participants overestimated the number of words that began with the letter K and underestimated the number of words that had the letter K as the third letter.
There are three times more words that have the letter K as the third letter, but it’s much easier to recall the ones that start with the letter K, which makes them seem more common.
Peak-End Heuristic
The peak-end heuristic is our tendency to judge an experience based on how we felt at its peak and its end.
Kahneman, Fredricson, Schreiber, and Redelmeier did a study in 1993, the results of which they published in a paper called “When More Pain Is Preferred to Less: Adding a Better End”.
Here’s what they did:
- In the first trial, they asked the participants to submerge a hand in 14 °C water, then keep it underwater for 60 seconds.
- In the second trial, they asked the participants to submerge a hand in 14 °C water, keep it underwater for 60 seconds, then keep it underwater for an additional 30 seconds during which the water temperature was raised to 15 °C.
The researchers then asked the subjects which trial they would like to repeat. Bizarrely, the subjects were more willing to repeat the second trial, although the total exposure to cold water was 50% longer.
The researchers came to the conclusion that this seemingly puzzling result was due to the fact that the last 30 seconds of the second trial were less uncomfortable than the last 30 seconds of the first trial due to increased water temperature. This led the participants to evaluate the overall experience as more pleasant, by paying too much attention to how they felt at the ‘end’.
Heuristics & biases for why we neglect preventing human extinction
Toby Ord — a Senior Research Fellow in Philosophy at Oxford University — has a new book The Precipice: Existential Risk and the Future of Humanity which identifies two biases that cause us to unduly underestimate the probability of our own extinction:
The first of these is the availability heuristic. This is a tendency for people to estimate the likelihood of events based on their ability to recall examples. This stirs strong feelings about avoiding repeats of recent tragedies (especially those that are vivid or widely reported). But it means we often underweight events which are rare enough that they haven’t occurred in our lifetimes, or which are without precedent. Because of this, we should expect risk-reducing activities to be under-supplied and risk-increasing activities to be over-supplied. This creates a need for international coordination on existential risk.
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We also suffer from a bias known as scope neglect. This is a lack of sensitivity to the scale of a benefit or harm. We have trouble caring ten times more about something when it is ten times as important. For example, we tend to treat nuclear war as an utter disaster, so we fail to distinguish nuclear wars between nations with a handful of nuclear weapons (in which millions would die) from a nuclear confrontation with thousands of nuclear weapons (in which a thousand times as many people would die, and our entire future may be destroyed).
