9.1 Heuristics

Humans make many decisions on a daily basis, often in the absence of complete information or under constraints of limited time and cognitive capacity. We present the mechanism and usefulness of cognitive shortcuts such as availability heuristic and representativeness heuristic, as well as the often unnoticed pitfalls that arise from using them for judgments and decision making.

The Lesson in Context

Humans make many decisions on a daily basis, often in the absence of complete information or under constraints of limited time and cognitive capacity. We use heuristics as useful shortcuts for quick decision making, which may introduce bias into our conclusions. The purpose of the lesson is not to cast doubt on our use of heuristics, but to recognize the limitations of quick human judgments and their consequences. This parallels 2.1 Senses and Instrumentation and 2.2 Systematic and Statistical Uncertainty, where the limitations of instruments are discussed and quantified, without rejecting the validity and usefulness of instruments altogether.

Earlier Lessons

2.1 Senses and Instrumentation

• Senses and instrumentation are inherently imperfect, but imperfect tools can still be useful in obtaining partial knowledge. Similarly, heuristics are flawed, but they can make useful tools when time, knowledge, and mental resources are limited.

2.2 Systematic and Statistical Uncertainty

• The use of heuristics can often introduce bias into our judgments — tendencies to make one decision more often than another, paralleling the idea of systematic uncertainty in instrumental measurements.

---

Later Lessons

9.2 Biases

• This lesson focuses on heuristics that affect our judgments of frequencies — how often things occur or likelihoods of events. The next lesson discusses biases in decision making that stem from a self-centered view of the world—an overemphasis on "me" and "now" and overusing defaults and assumptions.

10.1 Confirmation Bias

• We single out confirmation bias into its own topic, as it permeates scientific and group decision making, affecting both our sense of the prevalence of events around us as well as the importance of "me" and "now".

Takeaways

Learning GoalsDefinitionsExamplesCommon MisconceptionsExpanded Learning Goals

After this lesson, students should

1. Know that we use heuristics as a shortcut in everyday decision making.
2. Recognize that while heuristics can be useful and necessary, they can also lead us astray by introducing biases into our decision making.
3. Be able to identify cases of Base Rate Neglect.
4. Be able to avoid the temptation to engage in Base Rate Neglect.
5. Be able to identify cases of the Representativeness Heuristic, and not fall for it.
6. Be able to identify cases of the Conjunction Fallacy, and not fall for it.
7. Be able to identify cases of the Availability Heuristic, and not fall for it.
8. Learn the basics of Bayesian reasoning.

Heuristics

Mental shortcuts people make instead of analyzing all relevant information while making decisions.

Base Rates

The base frequency of a given attribute in a whole population.

• Base Rate Neglect

The act of overlooking the importance of base rates when calculating the probability of an event based on probabilities that seem more relevant to the specific case.

• Bayes' Rule

[math]\displaystyle{ \begin{align} &\ \ \quad\text{(probability that a positive is a true positive)} \\ &= \frac{(\#\text{ true positives})}{(\text{total } \# \text{ positives})} \\ &= \frac{(\#\text{ true positives})}{(\#\text{ true positives})+(\#\text{ false positives})} \\ &= \frac{\text{(base rate)}\times\text{(true positive rate)}}{\text{(base rate)}\times\text{(true positive rate)}+(1-\text{base rate})\times\text{(false positive rate)}} \end{align} }[/math]

Representativeness Heuristic

Cases in which how representative something is of a category or outcome is used as a proxy/heuristic for evaluating how likely the category membership or outcome is (not taking base rates into account).

Conjunction Fallacy

The tendency to neglect that something is less likely to be part of a subset of a set than a set itself. In reality, [math]\displaystyle{ A }[/math] is always more likely to be true than [math]\displaystyle{ A }[/math] and [math]\displaystyle{ B }[/math] because if [math]\displaystyle{ A }[/math] and [math]\displaystyle{ B }[/math] is true then [math]\displaystyle{ A }[/math] must be true. This usually happens as a consequence of the representativeness heuristic, when [math]\displaystyle{ B }[/math] appears more representative of the set in question.

Availability Heuristic

Cases in which people use how readily something comes to mind as a proxy for an estimate of its probability. This can be influenced by factors unrelated to probability, such as vividness or frequency of appearance in the media.

Bounded Rationality

The idea that people have limited information and capacity to analyze all factors when making decisions (i.e. "rationality" is "bounded"). Given bounded rationality, it's often reasonable (that is, better than if we decided randomly) to use heuristics to guide our decisions.

Availability Heuristic: Plane Crashes

Ever since reading about plane crashes in the news, Harriet insists on driving even long distances. She is falling into the availability heuristic. She is actually much more likely to die in a traffic accident than a plane accident. Traffic accidents are extremely common but rarely make the news, whereas plane accidents are rare but always make the news, making them more "available" to her memory and thus seem more common than they are.

Representativeness Heuristic: Pit Bulls

Having been assured that the rescue dog was extremely friendly, Milo was shocked to discover it was a pit bull. He knew that pit bulls are often less friendly than other dogs, and did not know that pit bulls are over-represented among rescue dogs (in part because of this reputation).

Conjunction Fallacy: Tabbies

Even though tabbies are the commonest brown cat, the fact that all brown tabbies are brown cats but not all brown cats are tabbies means that it must be more likely that the cat is brown than that it's a brown tabby.

Exemplary Quotes

“He's a great speaker for a mathematician, and mathematicians are not usually good speakers. Maybe he's some theater, too. But most mathematicians have not done theater, so it's also possible he's just really good at public speaking.”

“When asked whether lightning or sharks are responsible for the most human deaths, most tend to answer sharks since sharks are often portrayed in fiction or documentaries as violent animals when in reality only 19 shark attacks are recorded each year in the United States versus 51 for lightning strikes.”

“TH: O.K., so first we need to figure out why we're trying to improve our finance game, and now we have some tools for tracking our funds. You talk a lot about building better money habits, but how do we do that? KW: It helps to understand our cognitive biases around money and the weird ways our mind can work against us when it comes to money. The concept of anchoring is a good example. It's a bias where we rely too heavily on one piece of information to make decisions. So, for example, let's say you're at a restaurant and you see their featured menu is a $20 cheeseburger. You think, "Whoa, who would pay $20 for a cheeseburger?" Then you see a cheeseburger for $15 and you think it's a good deal, so you order it. But it's not actually a good deal, it just looks that way compared to the $20 anchor. We often think we have more control over spending decisions than we actually do.”

Source

“Apple will perform a massive U-turn this year slashing prices of its new iPhones by as much as $300. Breaking this down, Kuo states prices for the second gen iPhone X, enlarged iPhone X Plus and exciting 'budget iPhone X' will start from as low as $800, $900 and $600 respectively. Given prices for the first generation iPhone X started at $1,000 last year, and Apple is also bundling an essential $100 extra with all its new iPhones this year, that's a total saving of over $300 including tax.”

Source

If you look at the numbers, you can see that there are actually more vaccinated people vaccinated getting sick than unvaccinated people. So getting vaxxed actually makes you more likely to get sick!

This is a case of base rate neglect. Because more people are vaccinated than unvaccinated, you can get this pattern even when vaccination is strongly protective. For example, if 85% of people are vaccinated, and being vaccinated makes you 30% as likely to catch the disease, then out of every hundred people, .85 * .3 * 100 ＝ 25.5 vaccinated people on average will get sick, while .15 * 1 * 100 ＝ 15 unvaccinated people will get sick. So even though more vaccinated people are getting sick in terms of absolute quantity, getting the vaccine still makes any given person 1/3 as likely to get sick.

Heuristics cause us to make wrong judgements so they're bad and we should stop using them.

Heuristics can sometimes lead us towards fallacies. But, that does not mean that they are useless! Heuristics still tend to be better than making decisions arbitrarily. And we don't always have the time or means to fully analyze every decision.

After this lesson, students should

1. Attitudes
   1. Value processes of discovering and correcting errors, recognizing that formal statistics and logic are needed to counteract various types of (frequently unrecognized) errors, despite high confidence. Be aware of the pitfalls of human reasoning.
2. Concept Acquisition
   1. Availability heuristic: Cases in which people use how readily something comes to mind as a proxy for an estimate of its probability.
   2. Representativeness heuristic: Cases in which how representative something is of a category or outcome is used as a proxy for evaluating how likely the category membership or outcome is (not taking base rates into account).
   3. Anchoring heuristic: Cases in which an estimate is made by anchoring on a number that's provided and potentially irrelevant and adjusting, typically insufficiently.
   4. Base rate neglect: People frequently overlook the importance of base rates when calculating the probability of an event based on probabilities that seem more relevant to the specific case.
   5. Base rates: The base frequency of a given attribute in a whole population.
   6. Bayesian reasoning: Systematically combining information regarding new evidence with prior beliefs to determine the probability of a hypothesis.
      1. Bayes' Rule: [math]\displaystyle{ P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)} }[/math], where [math]\displaystyle{ A }[/math] and [math]\displaystyle{ B }[/math] are events and [math]\displaystyle{ P(B) \neq 0 }[/math]. This is the formal statistical rule for applying Bayesian reasoning. When updating beliefs, the final credence level should be influenced by the initial credence level and the strength of the new evidence.
3. Concept Application
   1. Recognize and resist instances of the availability heuristic in everyday and scientific contexts.
   2. Recognize and resist instances of the representativeness heuristic in everyday and scientific contexts.
   3. Recognize and resist instances of the anchoring heuristic in everyday and scientific contexts.
   4. Recognize and resist instances of base rate neglect in everyday and scientific contexts.
   5. Given an example in which a person updates her belief, identify the two factors that should influence her final credence level (initial credence level and strength of the evidence), and recognize that Bayes' rule provides a formal specification of how to do so.

Additional Content

You must be logged in to see this content.

---

⟨

All lesson plans

⟨

Previous

8.2 Fermi Problems

⟩

Next

9.2 Biases