# 16 <strong>Topic XV. Heuristics & Biases</strong>

**Topic XV. Heuristics & Biases**

- Some of the psychological biases that make our probability judgments go awry.
- Here we will explore the most widespread heuristics and biases which psychologists of judgment and decision-making have discovered in everyday reasoning: the availability heuristic, representativeness heuristic, and anchoring heuristic, and biases like optimism bias, hindsight bias, and status quo bias. Many examples are drawn from Daniel Kahneman's book,
*Thinking Fast and Slow*. - Addressing the Question:
*How can we avoid going wrong?*- Reasoning Biases
- Availability Heuristic
- Representativeness Heuristic
- Anchoring Heuristic
- Base Rate Neglect

## TOPIC RESOURCES

## EXAMPLES

- Exemplary Quotes
*Representativeness Heruistic*- "He's a great speaker for a mathematician, and mathematicians are not usually good speakers. Maybe he's done some theater, too. But most mathematicians have not done theater, so it's also possible he's just really good at public speaking."

*Availability Heuristic*- "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."

*Anchoring Heuristic*

- Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
*Representativeness Heuristic*- "He's a great speaker for a mathematician, and mathematicians are not usually good speakers. People who do theater are good speakers. So he must be a mathematician who does drama."

*Availability Heuristic*- "How can you swim in the ocean? Haven't you heard about shark attacks?"

*Anchoring Heuristic*- "My strategy to buy souvenir goods when on holiday trips is to ask the vendor for the price and negotiate my way down 30% from the initial etiquette price"

## LEARNING GOALS

*A. ATTITUDES*- 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.

*B. CONCEPT ACQUISITION**Availability heuristic:*Cases in which people use how readily something comes to mind as a proxy for an estimate of its probability.*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).*Anchoring heuristic:*Cases in which an estimate is made by anchoring on a number that’s provided and potentially irrelevant and adjusting, typically insufficiently.*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.*Base rates:*The base frequency of a given attribute in a whole population.- a.
*Bayesian reasoning:*Systematically combining information regarding new evidence with prior beliefs to determine the probability of a hypothesis. - b.
*Bayes Rule:*$$P(A|B) = (P(B|A)*P(A))/P(B)$$ where $$A$$ & $$B$$ are events and $$P(B)$$ ≠ $$0$$.- The formal statistical rule for applying Bayesian reasoning. When updating beliefs, final credence level should be influenced by initial credence level and the strength of the new evidence.

*Peak-End Rule:*The tendency to remember the peak, or highlight, and the very end of an experience, and to take them as more representative of the experience as a whole than they really are.

*C. CONCEPT APPLICATION*- Recognize and resist instances of the availability heuristic in everyday and scientific contexts.
- Recognize and resist instances of the representativeness heuristic in everyday and scientific contexts.
- Recognize and resist instances of the anchoring heuristic in everyday and scientific contexts.
- Recognize and resist instances of base rate neglect in everyday and scientific contexts.
- 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.

## CLASS ELEMENTS

- Suggested Readings & Reading Questions
- Clicker Questions
- Suppose you flip a fair coin. Which of the following sequences of heads and tails is more likely: HHHHH or HTHHT?
- A. HHHHH
- B. HTHHT
- C. They're equally likeley

- Dr. Six flips 6 coins at a time and counts how many heads and tails she gets. Every time she gets twice as many heads as tails (i.e., 4+heads), she eats an M&M. Dr. Twelve flips 12 coins at a time and counts how many heads and tails she gets. Every time she gets at least twice as many heads as tails (i.e., 8+heads), she eats an M&M. After 100 sets of flips, who will have eaten more M&Ms?
- A. Dr. Six.
- B. Dr. Twelve.
- C. They’ll have eaten about the same number.

- Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.
- A. Linda is a bankteller.
- B. Linda likes to cook and plays the trumpet.
- C. Linda is a writer.
- D. Linda is a bankteller and active in the feminist movement.

- A bat and a ball together cost $1.10. The bat costs a dollar more than the ball. How much does the ball cost?
- A. Ten cents.
- B. Five cents.
- C. Other.

- Discussion Questions
- Base rate neglect often turns up when people use statistics out-of-context to make their evidence seem stronger than it is. How might someone use base rate neglect to argue that some groups are more violent than others? How might you refute them (by pointing out that they are neglecting base rates)?
- How is base rate neglect connected to the need for a control condition in RCTs?

- Class Exercises
- Small group exercises and clicker questions to demonstrate these effects with the students.

- Homework
- Kahneman and Tversky (1974) end their paper on heuristics and biases with the words: “A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.” Give an example of a real-world situation in which one of the heuristics they discuss could bias judgments, and suggest a strategy for improving judgments. In other words, how might you get people to avoid using the heuristic as a basis for their judgment, and instead rely on a better alternative?