Thinking part II judgment heuristics reasoning decision-making

Judgment Heuristics Tversky & Kahneman propose that people in many cases might not reason optimally (e.g. follow rules of probability or rational choice) Instead, judgment may be based on heuristics Lower cognitive load but may lead to systematic errors and biases Example heuristics representativeness availability Kahneman Tversky

Memory for Names Tom Cruise Celia Weston Tom Hanks Frances O’Connor Jane Adams Mel Gibson Illeana Douglass Jim Carrey Marg Helgenberger George Clooney Debi Mazar Alyson Hannigan Russell Crowe Harrison Ford Bruce Willis Lindsay Crouse Molly Parker Brad Pitt 18 names 9 female names 9 male names

Availability Heuristic A person is said to employ the availability heuristic whenever he/she estimates frequency or probability by the ease with which instances or associations could be brought to mind

Availability Heuristic Are there more words in the English language that begin with the letter V or that have V as their third letter? What about the letter R, K, L, and N? (Tversky & Kahneman, 1973)

Which causes more deaths in developed countries? 1. (a) traffic accidents (b) stomach cancer 2. (a) homicide (b) suicide Note: trend has reversed for 1a and 1b 32,885 traffic deaths in 2010 Stomach cancer 3.5 per 100,000 men and women per year 11K deaths stomach cancer in US in 2013 (Kahneman & Tversky, 1974)

Results Traffic accident vs. Stomach cancer: Typical Guess (in 1974) traffic accident = 4X stomach cancer Actual (1974 estimates) 45,000 traffic, 95,000 stomach cancer deaths in US Ratio of newspaper reports on each subject 137 (traffic fatality) to 1 (stomach cancer death) Actual Homicide vs. Suicide rates (2013): Murder rate 6 per 100,000 Suicide rate 10.8 per 100,000 2013: murder rate in America is about 6 per 100,000; for suicides it’s about 10.8. Nearly a third more people die at their own hands than at other people’s  http://news.discovery.com/human/psychology/suicide-a-more-present-threat-than-murder-130823.htm (Kahneman & Tversky, 1974)

(Lichtenstein et al., 1978)

Why use the availability heuristic? Availability is based on fundamental aspect of memory search Works well under many circumstances Availability correlates with likelihood of events

A hospital is surveyed about the exact sequence of births of boys and girls (from different mothers) in a particular day. What is more likely: G B G B B G B B B B B B

Another example A coin is flipped. What is a more likely sequence? A) H T H T T H B) H H H H H H

Representativeness Heuristic The sequence “H T H T T H” is seen as more representative of or similar to a prototypical coin sequence

Linda is 31 years old, single, outspoken, and very bright 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. Please choose the most likely alternative: (a) Linda is a bank teller (b) Linda is a bank teller and is active in the feminist movement

Conjunction Fallacy Nearly 90% choose the second alternative (bank teller and active in the feminist movement), even though it is logically incorrect (conjunction fallacy) bank tellers feminists hands up who did psychology to avoid venn diagrams and maths aren’t you glad you’re here bank tellers who are not feminists feminists who are not bank tellers feminist bank tellers Kahnemann and Tversky (1982)

Representativeness heuristic Kahneman and Tversky (1982) explained these results using the representativeness heuristic tendency of people to judge probabilities or likelihoods according to how much one thing resembles another Linda is more representative of a feminist bank teller than just a bank teller alone, so people give the second answer

Hot Hand Belief in Basketball Question: Does a basketball player have a better chance of making a free throw shot after having just made his last two shots than he does after having just missed his last two shots? Answers by Cornell and Stanford University Basketball fans Yes = 91% No = 9% (Gilovich, Vallone, & Tversky, 1985)

Does the “hot hand” phenomenon exist? Most basketball coaches/players/fans refer to players having a “Hot hand” or being in a “Hot zone” and show “Streaky shooting” However, making a free throw shot after just making two free throw shots is just as likely as after just missing two shots  people can make errors in judging probabilities of sequential events Some comments by basketball coaches on these statistical studies: “Who is this guy? So he makes a study. I couldn’t care less.” (Celtics owner) “There are so many variables involved in shooting the basketball that a paper like this really doesn’t mean anything.” (Bob Knight; Hoosiers coach) (Gilovich, Vallone, & Tversky, 1985)

What to make of these results? One interpretation of Tversky & Kahneman’s findings: people do not use proper probabilistic reasoning people use arbitrary mechanisms/ heuristics with no apparent rationale However, heuristics can often be very effective

Which city has a larger population? A) San Diego B) San Antonio (TX) 66% accuracy with University of Chicago undergraduates. However, 100% accuracy with German students. San Diego was recognized as American cities by 78% of German students. San Antonio: 4%  With lack of information, the heuristic of picking the city that is recognized is very effective San Diego was only marginally larger than San Antonio at the time of the experiment Currently, San Antonio is actually larger: Population (2012) San Antonio: 1,383 million San Diego: 1,338 million (Goldstein & Gigerenzer, 2002) (note: at the time of the experiment, San Diego actually was the larger city – this is no longer true)

Decision Making In making choices, people are sensitive to outcomes and to degrees of risk. However, people are also heavily influenced by how a decision is framed. When cast in terms of gains, people tend to avoid any risk. When cast in terms of losses, people seek out risk, presumably in hopes of avoiding it. Whereas judgment and reasoning allow us to expand our knowledge based on previous experience or beliefs, decision making involves choosing among various options to guide actions. According to utility theory, decisions should be made by weighing possible outcomes and choosing the one with the greatest benefit, lowest cost, and greatest likelihood. The decisions that people actually make are not this logical.

Framing effect Problem 1 Suppose I give you $300, but you also have to select one of these two options: 1.0 chance of gaining $100 .50 chance of gaining $200 and a .50 chance of gaining nothing Problem 2 Suppose I give you $500, but you also have to select one of these two options: 1.0 chance of losing $100 .50 chance of losing $200 and a .50 chance of losing nothing (72%) (28%) The reversal in preferences does not make sense according to classic expected utility, because the *total* gains for options A and B are the same for problem 1 and 2. Clearly, individuals are focusing on how the options are framed, in terms of gains and losses. For problem 1, A is preferred over B, because people generally tend to be risk averse when it comes to gains. For problem 2, B is preferred over A because people tend to be risk seeking when it comes to losses. Subjective utility theory (and prospect theory) can explain these findings by considering the *subjective* utility of gains and losses. 9.15 Framing effects The outcomes of these two choices are identical. In both cases, option A leaves you with $400, while option B leaves you with a 50-50 chance of getting either $300 or $500. Even so, 72 percent of research participants selected option A in choice 1 and 64 percent selected option B in choice 2. Once again, change in the way the outcomes were framed reversed the choices the participants made. (36%) (64%) (Tversky & Kahneman, 1986)

Framing effect Problem 1: Select one of two prizes (36%) An elegant Cross pen (64%) $6 Problem 2: Select one of three prizes (46%) An elegant Cross pen (52%) $6 (2%) An inferior pen Decisions can change when other options are added – people make different choices depending on how the problem is described (Shafir & Tversky 1995)

Example: Cheeseburgers 50%

Example: Cheeseburgers This is also related to the compromise effect Itamar’s research shows that when given two choices, an expensive and less expensive camera, people will choose the less expensive option. However, when given three options, people prefer the middle option – the compromise effect. Marketers know of this effect and expand the list of options to steer you towards more expensive options. Think also of wine menus at restaurants. It is okay to choose the least expensive wine on menu. Recent research shows that the compromise effect will disappear when users can read reviews of products (e.g. Amazon). Therefore, user reviews might mitigate the compromise effect. 50% 10% 30% 60% 50%

Mental Accounting (A) Imagine you are at an electronics store and about to purchase a pair of headphones for $145 and a calculator for $20. Your friend mentions though, that the same calculator is on sale for $10 at a different store located 20 minutes away. Would you make the trip to the other store? (B) Now, imagine a different scenario. You are at an electronics store and about to purchase a pair of headphones for $20 and a calculator for $145. Your friend mentions that the same calculator is on sale for $135 at a different store located 20 minutes away. Would you make the trip to the other store? Most people would go in (A) but not in (B). However, in either scenario, the total purchase is $165 if you purchase both items in the first store and $155 if you buy at the second store. In both cases, you have to decide whether saving $10 is worth the trip. But people respond differently and treat these purchases into different accounts. If the $10 savings comes from the smaller amount, then it seems like a great deal (50% cheaper). If the savings come from the more expensive account, it seems much less persuasive (less than 7%). (Thaler, 1999)

Reasoning In reasoning, we try to draw implications from our beliefs. This is crucial for the use of knowledge and provides a means of testing our beliefs. However, people often show a pattern of confirmation bias. They take evidence more seriously if it confirms their beliefs than if it challenges them. Humans reason remarkably well in many cases, but like judgment, reasoning is not error free. For example, in the phenomenon of confirmation bias, people seek and then overvalue evidence that will confirm a hypothesis and ignore or distort evidence to the contrary. Confirmation bias can cause beliefs to become more extreme over time and can make it difficult to escape a false belief once it is acquired.

E K 4 7 Wason Selection Task “If a card has a vowel on one side, then it has an even number on the other side” Which cards do you need to turn over to test the correctness of the rule? Usual finding: people turn over the E and the 4 Logically, you need to turn over the E and the 7

Concrete examples are much easier If a person is drinking beer, then the person must be over 21. How to test whether somebody is abiding by this rule? Drinking beer Drinking Coke 16 years of age 22 years of age Problem activated permission schema, with which we are highly familiar From a pure deductive point of view, subjects often fail to reason appropriately with abstract problems However, from an inductive point of view, subjects’ choices are quite reasonable under certain assumptions: Rules such as “If Cause then Effect” are interpreted probabilistically Causes are rare Effects are rare Result: 74% answered correctly

Confirmation bias in hypothesis testing 2-4-8 is a set of numbers that conforms to a rule. Discover the rule by querying with any set of three numbers and I’ll give feedback whether it is a positive or negative example. (Wason, 1960)

Confirmation Bias Wason (1960): subjects test hypotheses by generating positive rather than negative examples Popper (1959): confirmatory strategies provide ambiguous information. The hypothesis may be correct or another hypothesis may be correct  scientists should try to falsify their theories (However, in many cases, it might make more sense to confirm hypotheses, and not to attempt falsification)

UPDATE Look at: Sunstein and zeckhauser 2011, overestimation of terrorist regrets http://www.hks.harvard.edu/fs/rzeckhau/overreaction_risks.pdf Gives a more up to date example of availability bias

Discuss Brain Training Maybe discuss research about intelligence (differeny chapter) and then recent research on brain training Could replace another lecture topic?? http://www.nytimes.com/2015/10/25/opinion/sunday/can-you-get-smarter.html?ref=international