Presentation on theme: "Behavioral and Experimental Economics. Many decisions are based on beliefs concerning the likelihood of uncertain events… Who will win the election? How."— Presentation transcript:
Many decisions are based on beliefs concerning the likelihood of uncertain events… Who will win the election? How will a $ trade for a tomorrow? Is the defendant guilty? What determines such beliefs? How do people assess the probability of an uncertain event or the value of an uncertain quantity?
We reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations. Resembles the subjective assessment of physical quantities such as distance or size. Example of how we judge the distance of an object using clarity. The more sharply the object is seen, the closer it appears to be. This is mostly right but subject to systematic errors.
What is a heuristic? Why do humans use them? Do heuristics help or hurt human decision making?
Heuristic ( /hj ʉˈ r ɪ st ɨ k/; or heuristics; Greek: " Ερίσκω ", "find" or "discover") refers to experience- based techniques for problem solving, learning, and discovery. Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical. Examples of this method include using a "rule of thumb", an educated guess, an intuitive judgment, or common sense./hj ʉˈ r ɪ st ɨ k/Greekrule of thumbcommon sense In more precise terms, heuristics are strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines. problem solving 
http://nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman-autobio.html http://www.dangoldstein.com/dsn/archives/2005/07/amos_tversky_1.html James Monitier Behaving Badly, 2/2006. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=890563 " Judgment under Uncertainty: Heuristics and Biases," Science,1974.
This seminal article by Tversky and Kahneman describes three Heuristics people rely on to assess probabilities and predict values: Representativeness Availability Adjustment and Anchoring
Many of the probabilistic questions with which people are concerned belong to one of the following types: 1. What is the probability that object A belongs to class B OR 2. What is the probability that process B will generate event A
Probabilities are evaluated by the degree to which A is representative of B… In other words, the degree to which A resembles B Is A similar to B?
Linda is 31 years old. She is single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and equality. Which is more likely? 1) Linda works in a bank 2) Linda works in a bank and is active in the feminist movement
To argue that (2) is more likely is to commit a conjunction fallacy. Bankers Feminists Tversky & Kahneman (1983) 85% of professional fund managers chose (1) It is more likely that Linda works in a bank. Feminist Bankers
The Representative Heuristic (rule of thumb) People base judgments on how things appear rather than how statistically likely they are. People are driven by the narrative of the description rather than by the logic of the analysis. Source of the error?
"The best explanation to date of the misperception of random sequences is offered by psychologists Daniel Kahneman and Amos Tversky, who attribute it to peoples tendency to be overly influenced by judgments of representativeness. Representativeness can be thought of as the reflexive tendency to assess the similarity of outcomes, instances, and categories on relatively salient and even superficial features, and then to use these assessments of similarity as a basis of judgment. We expect instances to look like the categories of which they are members; thus, we expect someone who is a librarian to resemble the prototypical librarian. We expect effects to look like their causes; thus we are more likely to attribute a case of heartburn to spicy rather than bland food, and we are more inclined to see jagged handwriting as a sign of a tense rather than a relaxed personality. Gilovich (1991), page 18
Steve is very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.
Farmer Salesman Airline pilot Librarian physician
Problem: similarity, or representativeness, is not influenced by several factors that SHOULD affect judgments of probability
1. Insensitivity to prior probability or outcomes: what is the base-rate frequency of the outcomes? The fact that there are many more farmers than librarians in the popn should enter into any reasonable estimate of the probability that Steve is a librarian rather than a farmer
Subjects shown brief personality descriptions of several individuals Subjects asked to assess, for each description, the prob that it belonged to an engineer rather than a lawyer In one experimental condition, subjects were told that the descriptions had been drawn from a sample of 70 engineers and 30 lawyers In another condition, 30 engineers and 70 lawyers
The odds that any particular description belongs to an engineer rather than to a lawyer should be higher in this condition, where there is a majority of engineers! (.7/.3)^2 = 5.44 In violation of Bayes rule, the subjects in the two conditions produced essentially the same probability judgments. People ignored the math and looked only at the representativeness!
When given no other information, subjects used the prior probabilities (evidence) Evidence ignored when given a description + evidence Evidence ALSO ignored when given a useless description
Dick is a 30 year old man. He is married with no children. A man of high ability and high motivation, he promises to be quite successful in his field. He is well liked by his colleagues.
2. Insensitivity to sample size Example: What is the average height of the following group of men? N = 1000 N = 100 N = 10 How do you make this assessment?
People failed to appreciate the role of sample size. As n increases, variability decreases John Nunley and I both got decent student evaluation scores last year (him: 3.87 me: 3.88 I taught 3 classes and he taught 6 Who is the better teacher?
A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. For a period of one year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded such days?
The larger hospital (21) The smaller hospital (210 About the same (that is, within 5 percent of each other) (53) Discuss
Joes Sample Bettys Sample Who should be more confident that the urn contains 2/3 red balls and 1/3 white? Posterior Odds Urn is 2/3 Red 8-to-1 Red 16-to-1 Red Conclusion: People underestimate evidence Dont consider sample size.
Intuitive judgments are dominated by the sample proportion and are essentially unaffected by the size of the sample.
3. Misconceptions of chance (AKA the gamblers fallacy) People expect that a sequence of events generated by a random process will represent the essential characteristics of that process even when the sequence is short. Example: People think that HTHTTH is more likely than HHHTTT or HHHTH
We think that chance will be displayed globally and locally Imagine a roulette wheel at Vegas that has fallen on red for the last five spins… The next spin MUST be black… Right? RIGHT? We think black is due because it will look more like a representative sequence than if the wheel spins red.
We think of chance as a self-correcting process in which a deviation in one direction induces a deviation in the opposite direction to restore the equilibrium. In fact, deviations are not corrected, rather as the chance process unfolds, they are diluted.
Suppose an unbiased coin is flipped 3 times, and each time it lands on head. If you had to bet $1,000 on the next toss, what side would you chose? Heads, Tails or no preference? 300 fund managers: no preference = 81% Of the 29 2010 BEE students: Tails = 4 (17%) No preference = 17 of 23 (74%) Heads = 2 (8%) What is going on here?
The coin has no memory and each side is equally likely You are betting on a SINGLE flip, not an ENTIRE sequence Source of the fallacy?
The Gamblers Fallacy Abstract Research on decision making under uncertainty demonstrates that intuitive ideas of randomness depart systematically from the laws of chance. Two such departures involving random sequences of events have been documented in the laboratory, the gamblers fallacy and the hot hand. This study presents results from the field, using videotapes of patrons gambling in a casino, to examine the existence and extent of these biases in naturalistic settings. We find small but significant biases in our population, consistent with those observed in the lab. The Gamblers Fallacy and the Hot Hand: Empirical Data from Casinos Rachel CrosonRachel Croson and James SundaliJames Sundali Journal of Risk and Uncertainty (2005)
4. Insensitivity to predictability: if predictability is nil, the same prediction should be made in all cases. Examples: which company will be profitable? What is the future value of a stock? Who will win the football game? What info do you have to make this assessment?
Three descriptions of a company: favorable, mediocre, poor. If mediocre description, mediocre prediction. The degree to which the description is favorable is unaffected by the reliability of that description or by the degree to which it permits accurate description. Predictions insensitive to reliability of evidence.
5. Illusion of validity: The unwarranted confidence which is produced by a good fit between the predicted outcome and the input information People are more confident in their predictions if they perceive that the inputs look more like the outputs-no regard for limits of predictability
People express more confidence in predicting the final GPA of a student whose first-year record consists entirely of Bs than in predicting the GPA of a student whose first year record includes many As and Cs. Basic statistics tells us we can make better predictions if we have independent inputs rather than redundant or correlated inputs
6. Misconception of regression: people dont understand regression toward the mean. Consider two variables X and Y which have the same distribution. If one selects individuals whose average X score deviates from the mean of X by k units, then the average of their Y scores will usually deviate from the mean of Y by less than k units. Note: this was first documented by Galton more than 100 years ago.
Comparison of height of fathers and sons Intelligence of husbands and wives Performance of individuals on consecutive examinations People do not develop correct intuitions about this phenomenon 1. They do not expect regression in many contexts 2. When they do recognize it they invent spurious causal explanations.
Conclusion: Verbal rewards are detrimental to learning, while verbal punishments are beneficial. Pilot instructors note that… Praise for an exceptionally smooth landing is typically followed by a poor landing on next try. Criticism for a rough landing is typically followed by an improvement on next try.
Probability Performance Mean Bad Whats more likely? Improvement or an even worse performance?
People assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind Example: one may assess the risk of a heart attack among middle-aged people by recalling such occurrences among ones acquaintances
1. Biases due to retrievability of the data: a class whose instances are easily retrieved will seem bigger than a similarly-sized class whose instances are less retrievable Car crashes versus airplane crashes Familiarity: example lists of famous people Salience: seeing a house burning Recent-ness
2. Biases due to the effectiveness of a search set: searching for words by first letter Example: Suppose one samples a word (of three letters or more) at random from an English text. Is it more likely that the word starts with r or that r is the third letter?
4. Illusory Correlation: Example mental patients with clinical diagnosis and drawings.
1. What are the last four digits of your telephone number? 2. Is the number of physicians in Wisconsin higher or lower than that number? 3. What is your best guess as to the number of physicians in Wisconsin?
Mean guesses of number of physicians Last 4 digits of telephone # Sources: Behaving Badly (2006) and B.E. Class Survey
Anchoring – the tendency to grab hold of irrelevant and subliminal inputs in the face of uncertainty. If people are rational, there should be no difference between those who happen to have high telephone numbers and those who have low ones
Adjustments to initial estimates are typically insufficient: Example: subjects were asked to estimate various quantities, stated in percentage (for example, the percentage of African countries in the UN). For each quantity, a number between 0 and 100 was determined by spinning a wheel in the subjects presence. Subjects first indicated whether that number was higher or lower than the value and then to estimate the value by moving upward or downward from the given number. Arbitrary starts had marked effect on estimates
Example: estimating 8x7x6x5x4x3x2x1 versus 1x2x3x4x5x6x7x8 To do this fast, take a few steps and extrapolate, but because adjustments are insufficient, the procedure leads to underestimation that is predictable in each sequence median for ascending: 512 Median for descending: 2,250 Actual answer: 40,320
Biases in the evaluation of conjunctive and disjunctive events: example: Simple events: draw a red marble from a bag with 50 percent red and 50 percent white Conjunctive: draw a red marble 7 times in succession with replacement from a bag containing 90% red Disjunctive: draw a red marble at least once in 7 successive tries with replacement from a bag containing 10 percent red.
Biases in the evaluation of conjunctive and disjunctive events Study where subjects were given the opportunity to bet on one of two events. Three types of events were offered:
1. Simple events: drawing a red marble from a bag that is 50% red and 50% white 2. Conjunctive events: drawing a red marble 7 times in succession with replacement from a bag containing 90% red and 10% white 3. Disjunctive events: drawing a red marble at least once in 7 successive tries with replacement from a bag containing 10% red and 90% white
Majority preferred to bet on the conjunctive event (prob is.48) rather than the simple event (prob is.50) Subjects preferred to bet on simple event rather than the disjunctive event (prob.50 versus prob.52) Most bet on the less likely event!
People use the simple probability to start and adjust from there, but they dont adjust enough Note that : Pr(disjunctive) > Pr(simple)>Pr(conjunctive) Due to anchoring: pr of conjunctive is overestimated and pr of disjunctive is underestimated
Framing Hindsight bias Black Swans The Affect Heuristic Scope Neglect Overconfidence Bystander Apathy
Framing refers to a situation whereby we fail to see through the way in which information is provided to us. In our pretest, I presented one question twice – two different frames.
The U.S. is preparing for the outbreak of an unusual disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. The scientific estimates of the consequences of the programs are as follows: If program A is adopted 200 people will be saved. If program B is adopted there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no one will be saved. Which program do you choose?
The U.S. is preparing for the outbreak of an unusual disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. The scientific estimates of the consequences of the programs are as follows: If program C is adopted 400 people will die. If program D is adopted there is a 1/3 probability that nobody will die, and a 2/3 probability that 600 people will die. Which program do you choose?
Program A200 14001 Live prob. Die prob. Program B600 1/36002/3 Program C200 14001 Program D600 1/36002/3 If you pick A over B, you should pick C over D Consistent Preferences imply:
Tendency to look for information that agrees with our prior beliefs. Examples: - Iraq had weapons of mass destruction - God Exists (or God does not exist) - Corporations are bad - raising taxes lowers growth
Failures of logic is an effective ploy to win arguments and winning arguments is an adaptive problem Arguing, after all, is less about seeking truth than about overcoming opposing views…. So while confirmation bias, for instance, may mislead us about whats true and real, by letting examples that support our view monopolize our memory and perception, it maximizes the artillery we wield when trying to convince someone… Confirmation bias has a straightforward explanation, argues Mercier. It contributes to effective argumentation. Source: http://www.newsweek.com/2010/08/05/the-limits-of-reason.html
Frederick, Shane Cognitive Reflection and Decision Making, Journal of Economic Perspectives 19(4), 2005. Three Questions: - Bat and Ball - Machines and Widgets - Lilly pads
Bat + Ball = 1.10 Bat - Ball = 1.00 Ball = Bat -1.00 Bat + (Bat -1.00) = 1.10 2 Bat = 2.10 Bat = $1.05 A bat and a ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost? Ball = $.05
If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets? 5 minutes
47 days ! In a lake, there is a patch of lily pads. Everyday, the patch doubles in size. If it takes 48 days for the patch to cover the entire lake, how long would it take for the patch to cover half the lake?
Group Mean CRT Score 0(%)1(%)2(%)3(%) MIT 2.18 7 1630 48 Princeton 1.63 18 2728 26 Boston fireworks Harvard 1.43 20 3724 20 Fund Mngrs 1.53 24 24 26 26 1.99 10 21 29 40 Sources: Frederick (2006) and B.E. Class Survey ucd(13) 1.24 (41%) 43% 15% 22% 23%
Two parts of the brain: X-System (the default) – Reflexive part in charge of perception. It is an effortless, fast parallel-processing system C-System – Reflective, in charge of thought. It requires deliberate effort to use and is slow, but logical The CRT measures how easy it is for people to interrupt their X-System style automatic response Cognitive Reflection Task (CRT)
People with high CRT scores are more patient The CRT was positively correlated with peoples attitude toward risk What explains variation in scores across participants?
Gamble $100 for sure or a 75% chance of $250 % choosing risky option Low CRTHigh CRT Lose $100 for sure or a 75% chance to lose $250 54%31% $100 for sure or a 3% chance of $7,000 8% 21% Lose $100 for sure or a 3% chance to lose $7,000 63%28% Source: Frederick (2005) 19%28%
Amounted need to win on toss of a fair die to balance 50% chance of $100 loss $152 CRT score of 2 or 3 CRT score of 0 or 1 $103.14 2011
You are going to play a game against the others sitting in this room. The game is simply this. Pick a number between 0 and 100. The winner of the game will be the person who guesses the number closest to two thirds of the average number picked. Your guess is:
Correct answer: 0 Under the assumptions of rationality and common knowledge, this is the only Nash equilibrium x = 2/3*x x = 0 is only solution
You are on a game show. You are offered a choice of one of three doors. Behind two of the doors there is a goat. Behind one of the doors there is a car. Upon you announcing what door you chose, the host of the show opens one of the two doors not selected by you, and reveals a goat. After he has done this, he offers you the opportunity to switch your choice. What should you do, stick or switch?