Judgment and Decision Making

1. Judgment and Decision Making

2. The Problem • Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in politics and social issues and spends most of his time on hobbies which include home carpentry, sailing and mathematical puzzles. • In a sample of 70 lawyers and 30 engineers, how likely is it that Jack is an engineer?

3. The Problem • Judgment Processes are notoriously faulty • Decisions are usually based on partial information • The solutions to decisions are often ambiguous. • Leads to the use of: • Heuristics • Rules of thumb • Biases • Stereotypic decisions

4. What is a decision? • Person must have a goal • There must be many ways to satisfy the goal • There is a set of options • Consideration set: Set of options being evaluated • Options are evaluated in some way • Eventually one of the options is selected

5. Economic approaches influenced much of the psychology of choice • Theories assume people are rational and want to make the optimal choice in a given setting • Rational decision making • What is the optimal choice i.e. best reflects the person’s preferences? • Decisions should be consistent • Law of contradiction • Reasoning processes that use the same information should reach the same conclusions • Those that do not are irrational • Example: Transitivity • If you prefer A to B, and B to C… • Then you should prefer A to C.

6. Rational models • Expected Value Theory • People calculate the potential value of each option • Pick the option with the highest expected value • Raffle with 10% chance to win $5.00 • EV = .10 * $5.00 = $0.50 • Example: Which gamble would you rather play? • A: 20% chance of winning $5.00 • B: 30% chance of winning $4.50 • EV(A) = .20 * $5.00 = $1.00 • EV(B) = .30 * $4.50 = $1.35 • Expected value suggests you should choose B

7. Expected Value Theory • Problem: • Not every dollar has the same subjective value • Graduate student: $100 would allow student to eat better food or to buy new clothes • Lawyer: $100 would not need to be spent on necessities • Example: Lotteries • People often play the lottery • Pay $1.00 for a 1/52,000,000 chance to win $10,000,000 • Expected value of this gamble is less than $1.00 (~$.19)

8. Expected Utility Theory • Value of an outcome is based on the individual’s goals • What can an option be used for? • That is the expected utility of an option • The Expected Utility model: • EU = probability of outcome*utilityi • Expected Utility is a rational model • Obeys the law of contradiction • All choices are transitive • Everything is evaluated relative to a global scale

9. Expected Utility Theory • Lottery • The expected utility of $1.00 may be low • There is not much you can do with $1.00 • The expected utility of the prize may be high • You could do a lot with that kind of money • The low probability of winning does not completely outweigh the high utility of the prize • There is also even the pleasure in dreaming about winning

10. Expected Utility Theory • Problem • The Allais Paradox • Situation 1 • A: A 100% chance to win $1,000 • B: An 89% chance to win $1,000 A 10% chance to win $5,000 A 1% chance to win $0 • Many prefer A • Situation 2 • C: An 11% chance to win $1,000 An 89% chance to win $0 • D: A 10% chance to win $5,000 A 90% chance to win $0 • Many of those who preferred A now prefer D, which is inconsistent as the difference between the two options is the same • 1% difference in probability of outcomes in both situations so • u(a) - u(b) = u($1k) - .89u($1k) - .10u($5k) - .01u($0) • = .11u($1k) - .10u($5k) - .01u($0) • and • u(c) - u(d) = .11u($1k) + .89u($0) - .10u($5k) - .90u($0) • = .11u($1k) - .10u($5k) - .01u($0) • so you should either choose a and c, or b and d.

11. The irrationality of choice • The Allais paradox represents a certainty bias • People prefer to avoid winning nothing and will forgo the likelihood of a larger amount for certain gain • Imagine that the US 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. Assume that the exact scientific estimates of the consequences of the program are as follows: • Program A: 200 people will be saved. • Program B: A 1/3 chance 600 people will be saved, and a 2/3 chance that no people will be saved. • People tend to pick Program A

12. Gains and losses • The previous example suggests people are risk averse for gains • They do not want to risk losing a certain gain. • What happens for losses? • Imagine that the US 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. Assume that the exact scientific estimates of the consequences of the program are as follows: • Program A: 400 people will die • Program B: A 1/3 chance no people will die, and a 2/3 chance that 600 people will die. • People tend to pick Program B • People are risk seeking for losses

13. Influence of Context on JDM • Framing Effects • The way we phrase the question matters • Contributes to other heuristics and biases • People bias toward absolutes rather than probabilities • Would you volunteer? • A disease will inflict 20% of the population. A vaccine is available that protects half of the people that take it. • Two strains of the same disease will each inflict 10% of the population. A vaccine is available that protects everyone against one strain but not the other

14. Framing effects • Kahneman and Tversky • People treat gains and losses differently • Losses loom larger than gains • The same situation feels worse when framed in terms of losses than when framed in terms of gains • May not be true in all cultures • Practical application • When making a decision, try to frame the options both in terms of losses and gains. • See whether your opinions about the options changes

15. Context effects • Explanation-based Decision Making • Trying to fit evaluation into the domain of explanation • Determines the mental models used • Expected utility predicts that each option is evaluated independently of other options • Adding more members to the consideration set should not influence people’s preferences. • The attraction effect • The compromise effect

16. Attraction effect • Choice A and B are better than the other along a particular dimension (e.g. price and quality) • Utility theories suggest that the choice of A or B should be unaffected by the presence of a third alternative • Their utility does not change • However, the presence of a completely dominated choice (A vs. C, B vs. D) attracts people to the dominating alternative

17. Compromise effect • Given choice between D and E • Add F • Is tops along dimension 1 • Results in more choice of D

18. Preference reversals • Slovic & Lichtenstein • Different measures of preference may lead to different outcomes • A: 11/12 chance to win 12 chips 1/12 chance to lose 24 chips • B: 2/12 chance to win 79 chips 10/12 chance to lose 5 chips • Some people asked to choose a bet and then ask how much they would sell the bet for • If choose A should sell for more (expected utility must be higher) • Often gave a higher price for B • There seems to be a compatibility effect • Making a choice increases the weight given to probability • Giving a price increases the weight given to the money prize

19. What else are people doing to make a (bad) choice? • Heuristics and biases • Cognitive heuristics are natural ways of thinking, rules of thumb for decision • However, they represent oversimplifications and may lead to bias

20. Heuristics and Biases • Jack is a 45-year-old man. He is married and has four children. He is generally conservative, careful, and ambitious. He shows no interest in politics and social issues and spends most of his time on hobbies which include home carpentry, sailing and mathematical puzzles. • In a sample of 30 lawyers and 70 engineers, how likely is it that Jack is an engineer (percent)? • Representativeness • Judgments based on the degree to which salient features of an event match those of a parent population • Comparing to the “typical”

21. Conjunction Fallacy • A health survey was taken of a representative sample of all ages. Mr. F. was in the sample. Which is more probable? • Mr. F. has had one or more heart attacks • Mr. F. has had one or more heart attacks and is over 55-years-old. • Conjunction fallacy • Mistake in believing the conjunction of traits is more likely than the individual traits

22. Representativeness • Insensitivity to prior probabilities • Judgments based on perceived frequency of occurrence. • Baseline rates cannot be ignored (Bayesian approach) • Insensitivity to sample size • With larger samples come more typical situations • Misconceptions of chance • Looking random • Gamblers’ Fallacy • Thinking that prior outcomes influence the long run of events

23. Representativeness • Insensitivity to predictability • Predictions based on limited info • Illusion of validity • Varying degrees of overlap among sources of information • Tendency to treat dependent sources as independent • More sources of (dependent) information increases confidence without increasing predictive accuracy. • Misconceptions of regression • Particular outcome, however extreme, may not necessarily mark a significant turn of events • Regression to the mean

24. How the Cues are Utilized • Availability Heuristic • Judgments based on the ease to which instances come to mind. • __ __ __ __ N __ • __ __ __ I N G • Generate words? • Frequency?

25. Availability Heuristic • Bias due to retrievability of instances • Easier-to-retrieve info perceived as more numerous • Solo members/Von Restorff effect • Bias due to (in)effectiveness of search set • E.g. more words that start with r or have r as third letter

26. Availability Heuristic • Illusory Correlation • Not correlated or correlation only due to relationship to third variable • Correlated to a lesser extent • Correlated in the opposite direction • If one event more frequent, something assumed to be correlated with will be judged accordingly • Counterfactual thinking • Availability of alternate explanations

27. Other biases • Confirmation Bias • Tendency to seek or recall information that confirms a hypothesis (or diagnosis) rather than information that refutes the hypothesis. • Hmmm… I wonder if that’s seen in the sciences at all? • May determine what cues are used in judgment • Often the source of prejudice • Hindsight bias • I knew it all along • Overconfidence • Use of norms • What may be the usual case may not apply • Ease of imagining possible alternatives

28. Influence of Context on JDM • Order of Cues • Anchoring and Adjustment • People often start judgment process with an initial value and alter evaluation around this anchor. • Poor initial values • Insufficient modification from new information • Recency and Primacy Effects • In remembering lists of items, memory for initial items and final items is better than memory for “middle” items. • Contributes to Anchoring and Availability Heuristic

29. Causal Schemas • Confidence in a conclusion is higher if you can construct a causal scenario that leads from one situation to the other and is in line with one’s expectations • Which prediction would be more accurate? • Predicting a boy’s height from his father’s height • Predicting a father’s height from his son’s height • Also occurs in jury decision making • Judgments of guilt and innocence are often based on juror’s ease of constructing a coherent story from the evidence.

30. Models that account for JDM process • Economic models predicted rational choices • Obey the law of contradiction • People’s choices are not always optimal in their decision making • That does not mean choices are bad • Psychologists have set up particular circumstances in which people make poor choices • Helps to illustrate processes people use. • Models of choice behavior • Many different processes are used to make choices

31. Prospect Theory • Similar to economic models • Value = Σ (π*ui) • π is subjective probability • u is the utility of each option • Utility is evaluated relative to a reference point rather than some absolute utility • Accounts for framing effects • No objective probability, but rather subjective probability • Objective probability is weighted by various psychological factors

32. Prospect Theory • p is the objective probability of certain outcomes resulting from a given choice, π the subjective weight given to such probability • Much more subjective weight given to higher objective probabilities, much less to lower ones (“not well-behaved at endpoints”) • E.g. weight given to p = .9 not just the combined weight of say, .6 and .3

33. Regret Theory • People may make choices to avoid regret • See Arthur Schopenhauer (19th century) • Status quo bias • People would prefer not to make a change • If a change is made, and it goes badly, there is regret • Regret is often overestimated

34. Reason-based choice • Imagine that you have just taken a tough qualifying examination. You feel tired and run-down, and you are not sure that you passed the exam. In case you failed you have to take the exam again in a couple of months. You now have an opportunity to buy a very attractive 5-day vacation package in Hawaii at an exceptionally low price. The special offer expires tomorrow, while the exam grade will not be available until the following day. • Do you sign up for the trip? Pay a non-refundable $5.00 deposit to decide on purchase after learning the results the next day? Not buy it? • A majority of people given this scenario pay the $5.00. (about 35%, 60%, 5% relatively for the options) • Two other groups are run • One group told they passed the exam: • Most choose to go • One group told they failed the exam: • Most choose to go

35. Reason-based choice • People want to be able to justify their choices • May make decisions that are easiest to justify • Shafir, Simonson, & Tversky • People want a reason to go on the trip. • If they get a passing grade: Celebration • If they get a failing grade: Consolation • Other reason-based effects • The attraction effect • Effect is stronger if people have to justify their choice • Justification is not always good • People tend to use less information and to rely on single dimensions when forced to justify a choice • It is easier to come up with these simpler justifications

36. Effort Accuracy framework • Dealing with complexity • People attempt to make accurate choices • People want to minimize effort • Some methods for making choices are highly accurate • Involve considering a lot of information • Calculating expected utility is a high effort-high accuracy way of making a choice. • Some methods are simpler • Involve considering less information

37. Satisficing • Simon • Choose the first option that is satisfactory • Will find an option that satisfies the goal • Does not guarantee finding the best option • Imagine you are a manager at a supermarket • You need someone to bag groceries • You get 100 applications • The cost of hiring a sub-optimal person is low • Take the first person who looks like they can do the job.

38. Elimination by Aspects • Tversky • Start with the most important attribute • Eliminate all options that are not satisfactory with respect to that attribute • Then go to the next most important attribute • Repeat this process until there is one option left • Lexicographic Semiorder • Like Elimination By Aspects • Look at the most important attribute • Select the option that has the best value on that attribute

39. Mental Accounting • Thaler • Utility theory is a common currency theory • All options are evaluated with respect to utility • But all gains and losses are not viewed as the same • People seem to have a variety of mental accounts • Imagine you are shopping for a calculator and a jacket, and you find them both at the same department store. The calculator costs $25, and the jacket costs $120. You are told that a store across town has both items, but the calculator is $15 cheaper at that store. Do you go across town? • Most people say yes. • If the jacket is $15 cheaper, most people say no. • In each case, they have spent the same amount of money.

40. Mental Accounting • The idea is that people are creating separate mental accounts for different goals. • Money for necessities • Money for entertainment • Spending money from one account does not affect others • Imagine you have gone to the movies to see a show. You got to the front of the line and realized you lost $10, do you still go to the movie? • Most people say yes • Imagine you have gone to the movies to see a show. The ticket costs $10. You buy the ticket early in the day. When you get to the theater, you realize you lost the ticket. Do you buy another one? • Most people say no

41. Mental Accounting • House money effect • You go to a casino and put a quarter in a slot machine. You win $100. • How is your gambling behavior affected? • People are often more willing to gamble in this situation • Not any windfall increase in money works. • You are just about to go into a casino, when you see a newspaper. You own 100 shares of a stock and find out that it went up $1.00 that day. • How is your gambling behavior affected? • Most people’s gambling behavior is unaffected by this news • In the first case, people feel as if they are gambling with the house’s money. In the second case, it feels like their own money.

42. Adaptive Decision Making • People adjust decision-making strategies in an adaptive manner • Satisficing, elimination by aspects, utility, random choice may all be utilized depending on the situation • Payne • Little time pressure, complexity  normative decision making procedures • More pressure, complexity  more reliance on heuristics

43. What makes a good Decision Maker? • Use the best sources of information possible • Base decisions strictly on the information given