Decision Making

1. Decision Making chapter 8

2. Daniel Kahneman • Dan Kahneman (e.g. Kahneman & Tversky, Kahneman & Treisman) won the 2002 Nobel Prize in Economics along with GMU Professor Vernon Smith. “Kahneman has integrated insights from psychology into economics, especially concerning human judgment and decision-making under uncertainty, the Royal Swedish Academy of Sciences said in its citation.” – CNN.com

3. What makes a decision good? • Maximize the expected value of the return • Good decisions produce good outcomes; bad decisions produce bad outcomes. • Expertise -- Experts tend to produce better decisions that novices.

4. What makes a decision good? • Maximizing expected value of return? • Over time, make sure the average payoff of a series of decisions is as high as possible? • Problems include: • the value/cost of a payoff might be hard to estimate • expected value is only realized over the long term; value of individual decisions may vary • it may be more important to avoid loss than to secure a benefit (“loss aversion”)

5. What makes a decision good? • Example • An investor might believe the higher return of a stock investment is less valuable than the security of a savings account. • The expected value of a series of roulette bets is negative, but a single bet might return a positive value

6. What makes a decision good? • Producing a good result for a given decision? • problem: since outcomes are probabilistic, a single good result might not reflect the value of average, long-term results. • Example: a single winning bet on red on a roulette wheel does not indicate that betting red consistently is a good strategy.

7. What makes a decision good? • Domain experts say that it’s good? • Problem: experts’ opinions about what is a good decision might conflict with those of first two criteria • Example: circumstances might meet criteria for a police officer to fire a weapon, even if suspect is not a threat.

8. What Constitutes a Good Decision? • Maximizing Expected Value • Producing a ‘good’ outcome • Meeting experts’ criteria • Expected Value • To calculate expected value: • List all potential outcomes, along with their value • Calculate probability of each outcome • Multiply probability of each outcome by value of each outcome. • Sum the resulting values.

9. Assume that in a lottery, 6 numbers are randomly chosen from the range 1-52. To win, a player must match all 6 numbers. The prize is $1 million. Q: What is the expected value of a $ 1 lottery ticket with 2 chances to win? • Probability of winning = .0000001 • Value of winning = $999,999 • Probability of losing = .9999999 • Value of losing = -$1 • Expected Value of $1 bet = = -$0.90 “A tax on people who aren’t very good at math” – Garrison Keillor

10. Assume that two people agree to bet on tosses of a coin. For every toss that lands heads, Person A wins $1. For every toss that lands tails, Person A loses $1 Q: What is the expected value of a coin toss to Person A? P(heads) = .5 Value(heads) = $1 P(tails) = .5 Value(tails) = -$1 E(coin toss) = (.5 x $1)+(.5 x -$1) = $0.5 - $0.5 = 0 Note that at after any given coin toss, one person or the other may be momentarily ahead of the other

11. Perception of Cues • To make a good decision, people need to be able to properly assess the situation. • That is, they need to look for clues that will guide them in their decision making. • However, humans being humans, there are some inherent biases and weaknesses in their ability to correctly perceive clues…

12. Perception of Cues • Humans are good at • estimating the mean of multiple values • estimating proportions that aren’t too extreme • Humans are poorer at: • estimating extreme proportions • If I have seen 99 normal parts, then detecting 1 abnormal part will have more of an impact than the 100th normal part

13. Perception of Cues • Humans are poorer at: • estimating extreme proportions • extrapolating nonlinear trends

14. Perception of Cues • Humans are poorer at: • estimating extreme proportions • extrapolating nonlinear trends • estimating variance • estimations are affected overall magnitude • tend to estimate ratio of variance to mean magnitude

15. Perception of Cues • Humans are poorer at: • estimating extreme proportions • extrapolating nonlinear trends • estimating variance • estimating degree of correlation in scatter plots • tend to overestimate small correlations and underestimate large correlations

16. Cues and Cue Integration • Observer must attend to & integrate cues to diagnose a situation & establish an hypothesis about the state of the world. • Cues can be characterized by 3 properties: • Diagnosticity • How much evidence does cue provide for a given hypothesis? • Reliability • How much can the cue be trusted? • Information Value = Diagnosticity x Reliability • Salience • How conspicuous is the cue?

17. Cues and Cue Integration Example: A financial website predicts that some company’s stock will rise. Should you consider investing in this stock? • Diagnosticity • Does prediction say that rise is almost certain, or is only a bit more likely than not? • Reliability • Have this website’s predictions been accurate in the past? • Salience • Is prediction written in big letters at the top of the page or in smaller letters near bottom of page?

18. Quality of situation assessment? • How well a person assesses the situation is dependent on: • Comprehensiveness of Info • Is important information missing? • Quantity of information • too little? too much? • Relative Salience of cues • Are some cues more salient than others • Relative weighting (importance) given to cues • Are some cues considered more important?

19. Quality of situation assessment? • Comprehensiveness of info • Important information might be lacking • a good decision maker should know what info is lacking • Example • A computer user calling for tech support might neglect to report a valuable symptom of his computer's problem. An experienced technician should recognize that symptom has not been discussed and ask for relevant info.

20. Quality of situation assessment? • Quantity of information • Cues rarely have an Information Value of 1.0, so we need additional cues to help us make a decision. • However, there might be too much info for decision maker to attend to and remember. • After about two cues, ability to integrate addition info declines • Decision makers might seek more info than they can effectively utilize • Example • a financial web site might provide minutiae which cannot be read & comprehended.

21. Quality of situation assessment? • Relative Salience • Some cues might be bigger/brighter/louder than other info, regardless of relative value. • Salient cues tend to be weighted higher in decision making • Example: • website might place info at top of page, above other info of equal value. • brochure might place some info in small type at bottom of page.

23. Quality of situation assessment? • Relative weighting given to cues • DM might fail to properly discount low value cues. • Often do not give more reliable cues enough weighting. • People tend to weight cues of equal salience equally. • Example: • Nurses might make diagnoses based on number of symptoms present, rather than on Diagnosticity of symptoms

24. Cue Conclusions • Humans good at estimating: mean, non-extreme proportions • Humans bad at estimating: • extreme proportions • extrapolating non-linear trends • estimating variance • estimating degree of correlation in scatter plots • Cues have 3 characteristics • Diagnosticity • Reliability • Salience • Cue Assessment (person side of things) • Comprehensiveness of info • Quantity of Info • Relative Salience • Relative Weighting

25. Expertise and Automaticity • Recognition-Primed DM • Rather than making a calculated decision, experts sometimes employ pattern matching to make rapid decisions. • That is, “I’ve encountered this in the past, and here is the solution I used” • Because detailed analysis is not performed, matching can sometimes occur in error and lead to incorrect or suboptimal choices.

26. Heuristics in Decision Making • Decision makers seem to behave as if guided by a number of Heuristics or “rules of thumb” • simplify decision making • don’t always produce a correct decision, but might be good enough most of the time

27. Example • If a coin is flipped 6 times, which of the following outcomes is most likely? • H T T H T H • H H H T T T • H H H H H T All are equally likely. Gamblers fallacy: assumes independent events are correlated.

28. Example 2 • A group of people contains 70 engineers and 30 lawyers. A person is drawn at random. This person is a bit shy, and enjoys math and science. What is the likelihood that this person is a lawyer? • A group of people contains 30 engineers and 70 lawyers. A person is drawn at random. This person is a bit shy, and enjoys math and science. What is the likelihood that this person is a lawyer?

29. Heuristics & Biases in DM • Representativeness Heuristic • “If it walks like a duck, and quacks like a duck, it’s probably a duck.” • Assessments of situation based on similarity to mental representation of hypothesized situation. • Ignores base rate of events (e.g. what if ducks are rare?) • Example: • If a patient has 4 symptoms that match disease A and 2 symptoms that match disease B, doctor might diagnose A even though B is much more common.

30. Heuristics & Biases in DM • Availability Heuristic • judged likelihood of event might be based on the ease with which event comes to mind. • incorporates base-rate info, because common events come to mind more easily • might be biased by irrelevant characteristics (salience, recency, or simplicity of diagnosis). • Example: • people tend to overestimate the frequency of deaths by an exciting cause (e.g. airplane, sniper) and underestimate frequency of death by mundane cause (e.g. heart disease).

31. Heuristics & Biases in DM • Anchoring Heuristic • after forming a belief, people are biased not to abandon it. • in other words, primacy of info increases its weighting in judgments • Example: • When surprised by reported earnings, analysts naturally anchor to their old earnings until they are convinced the earnings change is due to permanent rather than temporary factors.

32. Heuristics & Biases in DM • Anchoring Heuristic • When a long series of simple cues must be integrated, primacy effects are shown. • When cues are complex (detailed, unfamiliar), recency effects are more likely. • Punchline: • Beliefs might depend on order in which info is presented. • Where info should be equally weighted, should be presented simultaneously.

33. Heuristics & Biases in DM • Confirmation Bias • After forming a belief, people tend to seek evidence consistent with that belief, and discount inconsistent evidence. • Example: • A person that believes in the abilities of a psychic will tend to pay attention to the successes and ignore the failures.

34. Heuristics & Biases in DM • Overconfidence bias • People tend to assume that their judgments are much more accurate than is true • Example: • of all instances when a given stock picker estimates a a stock is 90% likely to climb, it might actually climb only 60% of the time.

35. Heuristics & Biases Conclusions • Experts might rely on automaticity (time-stressed situations) • Heuristics & Biases • Representativeness • Availability • Anchoring • Confirmation Bias • Overconfidence Bias

36. Choice of Action • Certain Choice • Results of action are known with certainty • A DM can optimize choice: • List attributes of potential choices, decide how important each attribute is • For each potential choice, multiply value of attributes by importance of attribute. • 3 Sum products, and choose option which maximizes this sum.

37. optimize choice

38. Choice of Action Or more likely… • Satisfice, or pick a choice which might not be optimal, but is good enough. • BMW dealer is 100 miles away, Mazda dealer is down the street… • or • choose by heuristic elimination by aspects: choose a single attribute, discard any choices which do not meet criterion for that attribute. • Example: • I only have $20k to spend on a vehicle. Therefore, the BMW is out of my price range and the Miata is iffy…

39. Choice of Action • Uncertain Choice • Consequences of choice are not certain • That is, the consequences are probabilistic • Maximize expected utility of outcome Utility = subjective value • Utility is not always the same as objective value

40. Utility Gain Value Gain Loss Loss Distortions of Value and Cost • People underestimate gains in value. • Potential losses are perceived as having greater subjective consequences.

41. Uncertain Choice • Biases in setting Utility • people are loss aversive, prefer to avoid risk of loss rather than gamble on a gain. • Example: • Imagine that you're a contestant on a TV game show. You have just won $10,000. The host offers you a choice: You can quit now and keep the $10,000, or you can play again. If you play again, there is a 0.5 probability that you will win again, and wind up with $20,000. If you play again and lose, you lose your $10,000 and take home nothing. You quickly calculate that the expected value of playing again is $10,000, the same as sticking with the $10,000 you have won so far. Which do you chose?

42. Utility Gain Value Gain Loss Loss Distortions of Value and Cost • There are progressively smaller gains in utility as value increases. A gift of $100 seems like a lot if you are a poor graduate student, but is a pittance of you are a movie star.

43. 1.0 Subjective Probability 0 1.0 Stated Probability Perceptions of Probability • Probability of rare events is overestimated e.g. probability of needing to file insurance claim is low

44. Perceptions of Probability • Example: Insurance • The estimated value of purchasing insurance is negative: on average, you will lose money (other wise the insurer would not make money). • Example: • the stock market might appear more risking than a savings account. However, with interest rate below the rate of inflation, a savings account guarantees losses

45. 1.0 Subjective Probability 0 1.0 Stated Probability Perceptions of Probability 2. Reduced Sensitivity to probability changes at low P(). e.g. sluggish beta, representativeness heuristic, & ignorance of base rates

46. 1.0 Subjective Probability 0 1.0 Stated Probability Perceptions of Probability 3. Perceived probability is less than real probability e.g. the probability of a risky but high-payoff outcome might be underestimated, and a surefire (but low-paying) outcome might be chosen instead.

47. Uncertain Choice • Biases in setting Utility • Perception of outcome as a gain or loss is determined by perceived neutral point. This is the framing effect. • Example: • people see a treatment with 90% survival rate as being preferable to a treatment with 10% mortality rate.

48. Biases in Uncertain Choice • Direct Retrieval/automaticity • Experts might automatically retrieve a solution when the cues fit a given pattern, rather than analyzing the situation. • Error example: • Policeman fires his gun when a person reaches into his pocket.

49. Choice Conclusions • Certain Choice • outcomes are known • Optimize choice • Satisfice • Elimination by aspect • Uncertain Choice • outcomes are probabilistic • maximize expected utility • gain is underestimated • loss is overestimated • rare events are overestimated • if not rare, overall probability is underestimated • Direct Retrieval/automaticity

50. Improving Decision Making • Practice in DM doesn’t necessarily make perfect. • Expertise in a DM task doesn’t make one immune from the effects of biases and heuristics.