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Day 2 Evolution of Decision-Making.  Tversky and Kahneman, 1974  Heuristics – general rules of thumb, or habits  Generally result in decent estimates.

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Presentation on theme: "Day 2 Evolution of Decision-Making.  Tversky and Kahneman, 1974  Heuristics – general rules of thumb, or habits  Generally result in decent estimates."— Presentation transcript:

1 Day 2 Evolution of Decision-Making

2  Tversky and Kahneman, 1974  Heuristics – general rules of thumb, or habits  Generally result in decent estimates  Can be fooled with systematic biases 2

3  Judging probabilities by the degree to which A is representative of B  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 antinuclear demonstrations. Please check the most likely alternative:  Linda is a bank teller  Linda is a bank teller and is active in the feminist movement 3

4  Most people (9 out of 10) answer B  However, B is more specific than A and is a smaller subset of the population, therefore is not more likely Bank TellersFeminists Bank Tellers who are also Feminists 4

5  Belief that random samples of a population will resemble each other  The mean IQ of a population of eighth graders in a city is known to be 100. You have selected a random sample of 50 children for a study of educational achievements. The first child tested has an IQ of 150. What do you expect the mean IQ to be for the whole sample? 5

6  First child has IQ of 150  Remaining 40 have mean IQ of 100  Total is 5050, average is 101, not 100  We expect remaining sample to somehow “balance out”  Small samples don’t randomly cancel out outliers with other outlier values  Fallacy of the Hot Hand 6

7  Heuristic in which decision-makers “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  What is a more likely cause of death in the US – being killed by falling airplane parts or by a shark?  Death by falling airplane parts is 30 times more likely, but shark death is more easily imagined 7

8  When imagination is limited  When imagining an event is so upsetting that it leads to denial 8

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10  Tversky and Kahneman 1981 define framing as “the decision maker’s conception of the acts, outcomes, and contingencies associated with a particular choice”  Frames are partly controlled by formulation of the problem, and partly controlled by norms, habits, and characteristics of the decision maker 10

11  Decision 1 (risk aversion with gains at stake)  Alternative A – a sure gain of $240 Preferred  Alternative B – a 25% chance to gain $1000, and a 75% chance to gain nothing  Decision 2 (risk seeking with losses at stake)  A sure loss of $750  A 75% chance to lose $1000, and a 25% chance to lose nothing Preferred 11

12  Classic exercise in loss aversion  Two choices presented to respondents  They had to choose option A or option B 12

13  The United States is preparing for the outbreak of an unusual Asian disease, which is expected to kill six hundred people. If program A is adopted, two hundred people will be saved; if program B is adopted, there is a one-third probability that six hundred people will be saved and a two-thirds probability that no people will be saved. Which program do you favor? 13

14  When asked of physicians, 72% chose option A, the safe-and-sure strategy 14

15  The United States is preparing for the outbreak of an unusual Asian disease, which is expected to kill six hundred people. If program C is adopted, four hundred people will die. If program D is adopted, there is a one-third probability that nobody will die and a two-thirds probability that six hundred will die. Which of the two programs do you favor? 15

16  When described in terms of deaths rather than lives saved, physicians reversed their choices, with 78% selecting option D, the risky strategy  Both scenarios are identical in lives lost or saved  Loss aversion is a way of skipping the math and using emotion to make the decision 16

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18  Bernoulli 1713  Linear model of utilities  Six axioms of expected utility theory Plous p. 81  Ordering of alternatives – comparing choices  Dominance – some better than others  Cancellation – common factors cancel out  Transitivity – if a>b and b>c, then a>c  Continuity – gamble preferred over intermediate  Invariance – not affected by presentation style 18

19  Herbert Simon, 1955  First major advance in decision theory since Bernoulli’s in 1700’s  Better match to real world decision-making  Satisfice rather than optimize  Satisficing finds alternative that meets most of the major criteria, then stops  Example – apartment search in Arlington 19

20 Feature 1Feature 2Feature 3Feature 4…Feature n PriceNearnessSizeSecurityNeighbors Complex 1$6502 miles800 sq ftLimitedFlight att. Complex 2$6003 miles810 sq ftNoneCars on blocks Complex 3$3504 miles600 sq ftNoneLots of kids Complex 4$8501 mile900 sq ftGatedNearby sorority Complex 5…$1200 Complex n$900 20

21  Lichtenstein (1971)  Utility theory doesn’t quite all aspects of consumer behavior  Rating of attractiveness (a weight applied to a probably outcome)  A gamble is seen by the authors as a multi- dimensional stimulus  Some dimensions of affect are playing a role in what should be a cognitive decision 21

22  Kahneman and Tversky, 1979  Replaces “utility” concept with “value”  Utility is defined in terms of net worth  Value is defined in terms of gains and losses  Losses loom larger than gains  Endowment effect  What one owns is more valuable than what someone else owns 22

23  The value of each outcome is multiplied by a decision weight  Decision weights are very subject to biases  This leads to a decidedly n0n-symmetrical value function, where the value function for losses is decidedly steeper than that for gains 23

24  Outcomes with small probabilities are overweighted relative to outcomes with higher certainty  This tendency leads to the concepts of insurance and gambling as industries 24

25 25 High Value Low Value Gains Losses

26  Example of $1 found by a millionaire or a homeless person – who values the incremental $1 more?  Who would be more concerned with the loss of that incremental $1?  For gains, this produces risk aversion  For losses, this produces gambling 26

27  Damasio and Loewenstein investing game  In each round, subject decides to invest $1 or invest nothing  No invest, subject keeps dollar  Invest, researcher flips coin for $1 loss or $2.50 gain  Rational investors should always choose to invest 27

28  Decisions made relative to a reference point  Comparison of imaginary outcomes referred to as “counterfactutal reasoning”  Regret is based on two assumptions:  People experience sensations of regret and rejoicing  When making decision under uncertainty, people try to anticipate and take into account these sensations 28

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30  People tend to evaluate personal risk outcomes based on the valence  Positive outcomes – more probable  Negative outcomes – less probable  Rose colored glasses as a lens to our lives 30

31  Conjunctive compound events are A+B, where A and B are simple events  Compound events are preferred when conjunctive  Simple events are preferred when compound events are disjunctive (A or B)  People anchor on the probabilities of the simple events that make up the compound event and fail to adjust probabilities 31

32  Once we estimate probabilities, we are slow to modify those estimates  When modified, the estimates are changed more slowly than the data would dictate 32

33  Three dimensions for public perception of risk (Slovic, 1987)  Dread risk – lack of control, catastrophic potential  Unknown risk – risks that are unobservable  Magnitude of risk – number of people exposed to it 33

34  Maintain accurate records – minimize primacy and recency effects  Beware of wishful thinking – wishing for positive outcomes  Disaggregate compound events into simple events 34

35  What is correlation?  What is causation?  Are they the same? 35

36  What is correlation? Degree of covariation between two variables  What is causation? The outcome of one event resulting in the outcome of another event  Are they the same? No – a common mistake made by many market researchers 36

37  Illusory - People can “see” a correlation between objects based on the objects semantic similarities, even when no correlation exists  Invisible – People fail to see a correlation even when it exists – our expectations of visible patterns causes us to miss some strong but unexpected patterns 37

38  Einhorn and Hogarth, 1986  Correlation need not imply causal connection  Causation need not imply a strong correlation  Some people believe that causation implies correlation – they called it “causalation” 38

39  How people make causal attributions (Kelley, 1967)  Three main variables to explain behavior  The person  The entity – feature of the situation  The time – feature of the occasion  Based on three sources of information  Consensus, distinctiveness, and consistency 39

40  Sometimes people ignore base rate information  Sometimes people focus on salient, available, or vivid information  Fundamental attribution error (Ross, 1958) is that people’s behaviors tend to swamp all other situational variables 40

41  When faced with a successful outcome, people are more likely to accept responsibility and take more credit for the outcome  When faced with an unsuccessful outcome, people are more likely to attribute blame to others  Ego-centric biases – married couple example  Positivity effect – attribute positive behaviors to dispositional factors and negative behaviors to situational factors 41

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