Prospect Theory, Framing and Behavioral Traps Yuval Shahar M.D., Ph.D. Judgment and Decision Making...
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Transcript of Prospect Theory, Framing and Behavioral Traps Yuval Shahar M.D., Ph.D. Judgment and Decision Making...
![Page 1: Prospect Theory, Framing and Behavioral Traps Yuval Shahar M.D., Ph.D. Judgment and Decision Making in Information Systems.](https://reader035.fdocuments.us/reader035/viewer/2022080914/56649d155503460f949ea82c/html5/thumbnails/1.jpg)
Prospect Theory, Framingand Behavioral Traps
Yuval Shahar M.D., Ph.D.
Judgment and Decision Making in Information Systems
![Page 2: Prospect Theory, Framing and Behavioral Traps Yuval Shahar M.D., Ph.D. Judgment and Decision Making in Information Systems.](https://reader035.fdocuments.us/reader035/viewer/2022080914/56649d155503460f949ea82c/html5/thumbnails/2.jpg)
Prospect Theory (Kahneman and Tversky, 1979)
• Normative decision-analysis theory assumes that outcomes of decisions (prospects) are described in terms of total wealth
• In reality, however, prospects are considered in terms of gains, losses, or neutral outcomes relative to some reference point (the current state)
• In Prospect Theory a decision maker considers prospects using a function that values all prospects relative to a reference point. – Phase I is framing the decision problem– Phase II is evaluating the prospects
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Properties of Value Functions in Prospect Theory
• The Value function V(X), where X is a prospect:– Is defined by gains and losses from a reference point– Is concave for gains, and convex for losses
• The value function is steepest near the point of reference: Sensitivity to losses or gains is maximal in the very first unit of gain or loss
– Is steeper in the losses domain than in the gains domain• Suggests a basic human mechanism (it is easier to make
people unhappy than happy)• Thus, the negative effect of a loss is larger than the positive
effect of a gain
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A Value Function in Prospect Theory
GainsLosses
- +
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Decision Weights in Prospect Theory• In Prospect Theory, a probability p is not used as is,
but has a decision weight Π(p)• Thus, the expected value of a prospect is V(X, p; Y, q) = V(X) Π(p) + V(Y) Π(q)
– A simple prospect has one positive and one negative value, such that p+q 1
– If p+q <1 we assume the other values or probabilities are 0; for example, ($100, 0.1; $0, 0.7; $-20, 0.2)
– The weight of middle to high probabilities is relatively reduced as opposed to the two endpoints; thus, going from p=0 to p=0.05 or from p=0.95 to p=1 is more significant than going from p=0.30 to p=0.35 (the “certainty effect”)
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A Decision-Weighting Functionin Prospect Theory
0 P 1
Decision
Weight
Π(p)
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Pseudo-Certainty Effects (I)
• (N = 85) Consider a 2-stage game: In the 1st stage there is a 75% chance of ending the game without winning anything and a 25% chance of moving into the 2nd stage. If you reach the 2nd stage you have a choice:
A. A sure win of $30 (preferred by 74%)
B. An 80% chance of winning $45 (preferred by 26%)
– You need to make your choice before the start of the game
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Pseudo-Certainty Effects (II)• (N=81) Which option do you prefer:
C. A 25% chance to win $30 (preferred by 42%)D. A 20% chance to win $45 (preferred by 58%)
• In fact, prospect A offers a 25% chance of winning $30 (like C); prospect B offers a 25% x 80% = 20% chance of winning $45 (like D)
• The failure of invariance is due to the fact that in options A,B people consider the second stage in isolation (framing) and then give a higher decision weight to the sure gain (a pseudo-certainty effect) in option B, as opposed to the lower weight of the increase from 20% to 25% in options C,D
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Certainty Effects: Probabilistic Insurance
• Imagine the following insurance options:1. Pay a premium for full coverage2. Pay half the premium for full coverage during half the
time, to be decided at random (e.g., only on odd dates of the month)
• Most people reject option (2), contrary to normative theory (which predicts preference towards it, due to a concave utility function)
– The reason is that reducing the risk probability from p to p/2 has a smaller decision weight than reducing the risk from p/2 to 0 (the certainty effect)
– This suggests (and has been verified) that framing an insurance as giving full protection against specific risks makes it more attractive (e.g. only fire vs. flood and fire)
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Prospect Theory and Framing
• Risky prospects can be framed in different ways- as gains or as losses
• Changing the description of a prospect should not change decisions, but it does, in a way predicted by Prospect Theory
• Framing a prospect as a loss rather than a gain, by changing the reference point, changes the decision, due to the properties of the value function (which is steeper in the losses domain, i.e., tends to show loss aversion)
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Framing: Loss Aversion (I)
• Imagine the US is preparing for the outbreak of an Asian disease, expected to kill 600 people (N = 152 subjects):– If program A is adopted, 200 people will be
saved (72% preference)– If program B is adopted, there is one third
probability that 600 people will be saved and two thirds probability that no people will be saved (28% preference)
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Framing: Loss Aversion (II)
• Imagine the US is preparing for the outbreak of an Asian disease, expected to kill 600 people (N = 155 subjects):– If program C is adopted, 400 people will die
(22% preference)– If program D is adopted, there is one third
probability that nobody will die and two thirds probability that 600 people will die (78% preference)
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Mental Accounts
• Imagine the following situations:A. you are about to purchase a jacket for $125 and a
calculator for $15. The salesman mentions that the calculator is on sale for $10 at another branch of the store 20 minutes away by car.
B. you are about to purchase a jacket for $15 and a calculator for $125. The salesman mentions that the calculator is on sale for $120 at another branch of the store 20 minutes away by car.
• 68% (N=88) of subjects were willing to drive to the other store in A, but only 29% (N=93) in B
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Framing and Mental Accounts
• Three potential framing options, or accounts:– Minimal (considers only differences between local
options, such as gaining $5, disregarding common features)
– Topical (considers the context in which the decision arises, such as reducing the price of the calculator from $15 to $10)
– Comprehensive (includes both jacket and calculator in relation to total monthly expenses)
• People usually frame decisions in terms of topical accounts; thus the savings on the calculators are considered relative to their prices in each option
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Loss Aversion : The Endowment Effect(Thaler, 1980)
• It is more painful to give up an asset than it is pleasurable to buy it, an endowment effect– Thus, selling prices are higher than buying prices,
contrary to economic theory
• There is a status quo bias (Samuelson and Zeckhauser 1988) since disadvantages of leaving the current state seem larger than advantages, for multi-attribute utility functions
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Endowments Effects and Fairness
(Kahneman, Knetch and Thaler, 1986)• Evaluate the two situations for fairness:
– A shortage had developed for a car model and the dealer now prices it $200 above list price (29% acceptability)
– A shortage had developed for a car model and the dealer, who had been giving $200 discounts, now sells the model for list price (58% acceptability)
• Perception of fairness depends on whether the question is framed as a reduction in gain or as an actual loss
• Imposing a surcharge is considered less fair than eliminating a discount; thus, cash prices are presented as a discount, rather than credit prices a surcharge
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Framing: Losses Versus Costs
• Consider the following two propositions:A. A gamble that offers a 10% chance of winning $95 and
a 90% chance of losing $5B. A payment of $5 to participate in a lottery with a 10%
chance of winning $100 and a 90% chance of winning nothing
• 55 of 132 subjects reversed their preferences, 42 of them rejecting A and accepting B
• Thinking of $5 as payment (cost) rather than a loss makes the venture more acceptable
• Similarly in experiments in which insurance is presented as the cost of protection vs. a sure loss
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Framing: Sunk-Cost (Dead Loss) Effect
• Framing negative outcomes as costs rather than as a loss improves subjective feelings, as in:– Playing with a tennis elbow in a tennis club, in spite
of pain, maintains evaluation of the membership fee as a cost rather than a dead loss
– Continuing a project that has already cost a lot without any results, rather than starting a new one, although the previous costs are sunk costs
– Eating bad-tasting food you have already paid for
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Summary
• Understanding human behavior requires – separation of prospects into positive and negative, relative
to a status quo, rather than considering absolute values, and – consideration of decision weights rather than probabilities
• Loss aversion, endowment effects, and status quo bias predict (correctly) less symmetry and reversibility in the world than do normative theories
• Framing changes the way decision makers treat the same problem, as can be predicted when the nature of these “anomalies” is taken into account