The Framing of Decisions and thePsychology of ChoiceAmos Tversky and Daniel Kahneman

Explanations and predictions ofpeople's choices, in everyday life as wellas in the social sciences, are often found-ed on the assumption of human rational-ity. The definition of rationality has beenmuch debated, but there is general agree-ment that rational choices should satisfysome elementary requirements of con-sistency and coherence. In this article

tional choice requires that the preferencebetween options should not reverse withchanges of frame. Because of imperfec-tions of human perception and decision,however, changes of perspective oftenreverse the relative apparent size of ob-jects and the relative desirability of op-tions.We have obtained systematic rever-

Summary. The psychological principles that govern the perception of decision prob-lems and the evaluation of probabilities and outcomes produce predictable shifts ofpreference when the same problem is framed in different ways. Reversals of prefer-ence are demonstrated in choices regarding monetary outcomes, both hypotheticaland real, and in questions pertaining to the loss of human lives. The effects of frameson preferences are compared to the effects of perspectives on perceptual appear-ance. The dependence of preferences on the formulation of decision problems is asignificant concern for the theory of rational choice.

we describe decision problems in whichpeople systematically violate the re-quirements of consistency and coher-ence, and we trace these violations to thepsychological principles that govern theperception of decision problems and theevaluation of options.A decision problem is defined by the

acts or options among which one mustchoose, the possible outcomes or con-sequences of these acts, and the contin-gencies or conditional probabilities thatrelate outcomes to acts. We use the term"'decision frame" to refer to the deci-sion-maker's conception of the acts, out-comes, and contingencies associatedwith a particular choice. The frame that adecision-maker adopts is controlled part-ly by the formulation of the problem andpartly by the norms, habits, and personalcharacteristics of the decision-maker.

It is often possible to frame a given de-cision problem in more than one way.Alternative frames for a decision prob-lem may be compared to alternative per-spectives on a visual scene. Veridicalperception requires that the *perceivedrelative height of two neighboring moun-tains, say, should not reverse withchanges of vantage point. Similarly, ra-

SCIENCE, VOL. 211, 30 JANUARY 1981

sals of preference by variations in theframing of acts, contingencies, or out-comes. These effects have been ob-served in a variety of problems and inthe choices of different groups of respon-dents. Here we present selected illustra-tions of preference reversals, with dataobtained from students at Stanford Uni-versity and at the University of BritishColumbia who answered brief question-naires in a classroom setting. The totalnumber of respondents for each problemis denoted by N, and the percentagewho chose each option is indicated inbrackets.The effect of variations in framing is

illustrated in problems 1 and 2.

Problem 1 [N = 152]: Imagine that the U.S.is preparing for the outbreak of an unusualAsian disease, which is expected to kill 600people. Two alternative programs to combatthe disease have been proposed. Assume thatthe exact scientific estimate of the con-sequences of the programs are as follows:If Program A is adopted, 200 people will be

saved. [72 percent]If Program B is adopted, there is 1/3 probabil-

ity that 600 people will be saved, and2/3 probability that no people will besaved. [28 percent]

Which ofthe two programs would you favor?

The majority choice in this problem isrisk averse: the prospect of certainlysaving 200 lives is more attractive than arisky prospect of equal expected value,that is, a one-in-three chance of saving600 lives.A second group of respondents was

given the cover story of problem 1 with adifferent formulation of the alternativeprograms, as follows:

Problem 2 [N = 155]:If Program C is adopted 400 people will die.

[22 percent]If Program D is adopted there is 1/3 probabil-

ity that nobody will die, and 2/3 probabili-ty that 600 people will die. [78 percent]

Which ofthe two programs would you favor?

The majority choice in problem 2 isrisk taking: the certain death of 400people is less acceptable than the two-in-three chance that 600 will die. The pref-erences in problems 1 and 2 illustrate acommon pattern: choices involving gainsare often risk averse and choices in-volving losses are often risk taking.However, it is easy to see that the twoproblems are effectively identical. Theonly difference between them is that theoutcomes are described in problem 1 bythe number of lives saved and in problem2 by the number of lives lost. The changeis accompanied by a pronounced shiftfrom risk aversion to risk taking. Wehave observed this reversal in severalgroups of respondents, including univer-sity faculty and physicians. Inconsistentresponses to problems 1 and 2 arise fromthe conjunction of a framing effect withcontradictory attitudes toward risks in-volving gains and losses. We turn nowto an analysis of these attitudes.

The Evaluation of Prospects

The major theory of decision-makingunder risk is the expected utility model.This model is based on a set of axioms,for example, transitivity of preferences,which provide criteria for the rationalityof choices. The choices of an individualwho conforms to the axioms can be de-scribed in terms of the utilities of variousoutcomes for that individual. The utilityof a risky prospect is equal to the ex-pected utility of its outcomes, obtainedby weighting the utility of each possibleoutcome by its probability. When facedwith a choice, a rational decision-makerwill prefer the prospect that offers thehighest expected utility (1, 2).Dr. Tversky is a professor of psychology at Stan-

ford University, Stanford, California 94305, and Dr.Kahneman is a professor of psychology at the Uni-versity of British Columbia, Vancouver, CanadaV6T IW5.

0036-8075/81/0130-0453$01.50/0 Copyright © 1981 AAAS 453

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As will be illustrated below, people ex-hibit patterns of preference which appearincompatible with expected utility theo-ry. We have presented elsewhere (3) adescriptive model, called prospect theo-ry, which modifies expected utility theo-ry so as to accommodate these observa-tions. We distinguish two phases in thechoice process: an initial phase in whichacts, outcomes, and contingencies areframed, and a subsequent phase of eval-uation (4). For simplicity, we restrict theformal treatment of the theory to choicesinvolving stated numerical probabilitiesand quantitative outcomes, such as mon-ey, time, or number of lives.Consider a prospect that yields out-

come x with probability p, outcome ywith probability q, and the status quowith probability 1 - p - q. Accordingto prospect theory, there are values v(.)associated with outcomes, and decisionweights 7r(.) associated with probabili-ties, such that the overall value of theprospect equals 7r(p) v(x) + ir(q) v(y). Aslightly different equation should be ap-plied if all outcomes of a prospect are onthe same side of the zero point (5).

In prospect theory, outcomes are ex-pressed as positive or negative devia-tions (gains or losses) from a neutral ref-erence outcome, which is assigned a val-ue of zero. Although subjective valuesdiffer among individuals and attributes,we propose that the value function iscommonly S-shaped, concave above thereference point and convex below it, asillustrated in Fig. 1. For example, the dif-ference in subjective value betweengains of $10 and $20 is greater than thesubjective difference between gains of$110 and $120. The same relation be-tween value differences holds for thecorresponding losses. Another propertyof the value function is that the responseto losses is more extreme than the re-sponse to gains. The displeasure associ-ated with losing a sum ofmoney is gener-ally greater than the pleasure associatedwith winning the same amount, as is re-flected in people's reluctance to acceptfair bets on a toss of a coin. Several stud-ies of decision (3, 6) and judgment (7)have confirmed these properties of thevalue function (8).The second major departure of pros-

pect theory from the expected utilitymodel involves the treatment of proba-bilities. In expected utility theory theutility of an uncertain outcome is weight-ed by its probability; in prospect theorythe value of an uncertain outcome is mul-tiplied by a decision weight 7r(p), whichis a monotonic function of p but is not aprobability. The weighting function ir

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Value

Losses

Fig. 1. A hypothetical value function.

has the following properties. First, im-possible events are discarded, that is,*0) = 0, and the scale is normalized so

that r( 1) = 1, but the function is not wellbehaved near the endpoints. Second,for low probabilities 7r(p) > p, butr(p) + 7r(1 - p) < 1. Thus low proba-bilities are overweighted, moderate andhigh probabilities are underweighted,and the latter effect is more pronouncedthan the former. Third, 7r(pq)/ir(p) <7r(pqr)/7r(pr) for all 0 < p, q, r ' 1. Thatis, for any fixed probability ratio q, theratio of decision weights is closer tounity when the probabilities are lowthan when they are high, for example,ir(.1)/7r(.2) > 7r(.4)/7r(.8). A hypotheticalweighting function which satisfies theseproperties is shown in Fig. 2. The majorqualitative properties of decision weightscan be extended to cases in which theprobabilities of outcomes are subjective-ly assessed rather than explicitly given.In these situations, however, decisionweights may also be affected by othercharacteristics of an event, such as am-

biguity or vagueness (9).Prospect theory, and the scales illus-

trated in Figs. 1 and 2, should be viewedas an approximate, incomplete, and sim-plified description of the evaluation ofrisky prospects. Although the propertiesof v and Xr summarize a common patternof choice, they are not universal: thepreferences of some individuals are notwell described by an S-shaped valuefunction and a consistent set of decisionweights. The simultaneous measurementof values and decision weights involvesserious experimental and statistical diffi-culties (10).

If Xr and v were linear throughout, thepreference order between options wouldbe independent of the framing of acts,outcomes, or contingencies. Because ofthe characteristic nonlinearities of ir andv, however, different frames can lead todifferent choices. The following threesections describe reversals of preferencecaused by variations in the framing ofacts, contingencies, and outcomes.

The Framing of Acts

Problem 3 [N = 150]: Imagine that you facethe following pair of concurrent decisions.First examine both decisions, then indicatethe options you prefer.Decision (i). Choose between:A. a sure gain of $240 [84 percent]B. 25% chance to gain $1000, and

75% chance to gain nothing [16 percent]Decision (ii). Choose between:

C. a sure loss of $750 [13 percent]D. 75% chance to lose $1000, and

25% chance to lose nothing [87 percent]

The majority choice in decision (i) isrisk averse: a riskless prospect is pre-ferred to a risky prospect of equal orgreater expected value. In contrast, themajority choice in decision (ii) is risk tak-ing: a risky prospect is preferred to ariskless prospect of equal expected val-ue. This pattern of risk aversion inchoices involving gains and risk seekingin choices involving losses is attributableto the properties of v and 7r. Because thevalue function is S-shaped, the value as-sociated with a gain of $240 is greaterthan 24 percent of the value associatedwith a gain of $1000, and the (negative)value associated with a loss of $750 issmaller than 75 percent of the value asso-ciated with a loss of $1000. Thus theshape of the value function contributesto risk aversion in decision (i) and to riskseeking in decision (ii). Moreover, theunderweighting of moderate and highprobabilities contributes to the relativeattractiveness of the sure gain in (i) andto the relative aversiveness of the sureloss in (ii). The same analysis applies toproblems 1 and 2.Because (i) and (ii) were presented to-

gether, the respondents had in effect tochoose one prospect from the set: A andC,BandC, AandD, BandD. The mostcommon pattern (A and D) was chosenby 73 percent of respondents, while theleast popular pattern (B and C) waschosen by only 3 percent of respondents.However, the combination of B andC is definitely superior to the combina-tion A and D, as is readily seen in prob-lem 4.

Problem 4 [N = 86]. Choose between:A & D. 25% chance to win $240, and

75.% chance to lose $760. [O per-cent]

B & C. 25% chance to win $250, and75% chance to lose $750. [100 per-cent]

When the prospects were combinedand the dominance of the second optionbecame obvious, all respondents chosethe superior option. The popularity ofthe inferior option in problem 3 impliesthat this problem was framed as a pair of

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separate choices. The respondents ap-

parently failed to entertain the possibilitythat the conjunction of two seeminglyreasonable choices could lead to an un-

tenable result.The violations of dominance observed

in problem 3 do not disappear in thepresence of monetary incentives. A dif-ferent group of respondents who an-

swered a modified version of problem 3,with real payoffs, produced a similar pat-tern of choices (11). Other authors havealso reported that violations of the rulesof rational choice, originally observed inhypothetical questions, were not elimi-nated by payoffs (12).We suspect that many concurrent de-

cisions in the real world are framed inde-pendently, and that the preference orderwould often be reversed if the decisionswere combined. The respondents inproblem 3 failed to combine options, al-though the integration was relativelysimple and was encouraged by instruc-tions (13). The complexity of practicalproblems of concurrent decisions, suchas portfolio selection, would preventpeople from integrating options withoutcomputational aids, even if they were in-clined to do so.

The Framing of Contingencies

The following triple of problems illus-trates the framing of contingencies. Eachproblem was presented to a differentgroup of respondents. Each group was

told that one participant in ten, pre-

selected at random, would actually beplaying for money. Chance events were

realized, in the respondents' presence,by drawing a single ball from a bag con-

taining a known proportion of balls of thewinning color, and the winners were paidimmediately.

Problem 5 [N = 77]: Which of the followingoptions do you prefer?A. a sure win of $30 [78 percent]B. 80%o chance to win $45 [22 percent]

Problem 6 [N = 85]: Consider the followingtwo-stage game. In the first stage, there is a75% chance to end the game without winninganything, and a 25% chance to move into thesecond stage. If you reach the second stageyou have a choice between:C. a sure win of $30 [74 percent]D. 8%o chance to win $45 [26 percent]Your choice must be made before the gamestarts, i.e., before the outcome of the firststage is known. Please indicate the option youprefer.

Problem 7 [N = 81]: Which of the followingoptions do you prefer?E. 25% chance to win $30 [42 percent]F. 20%o chance to win $45 [58 percent]30 JANUARY 1981

1.0

005

0

CD

0 0.5 1.0

Stated probability: p

Fig. 2. A hypothetical weighting function.

Let us examine the structure of theseproblems. First, note that problems 6and 7 are identical in terms of probabili-ties and outcomes, because prospect Coffers a .25 chance to win $30 and pros-pect D offers a probability of .25 x.80 = .20 to win $45. Consistency there-fore requires that the same choice bemade in problems 6 and 7. Second, notethat problem 6 differs from problem 5 on-

ly by the introduction of a preliminarystage. If the second stage of the game isreached, then problem 6 reduces to prob-lem 5; if the game ends at the first stage,the decision does not affect the outcome.Hence there seems to be no reason tomake a different choice in problems 5

and 6. By this logical analysis, problem 6is equivalent to problem 7 on the one

hand and problem 5 on the other. Theparticipants, however, responded simi-larly to problems 5 and 6 but differentlyto problem 7. This pattern of responsesexhibits two phenomena of choice: thecertainty effect and the pseudocertaintyeffect.The contrast between problems 5 and

7 illustrates a phenomenon discoveredby Allais (14), which we have labeled thecertainty effect: a reduction of the proba-bility of an outcome by a constant factorhas more impact when the outcome was

initially certain than when it was merelyprobable. Prospect theory attributes thiseffect to the properties of ir. It is easy toverify, by applying the equation of pros-pect theory to problems 5 and 7, thatpeople for whom the value ratio v(30)/v(45) lies between the weight ratios7r(.20)/ir(.25) and ir(.80)/ir(1.0) will pre-

fer A to B and F to E, contrary to ex-

pected utility theory. Prospect theorydoes not predict a reversal of preferencefor every individual in problems 5 and7. It only requires that an individual whohas no preference between A and B pre-fer F to E. For group data, the theorypredicts the observed directional shiftofpreference between the two problems .

The first stage of problem 6 yields t.same outcome (no gain) for both actConsequently, we propose, people evaluate the options conditionally, as if thesecond stage had been reached. In thisframing, of course, problem 6 reduces toproblem 5. More generally, we suggestthat a decision problem is evaluated con-ditionally when (i) there is a state inwhich all acts yield the same outcome,such as failing to reach the second stageof the game in problem 6, and (ii) thestated probabilities of other outcomesare conditional on the nonoccurrence ofthis state.The striking discrepancy between the

responses to problems 6 and 7, which areidentical in outcomes and probabilities,could be described as a pseudocertaintyeffect. The prospect yielding $30 is rela-tively more attractive in problem 6 thanin problem 7, as if it had the advantage ofcertainty. The sense of certainty associ-ated with option C is illusory, however,since the gain is in fact contingent onreaching the second stage of the game(15).We have observed the certainty effect

in several sets of problems, with out-comes ranging from vacation trips to theloss of human lives. In the negative do-main, certainty exaggerates the aversive-ness of losses that are certain relative tolosses that are merely probable. In aquestion dealing with the response to anepidemic, for example, most respond-ents found "a sure loss of 75 lives" moreaversive than "80%o chance to lose 100lives" but preferred "10%o chance to lose75 lives" over "8% chance to lose 100lives," contrary to expected utility theo-ry.We also obtained the pseudocertainty

effect in several studies where the de-scription of the decision problems fa-vored conditional evaluation. Pseudo-certainty can be induced either by a se-quential formulation, as in problem 6, orby the introduction of causal contin-gencies. In another version of the epi-demic problem, for instance, respond-ents were told that risk to life existed on-ly in the event (probability .10) that thedisease was carried by a particular virus.Two alternative programs were said toyield "a sure loss of 75 lives" or "80%chance to lose 100 lives" if the criticalvirus was involved, and no loss of life inthe event (probability .90) that the dis-ease was carried by another virus. In ef-fect, the respondents were asked tochoose between 10 percent chance oflosing 75 lives and 8 percent chance oflosing 100 lives, but their preferenceswere the same as when the choice was

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-ween a sure loss of 75 lives and 80rcent chance of losing 100 lives. Ainditional framing was evidently

.-dopted in which the contingency of thenoncritical virus was eliminated, givingrise to a pseudocertainty effect. The cer-tainty effect reveals attitudes toward riskthat are inconsistent with the axioms ofrational choice, whereas the pseudo-certainty effect violates the more funda-mental requirement that preferencesshould be independent of problem de-scription.Many significant decisions concern ac-

tions that reduce or eliminate the proba-bility of a hazard, at some cost. Theshape of Xr in the range of low probabili-ties suggests that a protective actionwhich reduces the probability of a harmfrom 1 percent to zero, say, will be val-ued more highly than an action that re-duces the probability of the same harmfrom 2 percent to 1 percent. Indeed,probabilistic insurance, which reducesthe probability of loss by half, is judgedto be worth less than half the price ofregular insurance that eliminates the riskaltogether (3).

It is often possible to frame protectiveaction in either conditional or uncon-ditional form. For example, an insurancepolicy that covers fire but not flood couldbe evaluated either as full protectionagainst the specific risk of fire or as a re-duction in the overall probability ofproperty loss. The preceding analysissuggests that insurance should appearmore attractive when it is presented asthe elimination of risk than when it is de-scribed as a reduction of risk. P. Slovic,B. Fischhoff, and S. Lichtenstein, in anunpublished study, found that a hypo-thetical vaccine which reduces the prob-ability of contracting a disease from .20to .10 is less attractive if it is described aseffective in half the cases than if it is pre-sented as fully effective against one oftwo (exclusive and equiprobable) virusstrains that produce identical symptoms.In accord with the present analysis ofpseudocertainty, the respondents valuedfull protection against an identified vi-rus more than probabilistic protectionagainst the disease.The preceding discussion highlights

the sharp contrast between lay responsesto the reduction and the elimination ofrisk. Because no form of protective ac-tion can cover all risks to human welfare,all insurance is essentially probabilistic:it reduces but does not eliminate risk.The probabilistic nature of insurance iscommonly masked by formulations thatemphasize the completeness of pro-tection against identified harms, but thesense of security that such formulations

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provide is an illusion of conditional fram-ing. It appears that insurance is boughtas protection against worry, not onlyagainst risk, and that worry can be ma-nipulated by the labeling of outcomesand by the framing of contingencies. It isnot easy to determine whether peoplevalue the elimination of risk too much orthe reduction of risk too little. The con-trasting attitudes to the two forms of pro-tective action, however, are difficult tojustify on normative grounds (16).

The Framing of Outcomes

Outcomes are commonly perceived aspositive or negative in relation to a refer-ence outcome that is judged neutral.Variations of the reference point cantherefore determine whether a given out-come is evaluated as a gain or as a loss.Because the value function is generallyconcave for gains, convex for losses, andsteeper for losses than for gains, shifts ofreference can change the value dif-ference between outcomes and therebyreverse the preference order betweenoptions (6). Problems 1 and 2 illustrateda preference reversal induced by a shiftof reference that transformed gains intolosses.For another example, consider a per-

son who has spent an afternoon at therace track, has already lost $140, and isconsidering a $10 bet on a 15:1 long shotin the last race. This decision can beframed in two ways, which correspondto two natural reference points. If thestatus quo is the reference point, the out-comes of the bet are framed as a gain of$140 and a loss of $10. On the otherhand, it may be more natural to view thepresent state as a loss of $140, for thebetting day, and accordingly frame thelast bet as a chance to return to the refer-ence point or to increase the loss to $150.Prospect theory implies that the latterframe will produce more risk seekingthan the former. Hence, people who donot adjust their reference point as theylose are expected to take bets that theywould normally find unacceptable. Thisanalysis is supported by the observationthat bets on long shots are most popularon the last race of the day (17).Because the value function is steeper

for losses than for gains, a difference be-tween options will loom larger when it isframed as a disadvantage of one optionrather than as an advantage of the otheroption. An interesting example of suchan effect in a riskless context has beennoted by Thaler (18). In a debate on aproposal to pass to the consumer someof the costs associated with the process-

ing of credit-card purchases, representa-tives of the credit-card industry re-quested that the price difference be la-beled a cash discount rather than acredit-card surcharge. The two labels in-duce different reference points by implic-itly designating as normal reference thehigher or the lower of the two prices. Be-cause losses loom larger than gains, con-sumers are less willing to accept a sur-charge than to forego a discount. A simi-lar effect has been observed inexperimental studies of insurance: theproportion of respondents who preferreda sure loss to a larger probable loss wassignificantly greater when the formerwas called an insurance premium (19,20).

These observations highlight the labil-ity of reference outcomes, as well astheir role in decision-making. In the ex-amples discussed so far, the neutral ref-erence point was identified by the label-ing of outcomes. A diversity of factorsdetermine the reference outcome ineveryday life. The reference outcome isusually a state to which one has adapted;it is sometimes set by social norms andexpectations; it sometimes correspondsto a level of aspiration, which may ormay not be realistic.We have dealt so far with elementary

outcomes, such as gains or losses in asingle attribute. In many situations, how-ever, an action gives rise to a compoundoutcome, which joins a series of changesin a single attribute, such as a sequenceof monetary gains and losses, or a set ofconcurrent changes in several attributes.To describe the framing and evaluationof compound outcomes, we use the no-tion of a psychological account, definedas an outcome frame which specifies (i)the set of elementary outcomes that areevaluated jointly and the manner inwhich they are combined and (ii) a refer-ence outcome that is considered neutralor normal. In the account that is set upfor the purchase of a car, for example,the cost of the purchase is not treated asa loss nor is the car viewed as a gift.Rather, the transaction as a whole isevaluated as positive, negative, or neu-tral, depending on such factors as theperformance of the car and the price ofsimilar cars in the market. A closely re-lated treatment has been offered by Tha-ler (18).We propose that people generally

evaluate acts in terms of a minimal ac-count, which includes only the directconsequences of the act. The minimalaccount associated with the decision toaccept a gamble, for example, includesthe money won or lost in that gamble andexcludes other assets or the outcome of

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previous gambles. People commonlyadopt minimal accounts because thismode of framing (i) simplifies evaluationand reduces cognitive strain, (ii) reflectsthe intuition that consequences shouldbe causally linked to acts, and (iii)matches the properties of hedonic expe-rience, which is more sensitive to desir-able and undesirable changes than tosteady states.There are situations, however, in

which the outcomes of an act affect thebalance in an account that was pre-viously set up by a related act. In thesecases, the decision at hand may be eval-uated in terms of a more inclusive ac-count, as in the case of the bettor whoviews the last race in the context of ear-lier losses. More generally, a sunk-costeffect arises when a decision is referredto an existing account in which the cur-rent balance is negative. Because of thenonlinearities of the evaluation process,the minimal account and a more in-clusive one often lead to differentchoices.Problems 8 and 9 illustrate another

class of situations in which an existingaccount affects a decision:

Problem 8 [N = 183]: Imagine that youhave decided to see a play where admission is$10 per ticket. As you enter the theater youdiscover that you have lost a $10 bill.Would you still pay $10 for a ticket for the

play?Yes [88 percent] No [12 percent]

Problem 9 [N = 200]: Imagine that youhave decided to see a play and paid the admis-sion price of $10 per ticket. As you enter thetheater you discover that you have lost theticket. The seat was not marked and the ticketcannot be recovered.Would you pay $10 for another ticket?

Yes [46 percent] No [54 percent]

The marked difference between the re-sponses to problems 8 and 9 is an effectof psychological accounting. We pro-pose that the purchase of a new ticket inproblem 9 is entered in the account thatwas set up by the purchase of the originalticket. In terms of this account, the ex-pense required to see the show is $20, acost which many of our respondents ap-parently found excessive. In problem 8,on the other hand, the loss of $10 is notlinked specifically to the ticket purchaseand its effect on the decision is accord-ingly slight.The following problem, based on ex-

amples by Savage (2, p. 103) and Thaler(18), further illustrates the effect of em-bedding an option in different accounts.Two versions of this problem were pre-sented to different groups of subjects.One group (N = 93) was given the val-ues that appear in parentheses, and the30 JANUARY 1981

other group (N = 88) the values shownin brackets.

Problem 10: Imagine that you are about topurchase ajacket for ($125) [$15], and a calcu-lator for ($15) [$125]. The calculator salesmaninforms you that the calculator you wish tobuy is on sale for ($10) [$1201 at the otherbranch of the store, located 20 minutes driveaway. Would you make the trip to the otherstore?

The response to the two versions ofproblem 10 were markedly different: 68percent of the respondents were willingto make an extra trip to save $5 on a $15calculator; only 29 percent were willingto exert the same effort when the price ofthe calculator was $125. Evidently therespondents do not frame problem 10 inthe minimal account, which involves on-ly a benefit of $5 and a cost of some in-convenience. Instead, they evaluate thepotential saving in a more inclusive ac-count, which includes the purchase ofthe calculator but not of the jacket. Bythe curvature of v, a discount of $5 has agreater impact when the price of the cal-culator is low than when it is high.A closely related observation has been

reported by Pratt, Wise, and Zeckhauser(21), who found that the variability of theprices at which a given product is sold bydifferent stores is roughly proportional tothe mean price of that product. The samepattern was observed for both frequentlyand infrequently purchased items. Over-all, a ratio of 2: 1 in the mean price of twoproducts is associated with a ratio of1.86:1 in the standard deviation of therespective quoted prices. If the effortthat consumers exert to save each dollaron a purchase, for instance by a phonecall, were independent of price, the dis-persion of quoted prices should be aboutthe same for all products. In contrast,the data of Pratt et al. (21) are consistentwith the hypothesis that consumershardly exert more effort to save $15 on a$150 purchase than to save $5 on a $50purchase (18). Many readers will recog-nize the temporary devaluation ofmoneywhich facilitates extra spending and re-duces the significance of small discountsin the context of a large expenditure,such as buying a house or a car. Thisparadoxical variation in the value ofmoney is incompatible with the standardanalysis of consumer behavior.

Discussion

In this article we have presented a se-ries of demonstrations in which seem-ingly inconsequential changes in the for-mulation of choice problems caused sig-nificant shifts of preference. The in-

consistencies were traced to the inter-action of two sets of factors: variationsin the framing of acts, contingencies, andoutcomes, and the characteristic non-linearities of values and decisionweights. The demonstrated effects arelarge and systematic, although by nomeans universal. They occur when theoutcomes concern the loss of humanlives as well as in choices about money;they are not restricted to hypotheticalquestions and are not eliminated by mon-etary incentives.

Earlier we compared the dependenceof preferences on frames to the depen-dence of perceptual appearance on per-spective. If while traveling in a mountainrange you notice that the apparent rela-tive height of mountain peaks varies withyour vantage point, you will concludethat some impressions of relative heightmust be erroneous, even when you haveno access to the correct answer. Similar-ly, one may discover that the relative at-tractiveness of options varies when thesame decision problem is framed in dif-ferent ways. Such a discovery will nor-mally lead the decision-maker to recon-sider the original preferences, even whenthere is no simple way to resolve the in-consistency. The susceptibility to per-spective effects is of special concern inthe domain of decision-making becauseof the absence of objective standardssuch as the true height of mountains.The metaphor of changing perspective

can be applied to other phenomena ofchoice, in addition to the framing effectswith which we have been concerned here(19). The problem of self-control is natu-rally construed in these terms. The storyof Ulysses' request to be bound to themast of the ship in anticipation of the ir-resistible temptation of the Sirens' call isoften used as a paradigm case (22). Inthis example of precommitment, an ac-tion taken in the present renders inopera-tive an anticipated future preference. Anunusual feature of the problem of inter-temporal conflict is that the agent whoviews a problem from a particular tem-poral perspective is also aware of theconfficting views that future perspectiveswill offer. In most other situations, deci-sion-makers are not normally aware ofthe potential effects of different decisionframes on their preferences.The perspective metaphor highlights

the following aspects of the psychologyof choice. Individuals who face a deci-sion problem and have a definite prefer-ence (i) might have a different preferencein a different framing of the same prob-lem, (ii) are normally unaware of alterna-tive frames and of their potential effectson the relative attractiveness of options,

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(iii) would wish their preferences to beindependent of frame, but (iv) are oftenuncertain how to resolve detected incon-sistencies (23). In some cases (such asproblems 3 and 4 and perhaps problems 8and 9) the advantage of one frame be-comes evident once the competingframes are compared, but in other cases(problems 1 and 2 and problems 6 and 7)it is not obvious which preferencesshould be abandoned.These observations do not imply that

preference reversals, or other errors ofchoice or judgment (24), are necessarilyirrational. Like other intellectual limita-tions, discussed by Simon (25) under theheading of "bounded rationality," thepractice of acting on the most readilyavailable frame can sometimes be justi-fied by reference to the mental effort re-quired to explore alternative frames andavoid potential inconsistencies. How-ever, we propose that the details of thephenomena described in this article arebetter explained by prospect theory andby an analysis of framing than by adhoc appeals to the notion of cost ofthinking.The present work has been concerned

primarily with the descriptive questionof how decisions are made, but the psy-chology of choice is also relevant to thenormative question of how decisionsought to be made. In order to avoid thedifficult problem ofjustifying values, themodern theory of rational choice hasadopted the coherence of specific prefer-ences as the sole criterion of rationality.This approach enjoins the decision-maker to resolve inconsistencies but of-fers no guidance on how to do so. It im-plicitly assumes that the decision-makerwho carefully answers the question"What do I really want?" will eventuallyachieve coherent preferences. However,the susceptibility of preferences to varia-tions of framing raises doubt about thefeasibility and adequacy of the coher-ence criterion.

Consistency is only one aspect of thelay notion of rational behavior. As notedby March (26), the common conceptionof rationality also requires that prefer-ences or utilities for particular outcomesshould be predictive of the experiencesof satisfaction or displeasure associatedwith their occurrence. Thus, a man couldbe judged irrational either because hispreferences are contradictory or becausehis desires and aversions do not reflecthis pleasures and pains. The predictivecriterion of rationality can be applied toresolve inconsistent preferences and toimprove the quality of decisions. A pre-

dictive orientation encourages the deci-sion-maker to focus on future experienceand to ask "What will I feel then?"rather than "What do I want now?" Theformer question, when answered withcare, can be the more useful guide in dif-ficult decisions. In particular, predictiveconsiderations may be applied to selectthe decision frame that best representsthe hedonic expenence of outcomes.

Further complexities arise in the nor-mative analysis because the framing ofan action sometimes affects the actualexperience of its outcomes. For ex-ample, framing outcomes in terms ofoverall wealth or welfare rather than interms of specific gains and losses may at-tenuate one's emotional response to anoccasional loss. Similarly, the experi-ence of a change for the worse may varyif the change is framed as an uncompen-sated loss or as a cost incurred toachieve some benefit. The framing ofacts and outcomes can also reflect theacceptance or rejection of responsibilityfor particular consequences, and the de-liberate manipulation of framing is com-monly used as an instrument of self-control (22). When framing influencesthe experience of consequences, theadoption of a decision frame is an ethi-cally significant act.

References and Notes

1. J. Von Neumann and 0. Morgenstern, Theory ofGames and Economic Behavior (PrincetonUniv. Press, Princeton, N.J., 1947); H. Raiffa,Decision Analysis: Lectures on Choices UnderUncertainty (Addison-Wesley, Reading, Mass.,1968); P. Fishburn, Utility Theory for DecisionMaking (Wiley, New York, 1970).

2. L. J. Savage, The Foundations of Statistics(Wiley, New York, 1954).

3. D. Kahneman and A. Tversky, Econometrica47, 263 (1979).

4. The framing phase includes various editing oper-ations that are applied to simplify prospects, forexample by combining events or outcomes or bydiscarding negligible components (3).

5. Ufp + q = I and eitherx > y > Oorx < y < 0,the equation in the text is replaced byv(y) + r*) [v(x) - v(y)], so that decisionweights are not applied to sure outcomes.

6. P. Fishburn and G. Kochenberger, Decision Sci.10, 503 (1979); D. J. Laughhunn, J. W. Payne,R. Crum, Manage. Sci., in press; J. W. Payne,D. J. Laughhunn, R. Crum, ibid., in press; S. A.Eraker and H. C. Sox, Med. Decision Making,in press. In the last study several hundred clinicpatients made hypothetical choices betweendrug therapies for severe headaches, hyperten-sion, and chest pain. Most patients were riskaverse when the outcomes were described aspositive (for example, reduced pain or increasedlife expectancy) and risk taking when the out-comes were described as negative (increasedpain or reduced life expectancy). No significantdifferences were found between patients whoactually suffered from the ailments describedand patients who did not.

7. E. Galanter and P. Pliner, in Sensation andMeasurement, H. R. Moskowitz et al., Eds.(Reidel, Dordrecht, 1974), pp. 65-76.

8. The extension of the proposed value function tomultiattribute options, with or without risk, de-serves careful analysis. In particular, indif-ference curves between dimensions of loss maybe concave upward, even when the value finc-tions for the separate losses are both convex,because of marked subadditivity between di-mensions.

9. D. Ellsberg, Q. J. Econ. 75, 643 (1961); W. Fell-ner, Probability and Profit-A Study of Eco-nomic Behavior Along Bayesian Lines (Irwin,Homewood, III., 1965).

10. The scaling of v and ir by pair comparisons re-quires a large number of observations. The pro-cedure of pricing gambles is more convenient forscaling purposes, but it is subject to a severe an-choring bias: the ordering of gambles by theircash equivalents diverges systematically fromthe preference order observed in direct com-parisons [S. Lichtenstein and P. Slovic, J. Exp.Psychol. 89, 46 (1971)].

11. A new group of respondents (N = 126) was pre-sented with a modified version of problem 3, inwhich the outcomes were reduced by a factorof 50. The participants were informed that thegambles would actually be played by tossing apair of fair coins, that one participant in tenwould be selected at random to play the gamblesof his or her choice. To ensure a positive returnfor the entire set, a third decision, yielding onlypositive outcomes, was added. These payoffconditions did not alter the pattern of prefer-ences observed in the hypothetical problem: 67percent of respondents chose prospect A and 86percent chose prospect D. The dominated com-bination of A and D was chosen by 60 percent ofrespondents, and only 6 percent favored thedominant combination of B and C.

12. S. Lichtenstein and P. Slovic, J. Exp. Psychol.101, 16 (1973); D. M. Grether and C. R. Plott,Am. Econ. Rev. 69, 623 (1979); I. Lieblich andA. Lieblich, Percept. Mot. Skills 29, 467 (1969);D. M. Grether, Social Science Working PaperNo. 245 (California Institute of Technology,Pasadena, 1979).

13. Other demonstrations of a reluctance to in-tegrate concurrent options have been reported:P. Slovic and S. Lichtenstein, J. Exp. Psychol.78, 646 (1968); J. W. Payne and M. L. Braun-stein, ibid. 87, 13 (1971).

14. M. Allais, Econometrica 21, 503 (1953); K.McCrimmon and S. Larsson, in Expected Util-ity Hypotheses and the Allais Paradox, M. All-ais and 0. Hagan, Eds. (Reidel, Dordrecht,1979).

15. Another group of respondents (N = 205) waspresented with all three problems, in differentorders, without monetary payoffs. The joint fre-quency distribution of choices in problems 5, 6,and 7 was as follows: ACE, 22; ACF, 65; ADE,4; ADF, 20; BCE, 7; BCF, 18; BDE, 17; BDF,52. These data confirm in a within-subject designthe analysis of conditional evaluation proposedin the text. More than 75 percent of respondentsmade compatible choices (AC or BD) in prob-lems 5 and 6, and less than half made compatiblechoices in problems 6 and 7 (CE or DF) or 5 and7 (AE or BF). The elimination ofpayoffs in thesequestions reduced risk aversion but did not sub-stantially alter the effects of certainty andpseudocertainty.

16. For further discussion of rationality in pro-tective action see H. Kunreuther, Disaster In-surance Protection: Public Policy Lessons(Wiley, New York, 1978).

17. W. H. McGlothlin, Am. J. Psychol. 69, 604(1956).

18. R. Thaler,J. Econ. Behav. Organ. 1, 39 (1980).19. B. Fischhoff, P. Slovic, S. Lichtenstein, in Cog-

nitive Processes in Choice and Decision Behav-ior, T. Wallsten, Ed. (Erlbaum, Hillsdale, N.J.,1980).

20. J. C. Hershey and P. J. H. Schoemaker, J. RiskInsur., in press.

21. J. Pratt, A. Wise, R. Zeckhauser, Q. J. Econ.93, 189 (1979).

22. R. H. Strotz, Rev. Econ. Stud. 23, 165 (1955); G.Ainslie, Psychol. Bull. 82, 463 (1975); J. Elster,Ulysses and the Sirens: Studies in Rationalityand Irrationality (Cambridge Univ. Press, Lon-don, 1979); R. Thaler and H. M. Shifrin,J. Polit.Econ., in press.

23. P. Slovic and A. Tversky, Behav. Sci. 19, 368(1974).

24. A. Tversky and D. Kahneman, Science 185,1124 (1974); P. Slovic, B. Fischhoff, S. Lich-tenstein, Annu. Rev. P-sychol. 28, 1 (1977); R.Nisbett and L. Ross, Human Itference: Strate-gies and Shortcomings of Social Judgment(Prentice-tIall, Englewood Cliffs, N.J., 1980);H. Einhorn and R. Hogarth, Annu. Rev. Psy-chol. 32, 53 (1981).

25. H. A. Simon, Q. J. Econ. 69, 99 (1955); Psychol.Rev. 63, 129 (1956).

26. J. March, Bell J. Econ. 9, 587 (1978).27. This work was supported by the Office of Naval

Research under contract N00014-79-C-0077 toStanford University.

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