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Win Some, Lose Some: The Effect ofChronic Losses on Decision Making

Under Risk

LOUIE RIVERS*,** & JOSEPH ARVAI{,{

*School of Environment and Natural Resources, The Ohio State University, Columbus, OH,USA, **National Science Foundation, 4201 Wilson Boulevard, Arlington, VA, USA,

{Environmental Science and Policy Program, Michigan State University, MI, USA, {DecisionResearch, Eugene, OR, USA

ABSTRACT Losses, including those that are chronic in nature, are a fact of life. Theresearch reported here was designed to examine, using a controlled experiment, theeffect of chronic losses in a given contextual domain on subsequent decisions withuncertain outcomes that take place in the same and in unrelated domains. Randomlyselected adult subjects who took part in the experiment were randomly assigned to oneof three treatment groups: One group, chronic losers, was exposed to chronic financiallosses as part of a controlled, multi-round gambling simulation. Groups two and threewere exposed to chronic wins and random outcomes, respectively, as part of the samegambling simulation. Results from the experiment revealed that chronic losses, incontrast to random outcomes and chronic wins, had clear effects on decision making inthe domain where the initial losses were incurred. Subjects who were exposed to thechronic loss induction demonstrated a significantly higher level of risk aversion whencompared with subjects who were exposed to either random outcomes or chronic wins.Subjects exposed to chronic losses also displayed a depressed affective state and atendency to accept less as an outcome of future decisions, and still consider it to be asatisfactory result, when compared to subjects in the two control conditions. Thereappears to be no spillover, however, of a similar degree of risk aversion whenconsidering similar kinds of decisions in unrelated contextual domains. These resultsseem consistent with prospect theory and the theory of learned helplessness, and haveimplications for risk communication and management in a variety of contexts.

KEY WORDS: Chronic loss, learned helplessness, representativeness, affect, decision making

Correspondence Address: Louie Rivers, National Science Foundation, 4201 WilsonBoulevard, Arlington, VA 22230, USA. Email: lrivers@nsf.gov

Journal of Risk ResearchVol. 10, No. 8, 1085–1099, December 2007

1366-9877 Print/1466-4461 Online/07/081085–15 # 2007 Taylor & Francis

DOI: 10.1080/13669870701615172

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Introduction

Each day, people are faced with consequential decisions that arecharacterized by uncertain outcomes. Some of these decisions result intangible gains, or in other words, outcomes that improve the welfare of thedecision maker. In other cases, these decisions result in losses, which maylead to a decline in overall welfare. For many people — e.g., those living inmuch of the developed world — there exists a balance with respect to thedegree to which losses and gains are experienced (e.g., fluctuations betweenfinancial gains and losses achieved as a result of investment decisions over alifetime). In some cases, however, losses occur much more frequently thangains. And in some instances, these losses can be chronic in nature.

In this paper, chronic loss can be defined as the experience of multiple,consecutive losses or negative outcomes in single or in related contextualdomains. Consider, for example, the Prairie View A&M University footballteam, which between 1989 and 1998 endured a losing streak of 80consecutive games. There is also the case of the television actor Susan Lucciwho, between 1978 and 1998 received 18 consecutive Daytime EmmyAward nominations for her work on the program All My Children beforefinally winning her first award in 1999.

Of course, the experience of chronic loss is not isolated to sports andentertainment and its long-term outcomes can be far from trivial.Consecutive losses via pathological gambling routinely results in significantsocial and financial costs for individuals and sometimes, entire communities(Cozzetto and Larocque, 1996; Lesieur, 1979, 1998). Chronic loss is alsocommon in minority and marginalized communities; e.g., the case of AfricanAmerican farmers in the southern United States. In addition to annual lossesresulting from falling crop prices, drought, and growing competition fromlarger factory farms, many African American farmers have also had tocontend with financial losses associated with discrimination by the UnitedStates Department of Agriculture (USDA) in terms of access to much-neededfederal assistance.1 The overall result of these losses has been a 98% declinein the number of farms owned by African Americans (vs. a 60% decline inthe number of farms operated by white owners) since 1920 (Wood andGilbert 2000).

One can presume from their longer-term consequences that chroniclosses have an effect on the decisions made by those experiencing themduring—and immediately following—the period when losses took place(e.g., during their respective losing streaks, decisions about athleticrecruiting at Prairie View and acting jobs by Susan Lucci; more importantly,

1 This situation led to the filing—by a group of African American farmers—of a class actionlawsuit citing discrimination against the USDA; the case was settled in 1999 for $3 billion.Since 1999, however, 90% of those awaiting compensation from the USDA were deniedpayment resulting in the filing of a second lawsuit—also citing discrimination—in which theplaintiffs are seeking $20.5 billion.

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decisions about treatment in the case of pathological gambling or the sale offarmland in the case of African American farmers). Yet, despite growinginterest in choices as they apply to linked or consecutive gambles (e.g.,Croson and Sundali, 2005; Hardy et al., 2006; Ranyard and Charlton,2006), there is a relative paucity of research that explicitly explores the effectof chronic losses on the decision-making process. There are, however,several theoretical propositions that provide clues—albeit somewhat contra-dictory ones—as to the possible effects of chronic losses on people’sdecisions.

Previous studies of the representativeness heuristic have shown, forexample, that people often believe that the probability associated with winsand losses is self-correcting; the related ‘‘gambler’s fallacy’’ (Croson andSundali, 2005; Tversky and Kahneman, 1971) results when peopleerroneously believe in negative autocorrelation of a non-autocorrelatedrandom sequence. Thus, in a simple gamble based on the outcome of coin-flips, a player who observes an outcome of three consecutive heads is likelyto judge the subjective probability of a fourth consecutive heads at less than0.5; in other words, most players of this game will believe (incorrectly) that,probability being self-correcting, chance is more likely to deliver an outcomeof tails on the fourth coin toss. Applied to choices following chronic loss, adecision maker may be willing to take greater risks in the belief that their runof bad luck (resulting in consecutive losses) is about to end.

Similarly, prospect theory (Kahneman and Tversky, 1979) in its mostbasic form suggests that assessments of the potential outcomes in a choiceproblem are a function of two judgmental operations. First, people tend toevaluate potential outcomes based on discernable changes in welfare relativeto an easily accessible reference point rather than final states. Second, in theminds of most, losses loom larger than gains; as Kahneman and Tversky(1979, p. 279) put it, ‘‘the aggravation that one experiences in losing a sumof money appears to be greater than the pleasure associated with gaining thesame amount.’’ As a result, decision makers are more likely to takeadditional risks to recover losses (and return to their initial reference point)as compared to individuals that have experienced the same relative level ofgain.

The theory of learned helplessness (Mikulincer, 1994; Peterson et al.,1993), by contrast, posits that that when individuals are exposed to repeatedaversive events (e.g., a series of difficult-to-solve puzzles), they often learn(or assume) that overcoming them is impossible. When similar aversivecircumstances arise in the future, these individuals will only make a limitedattempt — or no attempt at all — to overcome them. It seems plausible thata form of learned helplessness would apply in the case of decision makingfollowing chronic loss. Following chronic loss in a given context (e.g., aseries of failed gambles), individuals may learn to feel that any futuredecision that they make in this context will not lead to a positive outcome;the subsequent result might be a risk averse decision such as a safer bet or theavoidance of the gamble altogether.

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In light of these conflicting predictions, the exploratory researchreported here was designed to examine, via a controlled experiment, theeffect of chronic losses on subsequent decisions with uncertain outcomesthat take place in the same and in unrelated contextual domains.

Methods

Subjects

430 randomly selected adult subjects were invited to take part in this studythrough the web-enabled panel of Knowledge Networks (KN). These potentialsubjects were drawn from a probability sample of active members in KN’sdatabase. The sampling approach for this project was designed to increase thedemographic similarities between those who completed the experimentalprotocol and U.S. Federal Census benchmarks by factoring demographicgroups’ estimated response rates into the initial sample draw. The probabilitythat members of a given demographic group would take part in the experimentwas factored into the overall sampling frame by over-sampling groups thattend to have a lower response and consistency rate and under-sampling groupsthat tend to have a higher response and consistency rate. The set of completedexperiments came from a subject pool that mirrored that of the U.S. FederalCensus. Of the 430 subjects invited to take part in the research, 332 subjectscompleted the experiment for a response rate of 77%. All elements of theexperiment took place on a secure internet site.

Before the start of the experiment, subjects were randomly assigned to oneof three treatment conditions: (1) those who, at the start of the experiment (seebelow), would be subjected to an experimental manipulation designed toinduce chronic losses (CL; n5127); (2) those who would be subjected to anexperimental manipulation designed to induce chronic wins (CW; n598); and(3) those whowould be subjected to an experimental manipulation designed toinduce completely random outcomes (RO; n5108).

Design

The research was designed around a three-part controlled experiment thatconsisted of: (1) a seven round gambling game (Game A), played withhypothetical dollars (which was unknown to subjects at the time) that wasused to induce either chronic losses, chronic wins, or random outcomes; (2) areal-money lottery (Game B) which was used to test for the effect of chroniclosses on subsequent decisions in the same financial context; and (3) a seriesof related intervening questions that asked subjects to make similar decisionsin a different—environmental—context (Figure 1). On average, the experi-ment took slightly longer than 20 minutes to complete.

The experiment began the same way in each of the three (CL, CW, andRO) treatment conditions. Subjects were told that they were about toparticipate in an experiment that involved two simple games of chance

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during which they would have the opportunity to win real money (to amaximum of $25). Prior to the start of the experiment, subjects wereendowed with $12 in an account containing hypothetical funds (as notedabove).

After receiving their endowment, subjects took part in Game A, whichconsisted of a seven round gambling game designed to induce chronic losses(or chronic wins and random outcomes in the case of the two controlgroups). During each round of the game, subjects were shown a game boardthat was comprised of 10 blank squares (2 squares high by five squareswide). The object of the game as it was described to subjects was simple:During the first six rounds, subjects’ task was to select a desired square. Ifthey selected a winning square, word ‘‘win’’ would appear in the square andbetween $1 and $7 would be added to their account (with the largeramounts appearing randomly in the 7th, final round of the game). If they didnot select a winning square, the word ‘‘lose’’ would appear and between $1and $7 would be subtracted from their account (with the larger amountsonce again appearing randomly in the final round of the game; thus, it wouldbe possible for subjects in the CL treatment to lose their entire $12endowment).

All subjects were told that, on average, other players of the game selecteda winning square 70% of the time. However, the actual probability ofselecting a winning square varied by treatment group. In the CL condition,pwin50.1. For subjects in the In the CW condition, pwin50.9. And in the ROgroup, pwin50.5. The goal here was to ensure that those in the CL groupwould, in all likelihood, experience a long string of consecutive (or chronic)losses. Similarly, this procedure would help to ensure that those in thecontrol groups — the RO and CW treatments — would either experience aseries of consecutive wins (CW) or a pattern of outcomes that were randomin nature (RO).

Figure 1. Experimental design; the gambling and decision tasks were briefly halted at threepoints (S1, S2, and S3) so that subjects could respond to a series of self-rating questions. The

experiment ended with a series of post-test self-ratings (S4).

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To determine the role that the loss (as well as the win and randomoutcome) induction had on subjects’ frame of mind, the game wasinterrupted briefly after the 3rd, 6th and 7th rounds (Figure 1). At thesetimes, subjects were asked a series of time-limited and closed-endedquestions (with responses provided on 7-point Likert scales) aimed atdetermining their affective state in relation to the game. One question wasaimed at subjects’ level of arousal (where 15very frustrated, 45indifferent,and 75very calm) while another measured the level of valence associatedwith the game (where 15very bad, 45indifferent, and 75very good). Inaddition to these questions, subjects were asked to predict the outcome ofthe next round of the game; this question was asked only after the 3rd and6th rounds (where 15I’m sure that I’ll lose, 45I don’t know the outcome,and 75I’m sure that I’ll win).

The second element of the experiment, Game B, was composed of asingle-question lottery based on a modified Becker, DeGroot, Marschak(BDM) procedure (Becker et al., 1964) designed to elicit incentivecompatible responses. This element of the experiment was played for realmoney. Prior to playing the Game B, subjects were reminded of theiroverall performance in Game A (their number of wins and losses, and thesubsequent dollar total) and were told that the maximum amount ofmoney that could have won during the first game was $25. Subjects werenext asked the following question: If you had a chance to play all sevenrounds of the gambling game over again, how much money would youneed to win at the end of the seven rounds in order to be satisfied with thefinal result?

Prior to providing their response, the way in which the lottery workedwas explained to all subjects. First, each subject was asked to indicate theirpreferred dollar amount by keying it into their computer terminal. Then thecomputer program would randomly draw a number between 1 and 25. If thenumber drawn by the computer was equal to or greater than the amountspecified by the subject, then the subject would win their stated dollaramount. If, however, the number drawn by the computer was less than theamount specified by the subject, then the subject would receive nothing.Thus, the higher the dollar amount selected by the subject, the lower theirprobability of winning the lottery.

To help them fully understand the lottery, subjects were provided withtwo written examples of potential lottery outcomes with both a winning anda losing scenario described to each subject. Subjects were also given a singleopportunity to practice the lottery question prior to playing the game for realmoney,2 which was paid to each subject—depending upon their performancein the lottery—at the close of the experiment.

2 Previous research (e.g., Bohm et al., 1997) has shown that the opportunity to practice aBDM-type questions increases the validity of the final result.

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The practice and actual (i.e., real money) rounds of Game B wereseparated by a series of related intervening tasks. Here, subjects were askedto respond to a series of questions detailing gambles in an unrelated context(natural resource management). One question asked subjects to select theirpreferred plan for eradicating non-native species in a National Park. Theother asked for their preferred option for reducing the risk of wildfires instate forests. For the question dealing with invasive species, subjects wereasked to choose from a set of five options that differed in terms of their levelof effectiveness and degree of certainty such that:

N Option 1, which is successful 100% of the time but only results in a 10%reduction in the number of non-native species;

N Option 2, which is successful 80% of the time but only results in a 12.5%reduction;

N Option 3, which is successful 50% of the time but only results in a 20%reduction;

N Option 4, which is successful 20% of the time but only results in a 50%reduction; and

N Option 5, which is successful only 10% of the time but eliminates all non-native species.

The question that dealt with wildfire simply asked subjects to indicate theirpreference for how fire risks should be managed given the uncertainoutcomes associated with many fire management strategies. Subjectsresponded on 7-point Likert scales where 15carry out risk managementonly if outcomes are certain, 45Carry out risk management even ifoutcomes are somewhat uncertain and 75Carry out risk management evenif outcomes are extremely uncertain. The goal of these questions was todetermine if the experience of chronic losses in one domain (i.e., financialgambles) effected choices in another, unrelated domain (i.e., resourcemanagement).

The experiment ended with two closed-ended post-game questions withresponses provided on 7-point Likert scales. These questions asked aboutsubjects’ previous experience with respect to playing games of chance. Oneasked how often subjects played these games in their day-to-day lives (where15never, 45sometimes, and 75often); the other asked subjects how muchthey enjoyed playing these kinds of games (where 15not at all, 45some-what, and 75very much).

Analysis

Data collected during each element of the experiment was analyzed usingeither an analysis of variance (ANOVA) coupled with a Tukey’s post hoctest (to test for significant differences between treatment groups for singlemeasure items) or a repeated-measures ANOVA coupled with a Tukey’s posthoc test (to test for significant differences within treatment groups forrepeated measure items).

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Results

Game A

With respect to Game A, the mean number of rounds won among subjects inthe CL condition (x51.14, se50.08) was significantly lower (repeatedmeasures ANOVA with Tukey’s post-test, p,0.001) than the mean numberof rounds won by subjects in either the RO (x53.53, se50.14) or CW(x56.00, se50.09) treatment groups. Subjects in the CL treatment alsoended Game A with a significantly (p,0.001) lower mean dollar total(x5$6.54, se5$0.46) compared with subjects in the RO (x5$12.13,se5$0.55) and CL (x5$16.95, se5$0.52) conditions.

In terms of subjects’ affective responses to Game A, subjects in the CLtreatment began by feeling relatively indifferent (x53.95, se50.18) but grewmore frustrated as the game progressed through the 6th and 7th rounds. Incontrast, subjects in both the RO (x55.02, se50.22) and CW (x55.46,se50.20) treatments felt relatively calm after the third round but then grewto feel relatively indifferent about it as the game progressed (Figure 2). Thedeclines in all three groups were significant (repeated measures ANOVAwith Tukey’s post-test, p,0.001) as was the difference between the overallratings of frustration (i.e., arousal) in the CL treatment as compared witheither the RO or CW treatments.

Similarly, subjects in each of the CL, RO, and CW treatments feltrelatively indifferent about Game A after the third round; while subjects inthe RO and CW treatments remained indifferent after the 6th and 7throunds, subjects in the CL treatment began to feel increasingly ‘bad’ abouttheir performance in the game (Figure 2). While the observed within-treatment decline was not significant (repeated measures ANOVA withTukey’s post-test, p.0.05), the overall mean difference between the CL

Figure 2. Affect ratings across two dimensions (valence and arousal) elicited from subjects inthe chronic loss (CL), random outcome (RO), and chronic wins (CW) conditions following

rounds 3, 6, and 7 of Game A.

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(x53.33, se50.19) and both the RO (x54.09, se50.22) and CW (x54.46,se50.21) treatments was (repeated measures ANOVA with Tukey’s post-test, p,0.001).

With respect to subjects predictions about their performance during thefuture rounds of Game A (Figure 3), subjects in the CL treatment were generallypessimistic when compared with subjects in both the RO and CW treatmentsfollowing the 3rd and 6th rounds of the game. While within treatmentdifferences were not significant, the mean prediction in the CL treatment(x52.96, se50.13) was significantly lower than the predictions made bysubjects in both the RO (x53.86, se50.15) and CW (x54.03, se50.14)treatments (repeated measures ANOVA with Tukey’s post-test, p,0.001).

Figure 3. Subject’s predicted outcomes following the rounds 3 and 6 of Game A. Responseswere elicited on a 7-point Likert scale where 15I’m sure that I’ll lose, 45I don’t know the

outcome, and 75I’m sure that I’ll win.

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Game B

Lottery prices provided by subjects in the CL condition (x5$9.44,se5$0.55) were significantly lower than those provided by subjects in eitherthe RO (x5$12.31, se5$0.60) or CW (x5$13.81, se5$0.57) treatments(single factor ANOVA with Tukey’s post-test, p,0.001; Figure 4). Furtheranalysis of the data, when it was split based on subjects’ responses to thepost-game questions that dealt with how often and how much they enjoyedplaying games of chance, revealed no significant within-treatment differ-ences (2-sample t-test, p.0.05).

Figure 4. Lottery prices provided during Game B by subjects in the chronic loss (CL), randomoutcome (RO), and chronic wins (CW) conditions.

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Intervening Tasks and Post-test Questions

Significant differences were not observed across the three treatment groups(single factor ANOVA with Tukey’s post-test, p.0.05) with respect to theirresponses to the management questions that comprised the intervening tasksthat dealt with the eradication of an invasive species (xCL52.78, se50.13;xRO53.23, se50.15; xCW53.06, se50.14) in National Parks and thereduction of risk from forest fires (xCL53.35, se50.15; xRO53.95, se50.14;xCW53.78, se50.14) in State Forests.

Discussion

Results from the experiment reported here suggest that chronic loss — incomparison to either random outcomes or chronic wins — has a uniqueeffect on the decision making process. Subjects who were exposed to thechronic loss induction during Game A demonstrated a significantly higherlevel of risk aversion, as displayed by a tendency to accept less money — interms of their lottery selections during Game B — and still consider it to be asatisfactory result than did subjects in either the RO or CW treatment(Figure 4).

This case of lowered expectations displayed by subjects in the CLcondition is analogous to the passive behavior predicted by learnedhelplessness (Peterson et al., 1993). Despite the fact that Game B wasintroduced as a ‘‘new game’’ coupled with the presentation of a real financialincentive play, subjects in the CL condition indicated, through theirdecisions during the modified BDM procedure, that they would be satisfiedwith receiving less money as a potential lottery payout when compared withtheir counterparts in the RO and CW conditions.

Exit interviews with subjects suggest that these choices came as a resultof their experiences of chronic loss during Game A. Subjects in the CLcondition reported feeling a lack of connection between their choices duringGame A and the resulting outcomes. These feelings of lost control overpossible outcomes, which comes as a result of the loss induction, are acentral feature of learned helplessness. In learned helplessness, the perceivedlack of an apparent cause and effect relationship between an individual’sactions and subsequent outcomes heightens one’s sense of a lack of controlsurrounding their ability to do anything that would result in a positiveoutcome. Consequently, subjects who suffer from learned helplessness entera depressed state that effects their subsequent judgments (Peterson et al.,1993). Subjects in our study were no different; during the exit interviews,many reported that these feeling were evoked during Game A and thencarried over to influence their choices during Game B.

Results from the experiment also suggest that these feelings associatedwith learned helplessness affected subjects during Game A. When asked topredict the outcome of future rounds of Game A, people who were subjectedto chronic losses predicted additional losses in subsequent rounds when

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compared to the people who were subjected to random outcomes andchronic wins (Figure 3). Moreover, subjects in the CL treatment werecharacterized by a negative affective state when compared with subjects inthe RO and CW treatments (Figure 2). The development of these negativeaffective states are common features of learned helplessness (Miller andSeligman, 1975; Seligman and Schulman, 1986) that effect, often negatively,the decision making process. Indeed, ‘‘chronic losers’’ noted that theirpredictions about their (poor) performance, and their feelings of frustrationand badness influenced their subsequent decisions, during Game B, to acceptless and still consider it a satisfactory result.

The representativeness heuristic, by contrast, did not seem to play a rolein the decisions (following chronic loss) made by subjects in the CLtreatment. These subjects did not adjust their choices in Game B upward(relative to the choices made by subjects in the RO and CW treatments) asmight have been the case if they believed — according to the gambler’sfallacy — that their run of bad luck was about to end.

It is much more difficult to determine if the results obtained in thisexperiment are in line with predictions that would be made according toprospect theory. It would appear, at first glance, that the choices made inGame B by subjects in the CL condition represent a violation of prospecttheory. Faced with the prospect of repeated failures in Game A, which oftenresulted in the complete erosion of their original $12 endowment, subjects inthe CL condition could be expected to make decisions that reflect a desire totake risks as a means of recovering their losses (Kahneman and Tversky,1979). However, their choices (x5$9.44, se5$0.55) did not reflect the riskseeking behavior in the face of losses that is predicted by prospect theory; infact, the choices made by these subjects was significantly more risk averse —given the structure of Game B — than those made by subjects in the twocontrol conditions (xRO5$12.31, se5$0.60; xCW5$13.81, se5$0.57).Similarly, if CL subjects’ choices in Game B are compared with their finaldollar totals in Game A (x5$6.54, se5$0.46), one could again argue thatanticipated level of risk seeking behavior (to recover losses and return to alevel that is at or very near their initial reference point of $12) is notobserved.

However, examination of the value function that describes prospecttheory also shows that, in the domain of losses, the curve flattens as lossescontinue to accumulate; in other words, the value of accumulated lossesbegins to diminish with each successive loss over time. To this end, previouswork exploring prospect theory (e.g., Tversky and Kahneman, 1992;Wakker and Tversky, 1993) has suggested that individuals who haveexperienced a significant loss in a given context cease to place increasingvalue on subsequent losses. With these large losses, individuals seem todevelop a sense of numbness to additional losses in the same context.Though this previous work in the context of prospect theory did not explorechronic losses, it is conceivable that a similar effect would be observed in this

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case. Based on this interpretation, the results obtained from subjects in theCL condition would tend to support prospect theory.

Of interest, however, is the fact that subjects’ responses in the CWcondition do seem to fit with predictions associated with both prospecttheory and the gambler’s fallacy. In this case, subjects are adjusting theirchoices in Game B downward (x5$13.81, se5$0.57) relative to theirwinnings earned in Game A (x5$16.95, se5$0.52). These choices in GameB reflect a degree of risk aversion consistent with prospect theory (whichpredicts risk aversion in the gains domain of the value function; seeKahneman and Tversky, 1979) and the common belief — consistent with therepresentativeness heuristic — that their run of good luck is about to come toan end. These findings suggest, in part, that decision-making underconditions of chronic loss cannot simply be accounted for by assuming thatthe choices will be reciprocal in nature to those made under conditions ofrepeated gains.

In addition to the theoretical implications of these results, thisexploratory study begins to shed light on some of the unique practicalchallenges associated with decision making under conditions of chronic loss.There are numerous risk policy contexts that involve decision making underconditions where chronic losses are likely. One of the frequently discussed —and poorly understood — examples is that of adaptive management (Arvaiet al., 2006; Gregory et al., 2006); here, the management of risks is regardedas a process of learning over time from policies designed to reduceuncertainty and improve management’s ability to respond to inevitableenvironmental, social, or economic surprises. The implementation ofadaptive management calls for carefully planned and monitored policy‘‘experiments’’, with replication and comparison of management treatmentsat appropriate spatial and temporal scales. Thus, risk management policiesare not viewed as one-time decisions made on the basis of the best existingknowledge. Instead, adaptive management regards policy choices forcomplex environmental problems as part of a carefully planned, iterative,and sequential series of treatments that emphasizes monitoring and learningas the system changes, both in response to external stimuli and in response tothe manager’s actions (Walters, 1986).

But treating individual risk policies as experimental probes that must becompared under a strictly adaptive framework explicitly acknowledges that‘‘failures’’ are to be anticipated. In the context of adaptive management,failures of management treatments are an important observation and evenre-coded as successes insofar as they provide opportunities for learning,reducing uncertainty, and improving future outcomes in a managed system.This double-edged sword of ‘‘successful failures’’ has served as aninstitutional, political, and emotional barrier to the implementation ofadaptive management (Lee, 1993). The research reported here suggests that,beyond these types of barriers, the likelihood of consecutive policy failures(i.e., chronic losses) can lead to significant cognitive and judgmentaldifficulties. A run of consecutive failures within an adaptive management

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framework may result in an unnecessarily high level of risk aversion and alower threshold for what constitutes a satisfactory result, both of which canbe problematic when ambitious risk management action is called for.

There is also the example that opened this paper: chronic losses inminority and marginalized communities. Unlike Susan Lucci who finallywon her Daytime Emmy Award in 1999 and the Prairie View A&MPanthers who snapped their 80 game losing streak — also in 1999 — there isoften no such happy ending in many minority and marginalized commu-nities. In the case of African American farmers, for example, chronic losseshave resulted in a sharp decline in their numbers. In 1920, 1 in every 7farmers was Black; by 1994, less than 1 in every 100 farmers was Black.Unfortunately, there are many other examples. In America, continuallydeclining living conditions in many urban areas and the loss of many socialservices (e.g., affordable housing, access to healthcare, community servicessuch as libraries and recreation centers) has resulted in a tangible sense ofchronic loss among members of minority and marginalized communities.Abroad, nearly one billion people live under conditions that fit the definitionof extreme poverty (i.e., living on less than $1 US dollar per day). Despitethese conditions, losses in these areas continue to mount as people aredeprived of food, clean water, critical foreign aid, and often, a safe place tolive.

How do these mounting losses in a real-world context affect people’sdecisions? If the results from the experiment reported here are used as aguide, chronic losses will — for many — lead to a negative affective state, agrowing sense of pessimism and frustration, risk aversion, and a sense ofsatisfaction with choices that offer only relatively minor improvements inwelfare. It is worth noting, however, that the extent to which these factorswill either worsen or perhaps improve (i.e., risk aversion isn’t a bad thingwhen one risks losing everything) people’s long-term quality of life in thereal world is largely unknown. As is often the case with exploratory studiesof this type, more research — both experimental and observational — isnecessary. But alongside this research, we also believe that risk practitionersand policy makers who work with real people and real problems mustrecognize that chronic loss will likely bring with it a unique set of challengesand, as a result, support in the form of specialized risk communication anddecision aiding strategies will likely be necessary.

Acknowledgments

This research was supported by the National Science Foundation undergrant numbers SES-0519551 and SES-0350777. Any opinions, findings, andconclusions or recommendations expressed in this publication are those ofthe authors and do not necessarily reflect the views of the sponsors. Theresearch reported here benefited from the comments and insights of MelissaFinucane, Robyn Wilson, Hal Arkes, and Paul Slovic.

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