when opportunity matters: comparing the risk-taking attitudes of prisoners and recently released...

DOI: 10.1111/risa.12042

When Opportunity Matters: Comparing the Risk-TakingAttitudes of Prisoners and Recently Released Ex-Prisoners

Jonathan J. Rolison,1,∗ Yaniv Hanoch,2 and Michaela Gummerum2

Risk-taking tendencies and environmental opportunities to commit crime are two key fea-tures in understanding criminal behavior. Upon release from prison, ex-prisoners have amuch greater opportunity to engage in risky activity and to commit criminal acts. We hypoth-esized that ex-prisoners would exhibit greater risk-taking tendencies compared to prisonerswho have fewer opportunities to engage in risky activity and who are monitored constantly byprison authorities. Using cumulative prospect theory to compare the risky choices of prison-ers and ex-prisoners our study revealed that ex-prisoners who were within 16 weeks of theirprison release made riskier choices than prisoners. Our data indicate that previous studiescomparing prisoners behind bars with nonoffenders may have underestimated the risk-takingtendencies of offenders. The present findings emphasize the central role played by risk-takingattitudes in criminal offending and highlight a need to examine offenders after release fromprison.

KEY WORDS: Criminal offenders; ex-prisoners; prisoners; prison system; prospect theory; risk taking;risky choice

The financial burden of crime is an esti-mated $1 trillion in the United States each year.(1)

Costs of prosecution, prison services, and loss toproductivity—for offenders and their victims—areamong the financial costs borne by society as a directresult of crime.(1) Concern for financial cost and pub-lic safety is exacerbated by high reconviction rates inthe United States and in Britain, where 58% of ex-prisoners are reconvicted within two years of theirprison release.(2) During this period, reoffenders willeach have acquired on average three further convic-tions, costing an estimated £11 million in Britain eachyear as a result of criminal justice costs alone.(2)

What are the causes of criminal behavior and re-offending? Although criminal activity likely dependson a multitude of factors (e.g., socioeconomic and

1School of Psychology, Queen’s University Belfast, Belfast, UK.2School of Psychology, University of Plymouth, Plymouth, UK.∗Address correspondence to Jonathan J. Rolison, School of Psy-chology, Queen’s University Belfast, Belfast, UK; tel: +44 (0)289097 5653; [email protected].

personality characteristics(3)), including unemploy-ment, drug and alcohol misuse, financial problems,and debt,(2,4) two factors central to crime are risk-taking tendencies(5) and environmental opportuni-ties. Gottfredson and Hirschi’s(6) influential generaltheory of crime proposes that offenders differ fromnonoffenders in their willingness to take risks andthat the opportunity to commit crime is a key factorin criminal behavior. In this article we examine therisk-taking attitudes of prisoners in comparison withex-prisoners, who have a much greater opportunityto engage in risky activity and to commit crime uponrelease from prison.

This article is organized as follows. Section 1summarizes the literature on risk taking in the con-text of offending. Section 1.1 introduces the riskychoice framework to be used for studying the risk-taking attitudes of offenders in the reported study.Section 2 provides methodological details of thestudy. Section 3 presents the results of the study.We found that ex-prisoners recently released from

1 0272-4332/13/0100-0001$22.00/1 C© 2013 Society for Risk Analysis

2 Rolison, Hanoch, and Gummerum

prison are more likely to take risks than prisonerswhen risky choice outcomes involve potential lossesor potential gains, except when a potential gain isunlikely. Moreover, it is found that ex-prisoners’ in-creased risk taking is due in part to a reduced sensi-tivity to the magnitude rather than to the likelihoodof potential losses. Sections 4 and 5 discuss the resultsof the study and highlight the need to study offend-ers both inside and outside of the prison system foran accurate account of offenders’ risk-taking behav-ior. Our data also suggest that increasing the likeli-hood or threat of being caught may be a more effec-tive crime deterrent than threats of long-term prisonsentences.

1. INTRODUCTION

Gottfredson and Hirschi’s(6) general theory ofcrime proposes that offenders have poor self-control,which they describe is linked to increased risk-taking tendencies. A wealth of research has linkedcrime to impulsivity(7–9)—a psychological constructthat characterizes tendencies to act on impulse with-out deliberation.(10) Research also points to a pos-sible biological basis of offenders’ impulsive ten-dencies. Exposure to androgen hormones duringthe prenatal period of fetal development in thewomb, indicated by the 2D:4D ratio measurement—the ratio of the second finger length over thefourth ring finger length—correlates with impulsivityamong offenders(9,11) and identifies offenders amongnonoffenders.(9)

Impulsive tendencies may be rooted in offend-ers’ perceptions of their short- and long-term timehorizons. It has long been proposed in theories ofcrime that offenders have difficulties with delay-ing gratification and with considering long-term out-comes and consequences.(12) This is consistent withexperimental evidence that offenders discount de-layed rewards more steeply than nonoffenders whenmaking choices between immediate and delayedrewards.(13) Accordingly, rehabilitation programs ad-ministered by prison and probation services focus ondeveloping offenders’ long-term perspective.(8) The“Reasoning and Rehabilitation” program, for exam-ple, is a cognitive-behavioral treatment that has ledto significant reductions in reoffending in Britain, theUnited States, and in Canada,(14) by training cogni-tive skills of self-control, cognitive style, and criticalreasoning.

In combination with risk-taking attitudes, theopportunity to commit criminal acts is a central com-

ponent of Gottfredson and Hirschi’s(6) general the-ory of crime, and is likely a major cause of crim-inal activity. LaGrange and Silverman(15) reportedthat adolescents who scored low on measures of self-control were more likely to engage in criminal ac-tivity if they also reported spending less time un-der adult or parental supervision. Among a sampleof mainly adult offenders in the U.S. criminal jus-tice system, Longshore(16) found that offenders whoreported having encountered a greater number ofcriminal opportunities in the previous six monthsalso reported having committed a greater number ofcriminal acts within the same period.

There is good reason to believe that being in-side the prison also affects risk-taking attitudes andbehavior. Offenders have less opportunity to engagein risky behavior inside the prison, they have fewerfinancial resources, and their behavior is constantlymonitored by prison authorities, all of which may de-ter and dampen the risk-taking attitudes of prisoners.Ex-prisoners, on the other hand, have far greater op-portunity to engage in risky activity and to commitcrime, and this may influence risk-taking attitudesand behavior. The life circumstances and lifestylechoices of ex-prisoners may also differ from those ofprisoners, and are found to be important proximatecauses of recidivism among ex-prisoners.(4) Thus, therisk-taking tendencies of prisoners may not relate tothose of ex-prisoners upon release from prison. Yetsurprisingly, little is known about whether the risk-taking attitudes of prisoners differ from offenderswho have been released from prison, as most previ-ous studies have addressed the risk-taking attitudesof prisoners only or have compared risk taking inprisoners and nonoffenders.(7,9,11)

Release from prison affords greater opportunityto engage in risky activity and to commit crime, whichimplies that recently released ex-prisoners may bemore willing to engage in risk taking than prisoners.This would have clear and important consequencesfor criminal justice and rehabilitation, as previousemphasis on prisoner samples may have underesti-mated offenders’ risk-taking behavior. In this arti-cle we address this important issue directly by com-paring the risk-taking attitudes of prisoners in prisonwith those of ex-prisoners who have recently been re-leased from prison.

1.1. The Risky Choice Framework

Within Gottfredson and Hirschi’s(6) general the-ory of crime, risk-taking attitudes are treated as

When Opportunity Matters 3

a single dimension, described in terms of reducedself-control. Studies within the risky choice frame-work have revealed that offenders differ from nonof-fenders in their evaluations of individual outcomes.Although offenders take more risks than nonoffend-ers overall, this is especially the case when eval-uating potential losses.(17–19) In a mock economictask that penalized risk-taking behavior, Block andGerety(18) found that prisoners were deterred moreby the likelihood of receiving a penalty than by itsseverity, whereas nonoffenders were deterred moreby its severity than its likelihood. The involvementof such risk-taking tendencies in the root causes ofcriminal activity is a complex issue. In a 24-year lon-gitudinal study, Farrington(20) found that risk-takingtendencies among children (8–10 years), includingimpulsivity, were among a multitude of factors (e.g.,poor parenting style, poverty, etc.) that predicteddelinquency at later ages. Findings within the riskychoice framework, however, have important impli-cations for policy making and crime deterrence. AsBlock and Gerety(18) conclude, increasing the like-lihood of being caught may be a more effective de-terrent than threats of long-term prison sentences,where prisoners are shown to be more sensitive tothe likelihood of negative outcomes than to theirmagnitude.(17–19)

Recent evidence suggests that environmentalopportunity plays a major role in people’s riskychoice behavior.(21,22) Inesi(21) has found that self-perceptions of power over the environment, such aspromotion to a position of authority, reduce people’ssensitivity to the value of anticipated losses, causingthem to take greater risks. Participants in Inesi’s(21)

study perceived potential negative outcomes as lesssevere when they had a sense of power over theirenvironment. These findings provide testable pre-dictions for differences in risk-taking tendencies be-tween prisoners and ex-offenders, as there is goodreason to believe that prisoners’ risk-taking tenden-cies might alter once they leave the prison envi-ronment and encounter greater opportunities andfreedom to engage in risky behavior. The findingsof Inesi(21) suggest that ex-prisoners might exhibitstronger risk-taking tendencies than the prisoners ofprevious studies by discounting the severity of nega-tive outcomes. This would suggest that ex-prisonersmay be even less deterred by threats of increasinglevels of punishment than previous studies of prison-ers imply.

Presently, we examined the risk-taking atti-tudes of prisoners and ex-prisoners within the risky

choice framework using cumulative prospect theory(CPT).(23) Although there are recent alternatives toCPT,(24) including risky choice models that empha-size information processing,(25) CPT is among themost influential methods for examining risky choicebehavior. A key advantage of CPT is that sensitivi-ties to the magnitudes and probabilities of outcomesare treated separately for potential gains and poten-tial losses,(26,27) such that individual differences inpeople’s evaluations of outcomes can be character-ized by a tradeoff between potential gains and losses.According to CPT, when people evaluate riskychoice options, the magnitudes and probabilities ofoutcomes undergo subjective transformations thatdepend on the individual’s perceptions of potentialoutcomes, rather than the objective amounts. Thisenables us to capture differences in the risk-takingattitudes of prisoners and ex-prisoners when evaluat-ing risky choice options.

In sum, in this study we compare the risk-takingattitudes of prisoners with those of recently releasedex-prisoners who have greater opportunity to engagein risky activity and to commit crime. We hypothe-sized (1) that ex-prisoners would exhibit riskier atti-tudes compared to prisoners, and (2) that the riskierattitudes of ex-prisoners would result in part froma reduced sensitivity to the magnitude of potentiallosses.

2. METHOD

2.1. Participants

We compared 45 male ex-prisoners (M = 39.0years, SD = 8.5, youngest = 21, oldest = 58) witha previously studied(17) sample of 51 male prison-ers of similar age (M = 41.8 years, SD = 13.6,youngest = 21, oldest = 72), who were currently serv-ing a prison sentence in a medium security prisonin the same region of the United Kingdom that theex-prisoners were recruited. The ex-prisoners werewithin 16 weeks from the date of their prison release.Although we do not have data on prisoners’ and ex-prisoners’ ethnicity, we do know that the prison pop-ulation and the ex-prisoners population from whichour samples were drawn is mainly white British (over90%).

Twenty-six (51.0%) of the prisoners were sen-tenced for an offense against another person (mur-der, violence, or sexual assault) and a similar pro-portion of ex-prisoners had been sentenced forone of these offenses in the past (21; 46.7%).


Twenty-three (45.1%) prisoners, compared to 16(35.6%) ex-prisoners, were sentenced or had beensentenced in the past for an offense that was notagainst another person (drugs or burglary). Prisonersand ex-prisoners differed regarding some offenses.Ten (19.6%) of the prisoners and none of the ex-prisoners were sentenced for sexual assault. Con-versely, two (3.9%) prisoners were sentenced for adrug-related crime, compared to 25 (55.6%; groupdifference, χ2(1) = 31.53, p < 0.001) ex-prisonerswho identified drug-related crime as a previousconviction.

The ex-prisoners had more previous convictionsthan prisoners. Thirty-one (60.8%) prisoners had be-tween one and five previous convictions (includingtheir current conviction), compared to nine (20.0%;group difference, χ2(1) = 16.36, p < 0.001) ex-prisoners. Five (9.8%) prisoners and six (13.3%) ex-prisoners had between six and 10, and 30 (66.7%) ex-prisoners, compared to 15 (29.4%; group difference,χ2(1) = 13.32, p < 0.001) prisoners had more than 10previous convictions.

2.2. Materials

We followed the same procedure used by Pachuret al.,(17) and presented both groups of participants atotal of 115 gambles divided into five sets. Two sets ofgambles provided a choice between a sure gain and alottery gain of £100 with either a low probability (p =0.05) for one set or a high probability (p = 0.95) forthe other set, and two further sets provided a choicebetween a sure loss and a lottery loss with the sameprobability. Thus, the gain and loss sets were iden-tical except outcomes referred either to gains or tolosses. Within the gain and loss sets, the amount of-fered by the sure option incremented on each of 21gambles from a gain (or loss) of £0 to £100 in steps of£5. In a fifth set of gambles (the mixed-prospects set),participants were asked whether they would choose alottery that offered a 50% chance of losing £100 anda 50% of gaining an amount, x, that incremented oneach of 31 gambles from £0 to £600 in steps of £20.Participants were presented the five sets of gamblesin a counterbalanced order.

2.3. Procedure

The study protocol was approved by both theUniversity of Plymouth and the participating insti-tutions. The ex-prisoners were contacted throughan outreach organization in the Southwest of theUnited Kingdom, and were tested in the institution’s

premises. Prisoners were approached in prison andasked whether they would participate in the study.Both groups of participants were informed that tak-ing part in the study was voluntary and anonymous,that refusal to participate would not be associatedwith any negative results, and that the data would beused for research purposes only. Both prisoners andex-prisoners were tested in very similar conditions:they were tested individually, in a designated roomwhere they were provided with both oral and writ-ten instructions about the task, assured of privacy,and given no time limit. At all times, a research as-sistant (who was familiar to participants) was presentin the room to answer any potential questions. Ex-prisoners were paid a flat rate of £8 (in vouchers) fortheir participation, regardless of their performance.Due to prison regulations, we were not allowed topay prisoners.

Participants’ risky choice behavior was analyzedusing CPT. Following the procedure used by Pachuret al.,(17) we fitted CPT to the data of all partici-pants simultaneously, including all five gambling sets.The CPT model is described in detail in the Ap-pendix. For each gamble we calculated the proba-bility, L, that the lottery would be chosen over thesure outcome, S (or nothing, in the case of the mixedprospects set), such that:

P (L, S) = eφ·V(L)

eφ·V(L) + eφ·V(S), (1)

where V in Equation (1) is the subjective expectedvalue of each option. The consistency parameter,φ, in Equation (1) measures choice consistency andcould range in value from 0 to 10.(28–30) Switchingback and forth between choice options would yielda higher value for the consistency parameter. Fol-lowing the suggestions of others,(17,29) we used G2 asour measure of model fit, where smaller values of G2

indicate better fit. The best-fitting parameter valuesthat described participants’ choices were those thatminimized G2. To examine differences between pris-oners and ex-prisoners on the CPT parameters, wefitted the model to participants’ choices separatelyfor the prisoner and ex-prisoner samples, generat-ing a set of best-fitting parameter values for prison-ers and a set of best-fitting parameter values for ex-prisoners.

3. RESULTS

We first calculated participants’ certainty equiv-alents (CEs) for the gain and loss sets. The CE refers


to the amount of the sure option at which a partici-pant’s preference for the sure option is equivalent tothat of the lottery. Thus, the CE for a set of choicesis the mid-point between gambles where a participantswitches from preferring the sure option to preferringthe lottery, or vice versa. For this reason, we excludedex-prisoners (low probability gain set, N = 3, 6.7%;high probability gain set, N = 10, 22.2%; low proba-bility loss set, N = 8, 17.8%; high probability loss set,N = 13, 28.9%) and prisoners (low probability gainset, N = 5, 9.8%; high probability gain set, N = 8,15.7%; low probability loss set, N = 10, 19.6%; highprobability loss set, N = 11, 21.6%) who switched re-peatedly between options, but include all data in ourmodel analyses to follow. We compared participants’individual CEs for each set with the expected value(EV) of the lottery for that set. The EV is the amountof the sure option at which the lottery has the samevalue. Thus,

relative RP = EV − CE|EV| , (2)

where the relative risk premium (RP) represents aparticipant’s risk attitude relative to the EV of thelottery. A positive value for the relative RP indicatesrisk aversion, whereas a negative value indicates risk-seeking behavior.

Provided in Fig. 1 are the mean group relativeRPs for each of the gain and loss sets. Both groups ofparticipants displayed the four-fold pattern observedfor choices made by nonoffenders.(23) That is, bothgroups were risk seeking for the lowprobability gainand highprobability loss sets, and risk averse for thehighprobability gain and lowprobability loss sets (seeFig. 1). We hypothesized that ex-prisoners would ex-hibit riskier attitudes compared to prisoners. For therelative RPs there was a trend toward more risk-seeking behavior in the highprobability gain and losssets for ex-prisoners compared to prisoners, but morerisk-averse behavior in the lowprobability gain andloss sets. However, the group comparisons for thelowprobability (gain set, t(86) = 0.72, p = 0.474; lossset, t(76) = 1.19, p = 0.238) and highprobability (gainset, t(76) = 1.48, p = 0.144; loss set, t(70) = 1.14, p =0.259) sets were not individually significant.

We then examined participants’ risk attitudes forthe mixed prospects set, which combined gains andlosses. We excluded from the present analysis thoseprisoners (N = 21; 41.2%) and ex-prisoners (N = 13;28.9%) who switched repeatedly between choice op-tions, but as with our gain and loss sets, the data ofall participants were included in our model analysis

Table I. Best-Fitting Cumulative Prospect Theory (CPT)Parameter Values for Prisoners and Ex-Prisoners

Group α β γ δ λ φ Mean G2

Prisoners 0.651 0.583 0.484 0.549 2.834 0.169 129.337Ex-prisoners 0.504 0.435 0.688 0.525 2.022 0.338 124.173�G2 24.064 13.440 34.454 0.435 9.678 20.244p <0.001 <0.001 0.001 0.510 0.002 <0.001

Note: α, β, γ , δ, λ, and φ are the CPT parameters fitted to partic-ipants’ choices separately for prisoners and ex-prisoners. α and β

describe sensitivity to changes in payoff for gains and for losses, re-spectively. γ and δ describe sensitivity to changes in the probabilityof gains and losses, respectively. λ indicates the degree of loss aver-sion. φ measures the degree of choice consistency. G2 is the overallmodel fit. G2 is the reduction in model fit when the respective pa-rameter is held constant across prisoners and ex-prisoners. p is thesignificance test for the G2 computed by the chi-square statistic.

to follow. For each participant we determined thepoint at which he found the lottery equally attrac-tive to nothing. The lottery provided a 50:50 chanceof gaining an amount that incremented with eachgamble and losing £100, such that the lottery wasequivalent to nothing when the gain amount equaled£100. Prisoners (M = 191.0, SD = 130.6; one-samplet-test, t(29) = 3.82, p = 0.001) but not ex-prisoners(M = 115.9, SD = 126.7; one-sample t-test, t(31) =0.71, p = 0.482) indicated an amount that was above£100, exhibiting loss aversion for mixed prospects,and a mean group comparison confirmed that pris-oners were significantly more loss averse than the ex-prisoners (t(60) = 2.30, p = 0.025).

Provided in Table I are the best-fitting CPT pa-rameter values for prisoners and ex-prisoners. Themean model fit, G2, averaged across participants wasslighter better for ex-prisoners compared to prison-ers, indicated by a lower G2 value for ex-prisoners(see Table I). To test for significant differences be-tween prisoners and ex-prisoners on each parame-ter, we again fitted the model separately to the dataof prisoners and ex-prisoners, but this time holdingone of the parameters constant. By fixing a parame-ter across prisoners and ex-prisoners, any reductionin model fit would indicate that the fixed parametercontributes to differences between prisoners and ex-prisoners. The chi-square statistic was used to test forsignificant reductions in model fit, and the results areprovided in Table I.

As shown in Table I, the risk attitudes ofex-prisoners differed significantly from those ofprisoners in a number of respects. We hypothe-sized that compared to prisoners ex-prisoners would


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Fig. 1. Mean group relative risk premiums(RP) displayed separately for prisoners andex-prisoners for each of the gain and losssets. Error bars represent 1 standard erroraround the mean.

exhibit a reduced sensitivity to the magnitude of po-tential losses. As expected, ex-prisoners were lesssensitive than prisoners to differences in payoff forpotential losses (β parameter), and this was also thecase for potential gains (α parameter; see Table I).As an indication of what this means for their riskchoices, Fig. 2 plots the predicted probability thatthe lottery is chosen based on the best-fitting CPTparameter values, separately for prisoners and ex-prisoners. Observe that the ex-prisoners were morelikely to choose a lottery loss over a sure loss, re-gardless of its probability (see Fig. 2). However, ex-prisoners were instead more sensitive than prisonersto differences in the probability of gains (γ parame-ter). This means that the prisoners discriminated lessbetween low and high probabilities of gain. As a re-sult, the ex-prisoners were more likely to choose alottery gain over a sure amount only when the lot-tery gain was highly likely, and were instead lesslikely than prisoners to choose a lottery gain when itwas unlikely. Moreover, the ex-prisoners were signif-icantly less loss averse than prisoners (λ parameter).Prisoners were far less likely than the ex-prisoners tochoose the mixed prospects lottery over nothing, in-dicating greater loss aversion (see bottom panel ofFig. 2). The ex-prisoners were also more consistentthan prisoners in their choices (φ parameter), sug-gesting that the ex-prisoners were more determinis-tic in their choices and less likely to switch back andforth between gamble options. There were no signif-icant differences between prisoners and ex-prisonersin their sensitivity to differences in the probability oflosses (δ parameter).

The prisoner and ex-prisoner samples differedin their number of previous convictions, where ex-

prisoners had more previous convictions. Furtheranalysis indicated that offenders with more previ-ous convictions were less consistent in their riskychoices (φ parameter), less sensitive to changes in theprobability of losses (δ parameter), but more sen-sitive to changes in the magnitude of losses (β pa-rameter).3 With a reduced sensitivity to changes in

3We examined whether the number of previous convictions ofparticipants influenced our comparisons between prisoners andex-prisoners by testing for changes in the best-fitting CPT pa-rameter values when individuals with between one and five pre-vious convictions were removed from our entire data set, andin a separate analysis, when individuals with more than 10 pre-vious convictions were removed. As in our main analysis, weheld one of the parameters constant for each comparison, suchthat a reduction in model fit would indicate that offenders’number of previous convictions influenced the fixed parame-ter. This analysis revealed that the best-fitting parameter val-ues for the entire data set (α = 0.564; β = 0.497; γ = 0.565;δ = 0.541; λ = 2.400; φ = 0.250) were comparable when in-dividuals with between one and five previous convictions wereremoved (α = 0.570; β = 0.544; γ = 0.582; δ = 0.469; λ =2.195; φ = 0.235). The only significant effect was to reduceparticipants’ sensitivity to the probability of losses (δ, G2 =14.061, p < 0.001), with no significant effects on the remainingfive parameters (α, G2 = 0.100, p = 0.752; β, G2 = 3.434,p = 0.064; γ , G2 = 0.562, p = 0.453; λ, G2 = 1.423, p = 0.233;φ, G2 = 0.405, p = 0.524). When individuals with more than 10previous convictions were removed, response consistency (φ =0.311; G2 = 4.427, p = 0.035) and sensitivity to the probabilityof losses (δ = 0.657; G2 = 12.079, p = 0.001) increased, whereassensitivity to the magnitude of losses decreased (β = 0.435; G2

= 5.233, p = 0.022). There were no significant differences for eachof the remaining parameters (α = 0.537, G2 = 1.761, p = 0.184;γ = 0.528, G2 = 3.679, p = 0.055; λ = 2.580, G2 = 1.138,p = 0.286). This suggests that offenders with more previousconvictions were less consistent (φ parameter), less sensitive tochanges in the probability of losses (δ parameter), and more sen-sitive to changes in the magnitude of losses (β parameter).


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the probability of losses, this would suggest that sea-soned criminals take more risks than offenders withfewer previous convictions as the likelihood of a neg-ative outcome (e.g., getting caught for an offense)increases. But this pattern of results is inconsistentwith our observations of ex-prisoners and prisoners,for which we found no differences in their sensitivityto the probability of losses. Moreover, ex-prisonerswere found to be less sensitive to changes in the mag-nitude of losses and more consistent in their riskychoices than prisoners, even though they had moreprevious convictions.

4. DISCUSSION

Two key factors in Gottfredson and Hirschi’s(6)

general theory of crime are risk-taking tendenciesand environmental opportunities. Upon release fromprison, ex-prisoners have a much greater opportunityto engage in risky activity and to commit criminalacts. Prisoners, by contrast, have fewer opportunitiesto engage in risky activity and are monitored con-stantly by prison authorities. In this study we com-pared the risk-taking attitudes of prisoners with ex-prisoners who were recently released from prison.We found that ex-prisoners made riskier choicesthan prisoners for both potential losses and potentialgains, except when a potential gain was unlikely.

To our knowledge, this is the first study to useCPT to capture the cognitive processes that under-lie the risky choices of prisoners behind bars andex-prisoners outside the prison environment. In linewith Gottfredson and Hirschi’s(6) assumption, ex-prisoners were more risk seeking than prisoners inall cases when outcomes involved a potential loss,and made less risky choices than prisoners only whena potential gain was unlikely (see Fig. 2). As a re-sult, previous studies comparing prisoners behindbars with nonoffenders(17–19) may actually have un-derestimated the risk-taking attitudes of offenders.For example, the mixed prospects set in this studyoffered participants a lottery with a 50:50 chance oflosing £100 and gaining an amount that incrementedon each gamble. When the gain amount equaled £100the value of the lottery was equal to nothing, at whichpoint 42% of ex-prisoners compared to 18% of pris-oners were willing to gamble on the lottery and riska loss. These findings have important consequencesfor criminal justice and rehabilitation as they empha-size the central role played by risk-taking attitudes incriminal offending and highlight a need to examineoffenders after release from prison.

Ex-prisoners have greater opportunity to takerisks than prisoners behind bars, and our findingssuggest that the environment may play an importantrole in offenders’ risky choice behavior. Risk-takingdifferences between prisoners and ex-prisoners mayreflect changes in prisoners’ attitudes toward riskupon release from prison (e.g., their attitudes andexpectations about the future), or changes in situ-ational factors (e.g., opportunities to interact withothers and to engage in criminal activity), that re-sult from prison release. The cross-sectional natureof our study limits our ability to examine the causalpathways through which release from prison mightimpact on offenders’ risk-taking behavior.4 Our find-ings do, however, fit well with recent accounts ofrisky choice. Inesi(21,22) reports that self-perceptionsof power have important consequences for our atti-tudes toward risk, and that these can be influencedby environmental opportunities and situational fac-tors, such as promotion to a position of authority.According to Inesi,(21,22) a sense of power over theenvironment reduces people’s sensitivity to the valueof potential losses, leading negative outcomes to beperceived as less severe. Consistent with this account,we found that ex-prisoners, who had recently beenreleased from prison, had a reduced sensitivity tochanges in the magnitude of negative outcomes, lead-ing them to take greater risks than prisoners whenoutcomes were potential losses.

Although opportunity and the sense of powerthat release from prison provides may be involvedin risk-taking differences between prisoners and ex-prisoners, we can expect that numerous other causes(e.g., lifestyle choices and life circumstances)(4) arealso involved. Our findings indicate that ex-prisonersare likely to differ from prisoners in their risk-takingbehavior, and that conclusions about the risk-takingbehavior of offenders should not be based on pris-oner samples alone. In this study we captured of-fenders’ sensitivity to the probabilities and magni-tudes of potential outcomes by asking them to makechoices between monetary sums that could be wonor lost. We found that ex-prisoners were more risktaking than prisoners, in part, because of a reducedsensitivity to the magnitude of losses. Althoughpresently we studied choices with monetary sums, wecan expect that offenders’ sensitivity to outcomes will

4An alternative approach would have been to employ a longitu-dinal design. A longitudinal approach would, however, be verychallenging. The U.K. prison and probation services do not haveany mechanism to follow prisoners once they are released fromprison.


translate to other potential gains and losses. For ex-ample, our results indicate that threats of being pros-ecuted for criminal acts (or increasing the likelihoodof being caught) may be a more effective crime de-terrent than threats of long-term prison sentences.

This study has a number of limitations. Our pris-oner and ex-prisoner samples differed regarding thenature of their convictions. Some of the prisoners,and none of the ex-prisoners, had been convicted forassault, and the proportion of offenders convicted fora drug-related crime was higher among ex-prisonersthan prisoners. Variation in the nature of offenders’convictions may have influenced our results, and fur-ther research would benefit by examining how risk-taking attitudes of offenders differ depending on thenature of their crimes. For examples, sexual offend-ers, and offenders convicted for violent offenses, pro-cess some types of information (e.g., expressions ofemotion) differently from other offenders.(31) Long-term drug use is known to increase impulsivity andreduce self-control,(32) with potential impacts of risk-taking behaviors. Finally, the ex-prisoners in ourstudy were each compensated with £8 in vouchers fortheir participation, whereas due to prison regulationsthe prisoners participated on a purely voluntary ba-sis. It is possible that this unequal compensation in-fluenced the risky monetary choices of prisoners andex-prisoners.

5. CONCLUSIONS

Findings of this study indicate that ex-prisoners,upon recent release from prison, make riskier choicesthan prisoners. Previous studies comparing prisonersbehind bars with nonoffenders may have underesti-mated the risk-taking attitudes of offenders, whichfurther emphasizes the central role played by risk-taking attitudes in criminal behavior. Our modelinganalysis of offenders’ risky choices using CPT re-vealed that ex-prisoners were more risk taking thanprisoners in part because of a reduced sensitivity tothe magnitude of potential losses. This finding indi-cates that increasing the likelihood or threats of be-ing caught and prosecuted may be a more effectivecrime deterrent than policies that emphasize punish-ment severity, such as threats of long-term prisonsentences. The cross-sectional nature of our studylimited our ability to examine causal mechanismsthrough which release from prison might alter anindividual’s risk-taking behavior. A challenging en-deavor for future research will be to monitor offend-ers’ risk-taking attitudes (and other cognitive factors)

both inside and outside the prison system upon re-lease from prison.

ACKNOWLEDGMENTS

We would like to thank Taja Anderson, RuthSheldon, Kelly George, Michelle Hack, and AlannahHolloway for help in data collection, and all partici-pants and the prison authorities for making this studypossible.

APPENDIX

According to cumulative prospect theory(CPT),(23) the subjective value of an outcome, x, isdetermined by the value function, such that:

v (x) ={

xα, if x ≥ 0

−λ(−x)β, if x < 0,(A.1)

where α and β are decision weights that describe theindividual’s sensitivity to changes in payoff for gainsand for losses, respectively. In our model analysis, thegain and loss parameters were constrained to valuesgreater than 0 and less than 1, such that they pro-duce a concave value function for gains, and a con-vex value function for losses. The loss aversion pa-rameter, λ, applied to the value function for losses, isconstrained to a value greater than 0 and no greaterthan 10. A value greater than 1 for the λ parameterindicates loss aversion.

The probability, px, of a gain or loss amount, x, istransformed by the weighting functions, γ and δ, forgains and losses, respectively:

W+ (px) = pγ /(pγ + (1 − p)γ )1/γ ; (A.2)

W− (px) = pδ/(pδ + (1 − p)δ)1/δ. (A.3)

The weighting functions, γ and δ, describe theindividual’s sensitivity to changes in the probabilityof gains and losses, and are both constrained to beabove 0 and less than 1. When the weighting func-tions have values smaller than 1, the probability ofrare events is overestimated, and events of moderateto high probability are underestimated.(23)

The transformed probability, px, of a gain or lossamount, x, is used to assign probabilities to each ofthe gamble outcomes, such that the probability ofeach outcome,

π+i = w+ (pi + · · · + pn) − w+ (pi+1 + · · · + pn)

for k < i < n ; (A.4)


π−j = w− (p1 + · · · + pj ) − w− (p1 + · · · + pj−1)

for 1 < j ≤ k . (A.5)

The probability weights for each outcome refereither to possible gains, π+, or to possible losses, π−.In Equations (A.4) and (A.5), the respective prob-ability weights are determined by the difference be-tween the probability of an outcome at least as goodas x and an outcome better than x, for gains, and thedifference between the probability of an outcome atleast as bad as x and an outcome worse than x, forlosses.

The subjective value, V(gamble option), of eachgamble option is determined by combining the sub-jective value and probability weights of each out-come, such that:

V (gamble option) =k∑

j=1

v (xj ) π−j +

n∑i=k+1

v (xi ) π+i .

(A.6)CPT predicts that the individual will choose the

option with the highest subjective value.(23)

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