Judgment and Decision Making, Vol. 2, No. 6, December 2007, pp. 342–350 Maximizers versus satisficers: Decision-making styles, competence, and outcomes Andrew M. Parker1∗, Wändi Bruine de Bruin2, and Baruch Fischhoff2,3 1RAND Corporation, Pittsburgh PA 2Department of Social and Decision Sciences, Carnegie Mellon University 3Department of Engineering and Public Policy, Carnegie Mellon University Abstract Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bru- ine de Bruin et al., 2007). Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002), we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions, more avoidance of decision making, and greater tendency to experience regret. Con- trary to predictions, self-reported maximizers were more likely to report spontaneous decision making. However, the relationship between self-reported maximizing and worse life outcomes is largely unaffected by controls for measures of other decision-making styles, decision-making competence, and demographic variables. Keywords: maximizing, satisficing, decision making, competence, decision style. 1 Introduction 1998, 2000), asking whether, through preference or abil- ity, individuals make decisions in consistent ways, across tasks and situations (Bromiley & Curley, 1992). Individ- ualdifferencesthathavebeenexaminedincluderiskaver- sion and risk judgments (Slovic, 1962; Weber, Blais, & Betz, 2002); preference for rational, intuitive, dependent, avoidant, or spontaneous decision-making styles (Scott & Bruce, 1985); and decision-making competence (Bruine de Bruin et al., 2007; Finucane et al., 2002, 2005; Parker & Fischhoff, 2005). Building on Simon’s work, Schwartz et al. (2002) de- veloped a scale measuring the degree to which individ- uals report trying to maximize, rather than satisfice. It includes items such as “When I watch TV, I channel surf, often scanning through the options even while attempting to watch one program.” The other items capture ways in which one might explore as much information as possi- ble when making a choice. Given the many options often available in modern life (e.g., TV channels, cars, jobs, prospective mates), maximizing is no small feat (Iyen- gar & Lepper, 1999; Schwartz, 2004a; 2004b; Tversky & Shafir, 1992). Perhaps because of the challenges of successfully im- plementing a maximizing strategy, people who attempt to do so fare less well in life, in the sense of experienc- ing less happiness, optimism, self-esteem, and life satis- faction, while incurring more depression, perfectionism, Behavioral decision research (Edwards, 1961; Hastie & Dawes, 2001; Yates, 1990) characterizes behavior in terms of its consistency with the axioms of utility max- imization (Bernoulli, 1738/1954; von Neumann & Mor- genstern, 1953). A half-century of research has revealed both consistency with and departures from that norm (e.g., Baron, 2000; Plous, 1993). The latter include “sat- isficing,” choosing an alternative that is “good enough,” rather than “maximizing,” selecting the option with the highest expected utility (Simon, 1978). Such strategies can be beneficial — if they save enough cognitive effort to justify any loss in expected payoff (Simon, 1955, 1956, 1957). Historically, decision-making research has focused on general processes underlying deviations from normative theory, such as satisficing instead of maximizing (Lopes, 1987). More recently, attention has turned to individual differences in decision making (e.g., Stanovich & West, ∗This research was funded by the National Science Foundation (SES–0213782, SES–0433152). The authors thank Jónína Bjarnadót- tir, Jacob Chen, YoonSun Choi, Rebecca Cornelius, Mandy Hol- brook, Mark Huneke, Kathleen Pinturak, and Alanna Williams for their assistance. Address: Andrew M. Parker, RAND Corporation, 4570 Fifth Avenue, Suite 600, Pittsburgh, PA 15213–2665; email: firstname.lastname@example.org. 342
343 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers and regret (Schwartz et al., 2002). Moreover, individuals who try to maximize may have less constructive decision- makingstyles(Schwartzetal., 2002). Forexample, aspir- ing maximizers make more upward social comparisons, thereby inducing regret and counterfactual thinking about what might have been. They rely more on external in- formation sources (Iyengar, Wells, & Schwartz, 2006), which might lead them to further question their choices. Thus, these decision-making styles may undermine the very satisfaction that attempted maximizers try so hard to achieve (Schwartz et al., 2002). Even when maximizing pays off with better outcomes, satisfaction with those outcomes still may not follow. Iyengar, Wells & Schwartz (2006) found that recent col- lege graduates who described themselves as maximiz- ers secured jobs with 20% higher starting salaries, but felt less satisfied during the job search and afterward. One possible explanation is that attempting to maxi- mize encourages focusing on one easily compared fea- ture (salary), while neglecting other features important to job satisfaction. Were that the case, then those who at- tempt to maximize may make poorer decisions, despite strongly desiring the opposite. Conversely, in decisions that lack easily compared criteria, would-be maximizers may face cognitively intractable situations, like those that led Simon to propose the advisability of satisficing. Consistent with these hypotheses, Bruine de Bruin et al. (2007) found that people with higher self-ratings on Schwartz et al.’s (2002) maximizing scale had lower scores on a measure of Decision-Making Competence (DMC), which is described below (r= −.19, p<.001). In addition, self-identified maximizers also reported worse outcomes on the Decision Outcome Inventory (DOI), which includes 41 negative life events that might reflect poor decision making. These events range broadly in their impacts and frequency, and include ruining clothes in the laundry, having a check bounce, having a mortgage or loan foreclosed, being in jail overnight, and having beendiagnosedwithtype2diabetes(whichismorelikely among people who have made poor lifestyle choices). The analyses in Bruine de Bruin et al. (2007) focused on developing and validating the DMC and DOI mea- sures. In that context, Schwartz et al.’s (2002) self- reported maximization scale was one of several compari- son measures. As a result, the paper reported zero-order correlations of maximizing with DMC and DOI, but not with other decision-making styles or demographic char- acteristics. In particular, the analyses did not examine the extent to which lower DMC scores and problem- atic decision-making styles account for the correlation between self-reported maximizing and poorer life out- comes. Here, we examine this question, using Bruine de Bruin et al.’s (2007) diverse community sample and rich dataset. We begin by asking whether self-reported maximiz- ers tend to report several decision-making styles. One such measure is behavioral coping, or taking action to resolve difficult tasks, rather than working around them (Epstein & Meier, 1989; Katz & Epstein, 1991). Because self-reported maximizers may set unattainable goals, they should report less of such coping. Five other measures come from Scott and Bruce’s (1985) suite of decision- making style scales. Self-reported maximizers should re- port engaging in (1) more rational decision making, re- flecting their perception of systematic deliberation about their choices; (2) less intuitive decision making, attempt- ing to avoid relying on feelings and instincts (e.g., Slovic, Finucane, Peters, & McGregor, 2004); (3) more depen- dence on others, reflecting interpersonal comparisons and the quest for information; (4) more avoidant decision making, postponing decisions to search for more infor- mation and ponder the possibilities; and (5) less sponta- neous decision making, in the sense of taking more time to carefully decide. Finally, we expect self-reported max- imizers to report greater regret about their past decisions, replicating Schwartz et al.’s (2002) finding in a diverse community sample. Subsequently, we take advantage of the diversity of Bruine de Bruin et al.’s sample to examine how self- reportedmaximizing varieswith socio-demographicvari- ables. Finally, we examine whether the correlations be- tween self-reported maximizing and the two performance measures, A-DMCandDOI,arereducedaftercontrolling for the other styles and demographics. 2 Method 2.1 Sample We recruited 360 people from the Pittsburgh area through social service organizations (46.1%) and other commu- nity groups. The social service organizations were lo- cated in poorer sections of the city and served disadvan- taged populations. Other community groups were located in relatively more affluent locations and did not address the needs of disadvantaged populations. Among partici- pants responding to demographic questions, ages ranged from 18 to 88 (M=47.7, SD=17.0); 73.8% were women, 65.5% white, 28.2% African-American, and 6.3% other racial minorities. Highest level of education was 2.8% no degree, 44.6% a high school degree, 13.0% an asso- ciate’s degree, 29.1% a bachelor’s degree, 9.5% a mas- ter’s degree, and 0.9% a Ph.D. Except for the proportion of women, the sample resembles U.S. Census figures for the Pittsburgh area.
344 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers 2.2 Measures edge test, with each answer accompanied by a probabil- ity judgment (on a scale ranging from 50%=just guess- ing to 100%=absolutely sure) that it is correct. Each per- son’s score is the absolute value of the difference between their mean probability judgment and the actual percent- age correct. The questions were representatively drawn from 17 Complete Idiot’s guides advising on a wide vari- ety of decisions. Applying Decision Rules assesses the ability to apply specified decision rules (e.g., elimina- tion by aspects) to ten hypothetical choices, with each option characterized on several attributes in a table. Con- sistency in Risk Perception assesses the ability to make risk judgments that are internally consistent (e.g., giving a lower probability to dying in a terrorist attack than to dying from any cause), with 20 paired judgments. Resis- tancetoSunkCostsusestensingle-choicesunkcostprob- lems to assess the ability to ignore irrecoverable prior in- vestments, and consider only future consequences when making decisions. The aggregate Adult Decision-Making Competence (A-DMC) measure is the unweighted aver- age of standardized task scores. It represents the extent to which individuals can make decisions normatively — as a potential correlate of their self-reported attempts to maximize. Decision Outcomes Inventory (DOI). The Decision Outcome Inventory elicits self-reports of having experi- enced each of 41 negative events, varying widely in do- main and severity (e.g., threw out food or groceries you had bought, locked yourself out, got divorced, had an unplanned pregnancy). For 35 of these events, respon- dents only received credit for avoiding a negative out- come if they indicated having made a decision crucial to experiencing it (e.g., only those who reported hav- ing a driver’s license received credit for not having lost it). As a proxy for severity, each outcome was weighted by the proportion of participants who reported not expe- riencing it (among those who had the opportunity), as- suming that more severe outcomes tend to be less fre- quent (as is the case with spending a night in jail, versus forgetting a birthday). Severity was computed specifi- cally using responses from this sample. Weighted out- comes were averaged and subtracted from 0, so that higher scores reflect better outcomes. Thus, the DOI score reflects the number of negative outcomes respon- dents had avoided, out of those they had the oppor- tunity to experience, weighted by severity. DMC and DOI measures are available from the authors or online at http://sds.hss.cmu.edu/risk/ADMC.htm, as well as in this issue of Judgment and Decision Making (http://journal.sjdm.org/vol2.6.htm). Demographics. Education is assessed from answers to, “What is currently your highest level of education?” with the options of no degree, high school, associate, bachelor, masters, and Ph.D. A dummy variable for being recruited Self-reported maximizing. (2002) 13-item measure of tending to maximize, rather than satisfice, which uses a scale anchored at 1 (=com- pletely disagree) and 5 (=completely agree). Other decision-making styles. We used the 15-item be- havioral coping module of the Constructive Thinking In- ventory (e.g., “When I realize I have made a mistake, I usually take immediate action to correct it;” Epstein & Meier, 1989; Katz & Epstein, 1991), with a scale an- chored at 1 (=definitely false) and 5 (=definitely true). Scott and Bruce (1985) provided scales for self-reported attempts to (a) make decisions rationally (4 items; e.g., “I make decisions in a logical and systematic way”), (b) base decisions on intuitions (5 items; e.g., “I gener- ally make decisions that feel right to me”), (c) depend on others (5 items; e.g., “I often need the assistance of other people when making important decisions”), (d) avoid making decisions (5 items; e.g., “I postpone de- cision making whenever possible”), and (e) make deci- sions spontaneously (5 items; e.g., “I make quick deci- sions”). The response scale was anchored at 1 (=com- pletely disagree) and 5 (=completely agree). Finally, we used Schwartz et al.’s (2002) 5-item measure of the ten- dency to feel regret (e.g., “When I think about how I’m doing in life, I often assess opportunities I have passed up”), using the same response scale as Scott & Bruce (1985). Adult-Decision-Making Competence. A-DMC has six component tasks, selected to cover the skills central to normative theories of decision making (Bruine de Bruin et al., 2007; Parker & Fischhoff, 2005). The A-DMC tasks were based on ones studied by behavioral decision researchers, drawing on the understanding derived from multiple rounds of experimental research. Resistance to Framing uses valence-framing problems (Levin, Schnei- der & Gaeth, 1998) to measure whether choices are af- fected by formally irrelevant variations in how options are described. For example, one pair of items asks for quality judgments of ground beef described as either (a) “20% fat” or (b) “80% lean.” Fourteen such pairs are presented in two sets, with one containing the positively- valenced member of each pair, and the other containing the corresponding negatively-valenced items. Recogniz- ing Social Norms asks “out of 100 people your age, how many would say it is sometimes OK” to engage in each of 16 undesirable behaviors (e.g., “steal under certain cir- cumstances”). These estimates are compared to the per- cent of respondents from this study who had reported earlier that “it is sometimes OK” to engage in each be- havior, with each person’s score being the within-subject correlation between judged norms and observed norms. Under/Overconfidence uses a 34-item true/false knowl- We used Schwartz et al.’s The A-
345 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers Table 1: Relationships among decision-making styles. Decision-making Style Maximize Cope Rat. Int. Dep. Avoid. Spon. To cope behaviorally To decide rationally To decide intuitively To depend on others To avoid decisions To decide spontaneously To feel regret −.20*** .06 .05 .29*** .37*** .31*** .47*** .58*** .42*** −.04 −.43*** −.11+ −.26*** −.21*** −.31*** .44*** .23*** .24*** .00 .19*** −.01 .40*** .10+ .34*** .44*** .41*** .01 .18** + p<.10; *p < .05; ** p < .01; *** p < .001 two-sided. 2.4 Analysis strategy fromasocialserviceorganizationtargetingresidentswith lower levelsof socio-economic status wasused as a proxy for socio-economic status (SES; 0 = lower, 1 = higher). First, we examine Pearson correlations between self- reported maximizing and the other decision-making styles, as well as the demographic variables. Then, we report hierarchical multiple linear regressions predicting A-DMC, entering maximizing on the first step, the other the decision-making styles on the second step, and de- mographics on the third. Finally, we report hierarchical regressions predicting DOI, entering maximizing and the other decision-making styles on the first two steps, demo- graphics on the third, and A-DMC on the fourth. 2.3 Procedure Respondents were run in group survey sessions held in their communities. On a cover letter, respondents were told that the study was about decision styles, and that they would “be given several decision problems, items from an intelligence test, as well as questions about de- cision styles, decision outcomes, and demographic in- formation.” A-DMC tasks were self-paced, in the or- der: (a) positive versions of the Resistance to Framing items, (b) Recognizing Social Norms questions asking if “it is sometimes OK” to engage in different behaviors, (c) Under/Overconfidence, (d) Applying Decision Rules, (e) Consistency in Risk Perception, (f) Resistance to Sunk Costs, (g) negative versions of the Resistance to Fram- ing items, and (h) Recognizing Social Norms questions asking about their peers’ reported behaviors. This order maximized the distance between paired tasks (Resistance to Framing, Recognizing Social Norms). Subsequently, participants completed the decision-making styles mea- sures, in the order: regret, maximizing, behavioral cop- ing, rational, intuitive, dependent, avoidant and sponta- neous decision making. Finally, they completed the DOI and the demographic questions. Participants received two envelopes containing $17.50 each, with the option to do- nate to the organization that recruited them. As stated in the recruitment materials: “We will give $35 for your time and effort. You will be given a choice between (a) taking home $35; (b) giving $35 to the organization through which you were recruited or (c) taking home $17.50 and giving the organization $17.50.” 3 Results 3.1 Scale properties All eight decision-making style variables had ranges of 1 to 5, with higher scores indicating greater endorsement of the style. The mean self-reported maximizing score was 2.9 (Cronbach α = .76). Other means were 3.0 for regret (Cronbach α = .65), 3.8 for behavioral coping (Cronbach α=.86), 3.8 for rational (α = .85), 3.6 for intuitive (α = .87), 3.4 for dependent (α = .83), 2.6 for avoidant (α = .89), and 2.6 for spontaneous (α = .87) decision making. As reported in Bruine de Bruin et al. (2007), A-DMC has an α of .85 and test-retest reliability of .73. DOI has an α of .88. 3.2 Self-reported maximizing and other decision-making styles Table 1 presents correlations between self-reported max- imizing and the other decision-making styles. Consistent with Schwartz et al.’s account, self-reported maximizing is related to less behavioral coping, more depending on others, and a stronger tendency to avoid decisions. How- ever, we did not find the expected corrections with self-
346 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers Table 2: Relationships between decision-making styles and demographics. Gender Age SES Education To maximize To cope behaviorally To decide rationally To decide intuitively To depend on others To avoid decisions To decide spontaneously To feel regret −.11* .01 −.04 .10 .07 .09 .01 −.04 .01 .20*** .19*** .20*** .11 −.07 −.08 −.08 −.16** .24*** .16** .05 .05 −.20*** −.25*** −.11* −.16** .20*** .16** −.07 .05 −.12* −.27*** −.09 + p<.10; *p < .05; ** p < .01; *** p < .001 two-sided Notes: Gender is coded as 0 if male and 1 if female. SES is 1 (higher) if respondent was interviewed at a social service organization (0 otherwise). Education is coded as 1 if no degree, 2 if high school, 3 if associates degree, 4 if bachelors degree, 5 if masters degree, and 6 if Ph.D. reports of deciding rationally and deciding intuitively — and found the opposite of the expected correlation with deciding spontaneously. Replicating a key result from Schwartz et al. (2002), we find that people who reported stronger commitments to maximizing also reported expe- riencing greater regret. models predicting A-DMC (left) and DOI (right). Step 1 uses self-reported maximizing as a predictor, Step 2 adds the other seven decision-making styles, Step 3 adds the demographics, and Step 4 adds A-DMC (for the DOI analyses). 3.4.1 Decision-making competence 3.3 Decision-making graphic characteristics styles and demo- Step 1 of Table 3 shows that the self-ratings on the maximizing scale have a significant negative relation- ship with A-DMC. When all eight styles are considered (Step 2), A-DMC scores are significantly correlated with two seemingly contradictory styles: less reported maxi- mizing and less spontaneous decision making.2Adding the demographics (Step 3) leaves weak significant rela- tionships with reported maximizing and spontaneous de- cision making, while revealing that A-DMC is slightly lower for women and much higher for respondents with more education and higher SES. Row 1 of Table 2 shows that self-reported maximizing was greater for respondents drawn from lower SES lo- cations and reporting less education (SES and education were correlated at r = .42, p < .001). It was slightly higher among men and unrelated to age. The rest of the table shows that each style measure, except depending on oth- ers, was significantly correlated with at least one demo- graphic variable. The analyses below clarify these rela- tionships. 3.4 Predicting tence and decision outcomes from decision-making styles graphics decision-making compe- 3.4.2 Decision outcomes Step 1 of Table 4 shows a significant correlation between reporting more maximizing and experiencing worse de- cision outcomes. When all eight styles are considered (Step 2), significantly better outcomes are reported by re- spondents who report less maximizing, more behavioral coping, more intuitive decision making, and less deciding spontaneously — with marginally significant correlations with depending on others more and avoiding decisions and demo- As mentioned, Bruine de Bruin et al. (2007) found statis- tically significant pair-wise relationships between several of the decision-making style variables and both A-DMC and DOI.1Tables 3 and 4 present hierarchical regression 1As reported in Bruine de Bruin et al. (2007), each of the A- DMC components correlated negatively with self-reported maximizing, reaching statistical significance for under/overconfidence (r = −.21, p > .001) and consistency in risk perception (r = −.13, p < .05). 2These regressions were run using all planned tests. Follow-up anal- yses, removing non-significant predictors, did not qualitatively change the patterns of results presented here.
347 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers Table 3: Hierarchical linear regressions predicting A- DMC. Table 4: Hierarchical linear regressions predicting DOI. Predictor Variable Step 1 Step 2 Step 3 Predictor Step 1 Step 2 Step 3 Step 4 To maximize cope behaviorally decide rationally decide intuitively depend on others avoid decisions decide spontaneously feel regret Female Age Education Higher SES A-DMC F-statistic (df) Adjusted R2 −.21*** −.15* − − − − − − − − − − − − 14.85*** 5.35*** (1, 300) .04 −.12* .00 .04 .08 .01 −.01 −.10+ .03 −.12* −.04 .23*** .36*** − 15.58*** (12, 289) .37 maximize cope rational intuitive others avoid spontaneous regret Female Age Education Higher SES A-DMC F-statistic (df) Adjusted R2 −.26*** −.15* − − − − − − − − − − − − 20.96*** 11.46*** 12.34*** (1, 300) (8, 293) .06 .22 −.16** .14+ −.03 .11+ .07 −.09 −.13* .14+ −.04 .09 .07 −.08 −.17** .06 .01 .28*** −.12* .14* .21*** 12.69*** (13, 288) .34 .09 .07 .01 .05 .17* −.02 .15* .12+ −.12+ −.24*** −.20** .04 − − − − − −.04 −.19** .03 − − − − − .07 −.02 .28*** −.08 .21*** − (8, 293) .10 (12, 289) .31 + p<.10; *p < .05; ** p < .01; *** p < .001 two-sided Note: Statistics are standardized regression coefficients. Notes: N = 302. Variance inflation factors range from 1.0 to 2.5, indicating only modest multicollinearity. 4 Discussion less. As with A-DMC (Table 3), spontaneous decision- making style stood out.3The correlations with reported maximizing and spontaneity appear to remain stable with the addition of the demographic variables (Step 3). Step 3 also reveals better DOI for older and higher SES respon- dents. Adding the A-DMC (Step 4) reveals that scoring higher on A-DMC is significantly related to reporting more positive decision outcomes on the DOI, even af- ter including all the decision style and demographic mea- sures. DOI scores are also higher for individuals who are older, have less education, and come from the higher SES locations. Among the style measures, significant positive relationships remain for self-reported maximiz- ing and deciding spontaneously.4,5 As predicted, individuals who report a stronger tendency to maximize are also more likely to report other maladap- tive decision-making styles, such as less behavioral cop- ing, greater dependence on others, more avoiding of de- cision making, and greater experience of regret. Contrary to predictions, self-reported maximizers are more likely to report spontaneous decision making. We failed to find predicted correlations with rational and intuitive styles. Below, we first explain how these findings affect the rela- tionship of maximizing with A-DMC and DOI, and then discuss each in more detail. Bruine de Bruin et al. (2007) found that self-reported maximizers appeared to be poorer decision makers, whether measured by a standard measure (A-DMC) or self-reported outcomes (DOI). Here, we found that self- reported maximizing and A-DMC were negatively re- lated in hierarchical regression analyses that included other decision-making styles that might characterize maximizers, as well as several standard demographic variables. The introduction of spontaneous decision making into the regression models substantially reduced 3Follow-up analyses, adding each decision style individually, show that the reduction in the coefficient for maximizing is due almost en- tirely to the addition of the spontaneous decision-style variable. 4A quadratic maximizing term, added to this equation after Step 1, hadastandardizedbetaof.76, p<.05. Thepositivecurvaturerepresents modestly better reported decision outcomes for those with very low and very high maximizing scores. Adding this term did not markedly affect the standardized coefficient for maximizing, either alone (standardized beta = −.25) or with the other variables included (standardized beta = −.12). Including a quadratic maximizing term had no effect on the regression predicting A-DMC. 5When an interaction between A-DMC and self-reported maximiz- ing is entered into the equation, it is non-significant (standardized beta = −.05). Hence, whereas higher DMC can compensate for a maximizing style, it does not moderate it (or vice versa).
348 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers the relationship between self-reported maximizing and decision-making ability, while other decision-making styles (behavioral coping, deciding rationally, deciding intuitively, depending on others, avoiding decisions, and feeling regret) did not. Thus, with the exception of spon- taneity (which we address below), attempting to max- imize appears to be a distinct, maladaptive decision- making style, associated with poor decision making, in this diverse community sample.6 efficient on maximizing was not substantially reduced in hierarchical analyses where many of the other styles dropped from significance, suggesting that maximizing, as measured, may be a more proximal correlate of A- DMC and DOI. Past research has found greater dissat- isfaction among self-reported maximizers (Iyengar et al., 2006; Schwartz et al., 2002), a result that might reflect their having higher expectations or lower abilities. The present results suggest that maximizers actually experi- ence more negative life outcomes (as measured by the DOI),perhapsasaresultofbeinglesscompetent(asmea- sured by A-DMC). Consideration of the other decision-making styles may help clarify these patterns. Self-reported maximizers re- port less use of behavioral coping strategies for reduc- ing the stress of unresolved challenges (Epstein & Meier, 1989; Katz & Epstein, 1991). Satisficing could be one such strategy, which Bruine de Bruin et al. (2007) found to be associated with better decision-making competence and outcomes. Self-reported maximizers report depending more on others for information, consistent with the accounts of Schwartz et al. (2002) and Iyengar et al. (2006). While good advice can improve decisions, consultation can also undermine effective decision making by encouraging un- realistic aspirations, focusing attention on readily quan- tified outcomes, and revealing contradictory advice (Fis- chhoff, 1992). Perhaps reflecting these contrary possibil- ities, self-reported dependence on others is unrelated to decision-making competence or outcomes. Self-reported maximizers report greater decision avoidance, plausibly theresult of takingthe time toexam- ine each option in detail. When that examination fails to yield clearly superior options, the result may be inaction or actions driven by events outside of individuals’ control (Fischhoff, in press). The perils of these processes might contribute to the finding that reporting greater decision avoidance was correlated with worse decision-making competence and decision outcomes. Experimental stud- ies have found greater avoidance as the number and qual- ity of decision options increases (Dhar, 1997; Tversky & Shafir, 1992), decision features that might particularly af- fect maximizers. Self-reported maximizers reported greater spontaneity, in the sense of making spur-of-the-moment choices. This result appears contrary to the image of maximizers ago- nizing over the “best” option. One possible clue is that reporting spontaneity was correlated with reporting de- cision avoidance; perhaps people who postpone making decisions end up making what feel like “spur of the mo- ment” choices. It is also possible that maximizers tend to see choices as spontaneous because they have difficulty seeing them as adequately reasoned. As noted (footnote 3), the spontaneous decision-style measure was the only one that substantially reduced the coefficients on maxi- mizing in either hierarchical regression. Self-reported maximizing was, however, unrelated to the self-reported rational decision-making style measure, a seemingly related construct. A speculative explanation is that the maximizing scale includes items expressing frustration (e.g., “Renting videos is really difficult. I’m always struggling to pick the best one.”), whereas the ra- tionality items are more neutral (e.g., “When making de- cisions, I consider various options in terms of a specific goal”). Past research has found that rational decision- making scores are positively correlated with competence and good outcomes, while maximizing is negatively cor- related (Bruine de Bruin et al., 2007; Crossley & High- house, 2005; Russ, McNeilly & Comer, 1996). How- ever, in the presence of the other predictors, the ratio- nal decision-making style predicted neither A-DMC nor DOI, whereas maximizing continued to be negatively re- lated to them. Possibly, the rational decision-making scale captures self-observation of some behaviors actu- ally associated with good decision making, whereas the maximizing scale captures self-observation of ineffective ones. Self-reported maximizing was not negatively corre- lated with self-reports of deciding intuitively, seemingly the complement of deciding rationally. An intuitive style has sometimes been related to better competence and out- comes (Bruine de Bruin et al., 2007; Crossley & High- house, 2005), and sometimes not (Phillips & Strohmer, 1982; Singh & Greenhaus, 2004). Future research should investigate the possibilities that this is the result of mea- surement or more substantive issues. Self-reported maximizers tend to report experiencing more regret, replicating, with a diverse community sam- ple, a result that Schwartz et al. (2002) attributed to unfa- vorable social comparisons. Bruine de Bruin et al. (2007) found that people who express more regret have worse decision-making competence and outcome scores (Bru- ine de Bruin et al., 2007), suggesting that the feeling might be warranted. In this diverse sample, we found more self-reported maximizing among men (as found in some samples by The regression co- 6This conclusion does not exclude the possibility that there may be positive (or negative) externalities to maximizing, realized by others around the maximizer.
349 Judgment and Decision Making, Vol. 2, No. 6, December 2007 Maximizers versus saticficers References Schwartz et al., 2002), less educated people, and ones in- terviewed at an organization serving lower SES individ- uals. On the other decision-making style measures: (a) there were no other gender differences; (b) older individ- uals reported more behavioral coping and more rational and intuitive styles; (c) lower SES individuals reported lessbehavioralcoping, beinglessrational, moreavoidant, more spontaneous styles, and more regretful; (d) individ- uals with less education reported less behavioral coping, being more rational, more avoidant, and more sponta- neous. As with previous studies, we report correlations. Al- though it is tempting to interpret the results as show- ing that good outcomes and decision-making competence follow from attempting to satisfice (rather than maxi- mize), the opposite is also possible. Negative life ex- periences, or recognized limits to one’s decision making could encourage people to maximize, hoping that it will lead to better outcomes. That might be particularly true for low SES individuals, who tend to face a world with greater risks and fewer resources. The relationships among the decision-making styles (e.g., the positive correlation between maximizing and spontaneity) also raise the possibility of more complex relationships, including mediation or third-variable ex- planations. Experimental studies (e.g., manipulating maximizing pressure) might provide causal evidence. Prospective designs might identify temporal emergence of different tendencies — as reported by Parker & Fis- chhoff (2005), whose 18–19 year old respondents had entered a longitudinal study between ages 10–12. Our conclusions also depend on the reliability and validity of our measures – as seen in some of the discussion (above) about the meaning of various scales. 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