Synthese (2012) 189:131–145 Knowledge, Rationality & Action 717–731 DOI 10.1007/s11229-012-0156-1 Deliberative adjustments of intuitive anchors: the case of diversification behavior Shahar Ayal · Dan Zakay · Guy Hochman Received: 20 December 2011 / Accepted: 8 July 2012 / Published online: 31 July 2012 © Springer Science+Business Media B.V. 2012 Abstract As part of the rationality debate, we examine the impact of deliberative and intuitive thinking styles on diversity preference behavior. A sample of 230 students completed the Rational Experiential Inventory and the Diversity Preference Questionnaire, an original measure of diversification behavior in different real-life situations. In cases where no normative solution was available, we found a clear preference for diversity-seeking in the gain domain and diversity-aversion in the loss domain, regardless of cognitive thinking style. However, in cases where one alternative normatively dominated the other, participants high in deliberative thinking style were more calibrated to normative behavior, regardless of whether their intuitive tendency preference and the normative solution were contradictory or pointed in the same direction. Our findings support a model in which deliberative but not intuitive thinking style is the crucial predictor of rational behavior, since it enables people to better adjust their intuitive preference anchor when normative considerations require doing so. Keywords Rationality · Diversification behavior · Dual systems · Thinking styles 1 Introduction The issue of whether individuals are rational thinkers has been the focus of much research in the past few decades. From the notion of the “economic man” (Godkin 1891), through “bounded rationality” (Simon 1955) and the “administrative man” view S. Ayal (B) · D. Zakay School of Psychology, Interdisciplinary Center (IDC), Herzliya, Israel e-mail: s.ayal@idc.ac.il G. Hochman Duke University, Durham, NC, USA 123 132 Synthese (2012) 189:131–145 (Simon 1973) which was developed into the “heuristic and biases” approach (Tversky and Kahneman 1974) to recent notions such as the “adaptive decision maker” (Payne et al. 1993) and the “predictably irrational” (Ariely 2008) no firm conclusion has been reached. In line with Simon (1955) approach, rationality is defined here as adherence to the normative solution or a preference for options which provide the highest expected utility. One aspect of such rational behavior that has attracted less attention (Stanovich and West 1998) is individual differences in cognitive thinking styles and their impact on rationality. Typically, individual differences are treated as measurement or random errors (Stein 1996). Nevertheless, empirical examinations have consistently demonstrated their importance in decision making research. For instance, Zakay et al. (Shiloh et al. 2001; Zakay 1990) demonstrated that tendencies towards a more compensatory decision making style were highly correlated with individual levels of need for closure. Hilbig (2008) found substantial individual differences in the use of recognition information. Smith and Levin (1996) showed that people low in need for cognition are more affected by framing effects than those high in need for cognition, and Shiloh et al. (2002) found that people high in intuitive thinking are more prone to judgmental biases. According to Stanovich and West (2000), two factors can account for individual differences in rational behavior. One factor is related to performance errors; that is, momentary and fairly random lapses in ancillary processes such as lack of attention or memory distortions. The other factor, referred to as ‘alternative task construal’, reflects the way people perceive and interpret a given problem or task based on their information processing style. Here, we focus on the latter factor, to further explore the role of individual differences in information processing styles in rationality. As a case in point, we investigated the impact of cognitive thinking styles on diversity-preference behavior and in particular how people choose among pools of uncertain outcomes (e.g., portfolios). A variety of studies have shown that people exhibit a strong preference toward diversity-seeking under conditions of gains (Galak et al. 2011; Hedesström et al. 2006; McAlister and Pessemier 1982). For example, it has been shown that when people are required to simultaneously choose several goods (e.g., candy bars), they usually seek a more diversified package than they end up wanting (Read and Loewenstein 1995; Simonson 1990). Recently, Ayal and Zakay (2009) proposed that decision makers intuitively understand that higher levels of diversity can reduce risk, and thus over time develop a “perceived diversity heuristic” to evaluate the risk of each pool by intuitively assessing the diversity of its sources. Accordingly, since people tend to avoid risk under conditions of gain but seek risk under conditions of loss (Fishburn and Kochenberger 1979; Kahneman and Tversky 1979; Tversky and Kahneman 1992), they are expected to exhibit diversity-seeking in the gain domain, (so as to avoid putting all their eggs in one basket), but diversity-aversion in the loss domain (Ayal and Zakay 2009; Ayal et al. 2011). In line with this supposition, a recent study found that participants who manage multiple debt accounts prefer to close small accounts first in order to reduce the total number of debts even when it is irrational (in terms of interest rates) to do so (Amar et al. 2011). What makes diversity a good test bed for the rationality debate is that diversity preferences (i.e., a preference for the more or the less diversified option) can reflect rational behavior under conditions that entail a normative solution, but only personal 123 [718] Synthese (2012) 189:131–145 133 taste under conditions that do not entail such a solution. For instance, when the risk level associated with a certain pool is negatively correlated with its level of perceived diversity (e.g., as in the case of stock portfolio), diversity-seeking in the gain domain is indeed normatively recommended, since it actually reduces the risk of the portfolio. By contrast, when the risk level is positively correlated with its level of perceived diversity, as is the case for the ratio bias in which people prefer seven out of 100 over one out of ten (Denes-Raj and Epstein 1994), diversity seeking can yield biased choices (see Ayal and Zakay 2009 for a review). Finally, in many real-life situations no association exists between risk and the level of perceived diversity, and thus there is no normative benchmark for diversity preferences, such as in the choice between a package with three different types of candies and a package with three candies of the same type. In these kinds of situations the level of diversity only reflects the personal taste of each consumer, and no choice could be classified as more rational than the other. To test how individual preferences to diversify are influenced by cognitive thinking styles, we examine different aspects of diversity preference behavior in the context of the dual-systems approach (Epstein 1994; Evans 2003; Sloman 1996), which classifies cognitive processes as either intuitive or deliberative. Intuitive judgments are assumed to be associative, quick, unconscious, effortless, and more error-prone, while deliberative judgments are assumed to be slow, conscious, effortful, analytic and more calibrated to normative considerations (Kahneman and Frederick 2002). In a highly schematic way the dual-process model works as follows: when asked to make a decision, the intuitive system promptly processes some or all of the information, and immediately proposes an intuitive solution. At the same time, the deliberative system monitors the quality of the proposed solution, which it may approve, alter or override. Nevertheless, the relative contribution of each system is determined by situational factors (Epstein 2007; Inbar et al. 2010) and the individual characteristics of the decision maker (Epstein 2007; Stanovich and West 2002). Note, however, that although deliberative processes are assumed to be based on more analytic processes (e.g., Denes-Raj and Epstein 1994), recent research has shown that under certain conditions deliberative process can facilitate biases (Ayal and Hochman 2009; Dijksterhuis and Nordgren 2006) and intuitive processes can also lead to rational behavior (Acker 2008; Glöckner and Herbold 2011; Usher et al. 2011). Sloman (1996) defined Criterion S as a unique decision situation in which people simultaneously feel that two conflicting responses (intuitive or deliberative) are plausible, even if they do not act upon either. In these kinds of situations “…people first solve a problem in a manner consistent with one form of reasoning and then, either with or without external prompting, realize and admit that a different form of reasoning provides an alternative and more justifiable answer” (Sloman 1996, p. 11). That is, intuitive and deliberative processes compete for the control of overt responses, and a conflict arises in cases of incongruence where inputs from the intuitive system suggests a solution that is not aligned with a normative benchmark, whereas deliberative processes suggest a more rational solution that is based on more normative considerations. Building on this rationale, we aimed to explore the role of cognitive thinking styles on diversity preferences under two types of choice dilemmas: dilemmas in which no normative solution is available and diversity preferences only reflect personal taste, and dilemmas in which a normative solution is available in which one alternative dominates [719] 123 134 Synthese (2012) 189:131–145 the other. We further divided the dilemmas with a normative solution into dilemmas in which the normative solution contradicted the intuitive diversity preference (i.e., incongruent dilemmas) and dilemmas in which both the normative solution and intuitive diversity preferences pointed in the same direction (i.e., congruent dilemmas). Note that intuitive preferences are assumed to run counter to normative considerations only for incongruent dilemmas. Thus, we were able to assess which of the two systems prevailed, and whether cognitive thinking styles lead to an overlapping or an additive effect of the two systems in situations where both systems point in the same direction. 2 Hypotheses The general approach of this study was to characterize people according to their deliberative and intuitive thinking style and examine how this characterization affects their diversity preference in different types of situations. Based on previous research on cognitive thinking styles and Ayal and Zakay (2009) theoretical framework, we formulated two main hypotheses regarding the relationship between cognitive thinking style and diversity preferences: Hypotheses 1: In cases where no normative solution is available, diversity preferences (as a personal taste) are expected to increase under conditions of gain and decrease under conditions of loss. These tendencies will be observed regardless of the deliberative and/or the intuitive thinking style. Hypotheses 2: In cases where there is a normative solution in which one alternative dominates the other, higher levels of normative diversity preferences are expected for people high in deliberative thinking style as compared to people low in deliberative thinking style. The opposite pattern is expected for the intuitive thinking style. 3 Method 3.1 Participants 230 Interdisciplinary Center (IDC) Herzliya undergraduate (introduction to psychology course) and graduate (organizational behavior course) students (154 females, 76 males) volunteered to participate in the study in exchange for course credit. The average age was 24 years (SD = 5.68). 3.2 Design and procedure Participants were presented with a Qualtrics web-based questionnaire composed of two blocks. The first block included the diversity preference questionnaire (DPQ; see in full in the Appendix). This questionnaire consisted of eight dilemmas which were developed for the purposes of this experiment to evaluate diversity preference behavior. Each of the dilemmas described a different real-life situation that required a selection between two possible alternatives: one with high diversity and one with low or no diversity. In each of the dilemmas, the participants were asked to state which of the two alternatives they preferred, and to what extent, by dividing up 100 points 123 [720] Synthese (2012) 189:131–145 135 among them. A 50:50 split indicated indifference and any other division indicated preference for one of the alternatives. The DPQ Questionnaire included two categories of dilemmas. In the Diversity Preference Dilemmas there was no normatively dominant alternative and preference should be based only on personal taste rather than on rational considerations. There were four such dilemmas, two of which measured diversity preferences under conditions of gain and two under conditions of loss. For instance, one dilemma in the gain domain described a choice between two types of holiday gifts for employees in the form of a box of candy. The choices were a box filled with four different types of candy (high-diversified package), or a box filled with one type of candy (low-diversified package). On the other hand, one dilemma in the loss domain described a choice between two financial plans for a mortgage with the same constant interest rate, commission and management fee, one of which divided the amount of money among three different credit card loans (high-diversified portfolio) and the other charged the entire amount of money in one loan to one credit card (low-diversified portfolio). The Normative Diversity Preference Dilemmas could be solved by a normative solution. Thus, one alternative was dominant and preference based on rational considerations. There were four such dilemmas, two of which measured diversity preferences under conditions of gain and two under conditions of loss. For instance, one dilemma in the gain domain described a lottery in which participants needed to pick five numbers (between 1 and 49) with the chance of earning a large monetary prize if their numbers matched five lottery numbers randomly drawn by the experimenter. The choice alternatives for the lottery were to pick five different tickets and mark five numbers on each of them (high diversified portfolio with lower chances of winning) or pick one lottery ticket and mark six numbers on it (low-diversified portfolio with higher chances of winning). On the other hand, one dilemma in the loss domain described a choice between two blood transfusion clinics in a rural country. The choice alternatives for the clinic were a clinic that had a record of three HIV infections, three malaria infections and three hepatitis C infections for every 10,000 transfusions (high-diversified portfolio with lower risks) or a clinic that had a record of one HIV infection for every 1,000 transfusions (low-diversified portfolio with higher risk). Importantly, there were two different types of dilemmas with normative solutions. The first type was incongruent dilemmas in which the normative solution ran counter to intuitive diversity preferences under gain and loss domains. From the normative point of view, in these kinds of dilemmas participants should prefer the low-diversity portfolio in the gain domain (i.e., lottery dilemma), and the high-diversity portfolio in the loss domain (i.e., debts and transfusion dilemmas). The second type had one congruent dilemma from the gain domain (i.e., the funds dilemma) in which both the normative solution and the intuitive diversity preference point to diversity-seeking. The second block was composed of the short 24-item REI (Pacini and Epstein 1999) translated into Hebrew. The REI is a self-report inventory that assesses deliberative and intuitive thinking styles1 . Specifically, the REI consists of two unipolar scales 1 Pacini and Epstein originally used the terms rational versus experiential thinking styles. We use the more generic terms deliberative and intuitive, to differentiate cognitive thinking styles from normative/rational considerations. [721] 123 136 Synthese (2012) 189:131–145 (12 items each) which rank participants on two dimensions of decision making style. The first scale measures engagement in and favorability of cognitive activities and corresponds to deliberative thinking. The Deliberative Scale has been found to be positively associated with openness, conscientiousness and favorable basic beliefs, and negatively associated with neuroticism and conservatism (Pacini and Epstein 1999). The second scale measures engagement in and favorability of experiential activities and corresponds to intuitive thinking. The Intuitive Scale has been found to be positively associated with extraversion, agreeableness and emotional expressivity, and negatively associated with categorical thinking and intolerance (Pacini and Epstein 1999). Previous research has shown that the internal consistency reliability coefficient for each scale is high (above 0.85), whereas the correlation between them is small and negligible (Pacini and Epstein 1999). Thus, the REI is assumed to support Epstein (1994) claim of two independent information processing systems. The link to the questionnaire was sent by e-mail to participants via the IDC School of Psychology experiments website. This e-mail also included a consent form, basic instructions and explained the purpose of the questionnaire. We asked participants to answer the questions as best as they could. The order of the questionnaires was randomly assigned to participants such that half began with the DPQ questionnaire while the other half began with the REI questionnaire. The order of the items on each questionnaire remained constant. 4 Results 4.1 REI internal reliability Before we tested our hypotheses, we controlled for the reliability of the questionnaire scales. First, the reliability of the REI Hebrew translation was calculated using Cronbach’s alpha coefficient. In line with previous validations of the Hebrew translation of the questionnaire (Ayal et al. 2011; Shiloh et al. 2002) the internal consistency of the REI was found to be adequate for both the deliberative scale (α = 0.85) and the intuitive scale (α = 0.87). The correlation between the two scales was non-significant (r = 0.052, p = 0.435). Based on the median-split (Pacini and Epstein 1999) we classified individuals as either high or low on each of the two scales, and used these classifications in the subsequent analyses. 4.2 Individual differences in rational thinking 4.2.1 Dilemmas without a normative solution To test our first hypothesis, we calculated the diversity-preferences under conditions of gain (mean preference for the vacation and candy dilemmas) and diversity-preferences under conditions of loss (mean preference for casino and credit dilemmas) for each participant. In line with our hypothesis, we found a clear preference for diversity seeking in the gain domain and diversity aversion in the loss domain. This pattern of results was found regardless of cognitive thinking style (see Fig. 1). In the gain 123 [722] Synthese (2012) 189:131–145 137 Fig. 1 Mean diversity preferences for participants low and high in deliberative and intuitive thinking style under conditions of gain and loss. Error bars represent standard errors domain, the total mean diversity preference was 64.8 % (SD = 21.4) for individuals high in deliberative thinking and 63.3 % (SD = 21.1) for individuals low in deliberative thinking. Similarly, the total mean diversity preference was 64.5 % (SD = 21.3) for individuals high in intuitive thinking and 63.6 % (SD = 21.3) for individuals low in intuitive thinking. In the loss domain, however, a significant reduction in diversity preferences was observed. The total mean diversity preference was 42.7 % (SD = 24.8) for individuals high in deliberative thinking and 39.8 % (SD = 22.3) for individuals low in deliberative thinking. Similarly, the total mean diversity preference was 41.6 % (SD = 24.6) for individuals high in intuitive thinking and 40.9 % (SD = 22.6) for individuals low in intuitive thinking. A two (deliberative thinking: high vs. low) × two (intuitive thinking: high vs. low) × two (domain: gain vs. loss) repeated measure ANOVA revealed a significant effect for domain (F(1,226) = 112.230, p < 0.0001, η2 = 0.33) on diversity preference, but not for deliberative (F(1,226) = 1.12, p = 0.29, η2 = 0.05) or intuitive (F(1,226) = 0.08, p = 0.76, η2 = 0.00) thinking styles. In addition, no interactions were found between the thinking styles or between any of the thinking styles and the domain (e.g., the highest F score was found for the three-way domain × intuitive × deliberative interaction: F(1,226) = 1.443, p = 0.23). 4.2.2 Dilemmas with a normative solution To test our second hypothesis, we looked at diversity preferences separately for incongruent dilemmas (Criterion S dilemmas) and the congruent dilemma (i.e., the dilemma in which diversity preferences and the normative solution point to the same direction). For the incongruent dilemmas, we calculated diversity-preferences under conditions of gain (diversity preferences for the Lottery dilemma) and diversity-preferences under [723] 123 138 Synthese (2012) 189:131–145 Fig. 2 Mean normative-diversity preferences for participants low and high on deliberative and intuitive thinking styles under gain and loss conditions. Error bars represent standard errors. The arrows depict the direction of intuitive diversity preferences for gain and loss conditions of loss (mean preference for the debt and transfusion dilemmas) for each participant. This enabled us to examine whether participants were able to follow the normative recommendation which ran counter their intuitive preferences and would exhibit diversity aversion in the gain domain and diversity seeking in the loss domain. In line with our hypothesis, we found that participants high in deliberative thinking were much more calibrated to these normative solutions than low deliberative participants, regardless of their intuitive thinking style. As can be seen in Fig. 2, in the gain domain when the normative consideration suggested diversity aversion, participants low in deliberative thinking continued to show diversity seeking with an average preference of 59.6 (SD = 31.3). Participants high in deliberative thinking, on the other hand, reduced their diversification preference in the correct normative direction to 51.00 (SD = 34.4). By comparison, participants low in intuitive thinking showed diversity seeking with an average preference of 55.50 (SD = 32.02), which was similar to participants high in intuitive thinking who showed an average preference of 55.10 (SD = 33.70). Similarly, in the loss domain, when normative considerations recommended diversity seeking, participants low in deliberative thinking showed an average preference of 51.4 (SD = 27.4), while participants high in deliberative thinking increased their diversity preferences in the correct normative direction to 63.8 (SD = 30.1). Here again, participants low and high in intuitive thinking showed an average preference in the loss domain of 59.83 (SD = 30.02) and 55.40 (SD = 27.50) respectively, which were not significantly different from each other. A two (deliberative thinking: high vs. low) × two (intuitive thinking: high vs. low) × two (domain: gain vs. loss) repeated measure ANOVA revealed no significant effect for domain (F(1, 226) = 0.631, p = 0.43, η2 = 0.003), deliberative thinking (F(1, 226) = 0.431, p = 0.512, η2 = 0.002), or intuitive thinking (F(1, 226) = 0.688, p = 0.41, η2 = 0.003) on diversity preferences. More importantly, a significant 123 [724] Synthese (2012) 189:131–145 139 interaction was found between domain and deliberative thinking style (F(1, 226) = 12.830, p < 0.0001, η2 = 0.06), but not between domain and intuitive thinking style or between the two thinking styles (the highest F score was found for the intuitive × deliberative interaction: F(1,226) = 0.876, p = 0.35). Planned contrasts further revealed that diversity preferences were significantly different between participants high and low in deliberative thinking both in the gain (t (228) = 1.987, p < 0.05, d = 0.3) and in the loss domains (t (228) = −3.117, p < 0.005, d = 0.4). Thus, these results suggest that preferences for diversity seeking in the gain domain and diversity aversion in the loss domain were significantly reduced, and that this was more pronounced for participants high in deliberative thinking compared to participants low in deliberative thinking. Finally, we looked at diversity preferences in the congruent dilemma (i.e., funds dilemmas) in which the intuitive preference and the normative solution pointed in the same direction. In line with our findings for the incongruent dilemmas, we found that participants high in deliberative thinking were much more calibrated to the normative solutions than low deliberative participants, regardless of their intuitive thinking style. When the normative consideration suggested diversity seeking, participants low in deliberative thinking showed an average preference of 55.9 (SD = 31.6). Participants high in deliberative thinking, on the other hand, showed an average preference of 69.3 (SD = 31.6). By contrast, participants showed an average preference of 62.60 (SD = 32.0) whether they were low or high in intuitive thinking. A two-way ANOVA revealed a significant effect for deliberative thinking style (F(1,226) = 10.304, p < 0.01, η2 = 0.05), but not for intuitive thinking (F(1,226) = 0.113, p = 0.74, η2 = 0.001). In addition, no interaction was found between the two thinking styles. 5 Discussion We examined the effect of individual differences in cognitive thinking styles on rational choice. The results shed light on the impact of individual differences in deliberative thinking styles on diversity preferences, and highlight the importance of differentiating between cases in which the perceived diversity merely represents the spice of life and thus reflects general diversity preferences, and cases in which the perceived diversity is correlated with the level of risk and thus reflects normative diversity preference. When no normative solution exists, we found clear preferences for diversity-seeking under conditions of gain and diversity-aversion under conditions of loss which were not related to cognitive thinking styles. Thus, in line with previous research (e.g., Ayal and Zakay 2009; Read and Loewenstein 1995; Simonson 1990), these results suggest that in the absence of a normative advantage for a specific level of diversity, personal taste and task construal (e.g., gain vs. loss or sequential vs. simultaneous framing), rather than cognitive thinking styles, are the main drivers of diversity preferences. By contrast, when a normative solution exists and one alternative normatively dominates the other, diversity-preferences are also affected by normative considerations, and not only by personal taste. However, individuals whose style is high in deliberative [725] 123 140 Synthese (2012) 189:131–145 thinking are more calibrated to normative considerations than individuals low in deliberative thinking, since their diversity preferences are more adjusted toward the correct normative solution, even if it runs counter their initial intuitive diversity preferences (i.e., diversity-seeking in the gain domain and diversity-aversion in the loss domain). This was found for both congruent (where both the intuitive preference and the normative solution pointed in the same direction) and incongruent dilemmas. These findings may shed more light on the interaction between the two systems of reasoning (e.g., Epstein 1994; Sloman 1996). As can be seen in Fig. 2, our findings are compatible with a model in which diversity preferences are determined by initial inputs from the intuitive system that reflect personal tastes and task construal cues. In normative situations, however, these preferences are governed by the deliberative system that adjusts or overrides the intuitive system and directs behavior towards the normative (i.e., rational) solution (c.f., Kahneman and Frederick 2002). Thus, our results suggest that high deliberative individuals give more weight to rational considerations; hence their diversity preferences adjustments are stronger and more calibrated to normative standards. Moreover, our results may imply that the deliberative system not only overrides intuition in incongruent cases where the normative solution runs counter intuitive judgments, but also that it has an additive effect on intuition in congruent cases when the normative solution and the intuitive judgments point in the same direction. Of course, this model should be tested in future research that includes more diversity preference dilemmas and specifically additional congruent dilemmas in the gain and the loss domain. Finally, we showed that normative diversity preferences are affected by deliberative thinking, regardless of the intuitive thinking style. While previous research suggests that in certain conditions intuitive thinking gives more weight to irrelevant information and thus leads to more biases (e.g., Kahneman and Frederick 2002; Shiloh et al. 2002), our results suggest that this may not be the case, at least in diversity preferences. Rather, low deliberative thinking may serve as a much stronger driver of biased behavior than high intuitive thinking (see also Ayal et al. 2011). These results suggest that specific debiasing techniques and tools should be focused on educating people to give more weight to normative considerations, even when these considerations run counter their initial intuitions. 6 Conclusion Our perspective on individual differences in cognitive thinking styles contributes in several ways to the debate on the rationality of human kind. By using diversity preferences as a case in point, we demonstrated that behavioral tendencies are derived by initial (presumably automatic) intuitions. These tendencies serve as an anchor that is later adjusted by more rational considerations if it is necessary to correct or support the intuitive processes. The weight that individuals assign to these rational considerations, however, is highly dependent on their level of deliberative thinking style. Our findings also suggest that a deliberative and not an intuitive thinking style is the crucial predictor of optimal behavior, since this thinking style increases the vigilance of the deliberative system to adjust the intuitive anchor. 123 [726] Synthese (2012) 189:131–145 141 Appendix: the DPQ questionnaire Instructions for participants In the following pages you will be presented with eight everyday life dilemmas. For each dilemma, try to imagine the situation and decide which of the two solutions is preferable. For each dilemma, you will be asked to state your preference by dividing 100 points between the two solutions. Note that there are NO “correct” choices. Please give an honest opinion reflecting your own preferences. Example: a lunch dilemma Imagine you are going out for lunch with a friend. Your friend gives you a choice between two restaurants: Restaurant A Pepper’s pizza Restaurant B Patio Loco Mexican food Divide 100 points between the two packages to express your preference for each package. Remember, the numbers should add up to 100. Number of points Number of points For package A For package B _________ + _________ = 100 The DPQ dilemmas Table 1 Personal preference dilemmas (without a normative solution) Domain Dilemma Description Diversity level Choice alternative Gain Vacation Imagine that for your graduation your parents are giving you a weeklong exotic vacation in the Caribbean islands. The travel agency offers you a choice between two packages (both cost the same) High* Visiting three islands Low Visiting one island [727] 123 142 Synthese (2012) 189:131–145 Table 1 continued Domain Loss Dilemma Description Diversity level Choice alternative Box of candies For the upcoming Chanukah holiday, your company is giving out boxes of candy from four well known companies, and all employees are given a choice between two types of boxes High* Nice 10 × 10 inch wooden box filled with four different types of candy Low Nice 10 × 10 inch wooden box filled with one type of candy Urn which contains 100 balls, four are red, three are blue, three are green and 90 are transparent Online casino An online casino offers you free participation in three lotteries each involving only gains. The free lotteries are offered on condition that you first participate in one lottery involving potential losses. Imagine you agree to the casino’s offer and now have to take part in the loss lottery. You are asked to draw a ball from an urn consisting of transparent balls and colored balls. If the ball you draw is colored (red, blue, or green) you lose $100. If the ball you draw is transparent you lose nothing. You can draw the ball from one of the two urns High Low* Credit Imagine you are going to buy your first house and you need to apply for a mortgage for a large amount of money. Your financial advisor gives you a choice between two financial plans based on three different credit card loans (Visa, Master card, and American Express). All loans have an identical interest rate, and both plans have the same commission and management fee High Low* Urn which contains ten balls, one is red and nine are transparent Divide the amount of money among the three different credit card loans Allocate the entire amount of money to one loan on the Visa credit card * Note asterisks represent the perceived diversity heuristic prediction (Ayal and Zakay 2009) 123 [728] Synthese (2012) 189:131–145 143 Table 2 Dilemmas with normative solution Domain Dilemma Description Gain Imagine you are participating High in a lottery raffle. There are five stacks of lottery tickets. Each ticket has the numbers one to 49. A computer will randomly select five different numbers from this range. In order to win a monetary prize, you need to guess the numbers that will come up. In order to participate in the raffle you need to choose one of the betting methods Low* Lottery tickets (congruent) Funds (incongruent) You inherited a large amount of money from your grandfather. Your financial advisor offers you two financial plans based on different funds (X, Y, and Z) both of which had identical returns last year, and have the same commission and management fee Diversity level High* Low Loss [729] Debts (congruent) Imagine that you have three High* different credit accounts with different balances, each of which has a different annual percentage interest rate (APR): A—debt balance of $4,000 with 2.5 % APR; B—debt balance of $6,000 with 2 % APR; and C—debt balance of $10,000 with 3.5 % APR. Suppose that you have just received a $10,000 government stimulus rebate and that you have decided to use the entire rebate to pay off debt. Please choose one of the following options to pay off your debt Choice alternative Pick five tickets and mark five numbers on each of them. If one of your tickets includes all the five randomly selected numbers, you win Pick one lottery ticket and mark six numbers. If your ticket includes the five randomly selected numbers, you win Diversify the money among the three different funds Invest all the money in one of the three funds Pay off only Debt C 123 144 Synthese (2012) 189:131–145 Table 2 continued Domain Dilemma Transfusion (congruent) Description Imagine you are vacationing with friends in rural Africa. Unfortunately one of your friends is injured in a car crash and needs an urgent blood transfusion. The local clinic offers you a choice of blood from one of two available centers Diversity level Choice alternative Low Pay off both debts A and B A center with a record of three HIV infections, three Malaria infections and three hepatitis C infections for every 10,000 transfusions High* Low A center with a record of one HIV infection for every 1,000 transfusions *Note asterisks represent the normative solution References Acker, F. (2008). New findings on unconscious versus conscious thought in decision making: Additional empirical data and meta-analysis. Judgment and Decision Making, 3, 292–303. Amar, M., Ariely, D., Ayal, S., Cryder, C., & Rick, S. (2011). Winning the battle but losing the war: The Psychology of Debt Management. Journal of Marketing Research, 48, 38–50. Ariely, D. (2008). Predictably irrational. New York: Harper Collins. Ayal, S., & Zakay, D. (2009). The perceived diversity heuristic: The case of pseudodiversity. Journal of Personality and Social Psychology, 96, 559–573. Ayal, S., & Hochman, G. (2009). Ignorance or integration: The cognitive processes underlying choice behavior. Journal of Behavioral Decision Making, 22, 455–474. Ayal, S., Hochman, G., & Zakay, D. (2011). Two sides of the same coin: Information processing style and reverse biases. Judgment and Decision Making, 6, 295–306. Denes-Raj, V., & Epstein, S. (1994). Conflict between intuitive and rational processing: When people behave against their better judgment. Journal of Personality and Social Psychology, 66, 819–829. Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious thought. Perspective on Psychological Science, 1, 95–109. Epstein, S. (1994). An integration of the cognitive and psychodynamic unconscious. American Psychologist, 49, 709–724. Epstein, S. (2007). Intuition from the perspective of cognitive-experiential self-theory. In H. Plessner, C. Betsch, & T. Betsch (Eds.), Intuition in judgment and decision making (pp. 23–37). Mahwah: Lawrence Erlbaum. Evans, J. S. T. B. (2003). In two minds: Dual-process accounts of reasoning. Trends in Cognitive Sciences, 17, 454–460. Fishburn, P. C., & Kochenberger, G. A. (1979). Two-piece von Neumann–Morgenstern utility functions. Decision Sciences, 10, 503–518. Galak, J., Kruger, J., & Loewenstein, G. (2011). Is variety the spice of life? It all depends on the rate of consumption. Judgment and Decision Making, 6, 230–238. Glöckner, A., & Herbold, A. K. (2011). An eye-tracking study on information processing in risky decisions: Evidence for compensatory strategies based on automatic processes. Journal of Behavioral Decision Making, 24, 71–98. Godkin, E. L. (1891). The economic man. The North American Review, 153, 491–503. 123 [730] Synthese (2012) 189:131–145 145 Hedesström, T. M., Svedsäter, H., & Gärling, T. (2006). Covariation neglect among novice investors. Journal of Experimental Psychology, 12, 155–165. Hilbig, B. E. (2008). Individual differences in fast-and-frugal decision making: Neuroticism and the recognition heuristic. Journal of Research in Personality, 42, 1641–1645. Inbar, Y., Cone, J., & Gilovich, T. (2010). People’s intuitions about intuitive insight and intuitive choice. Journal of Personality and Social Psychology, 99, 232–247. Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases (pp. 49–81). New York: Cambridge University Press. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under risk. Econometrica, 47, 263–291. McAlister, L., & Pessemier, E. (1982). A dynamic attribute satiation model of variety seeking behavior. Journal of Consumer Research, 9, 141–150. Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. Journal of Personality and Social Psychology, 76, 972–987. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge: Cambridge University Press. Read, D., & Loewenstein, G. (1995). Diversification bias: Explaining the discrepancy in variety seeking between combined and separated choices. Journal of Experimental Psychology, 1, 34–49. Shiloh, S., Koren, S., & Zakay, D. (2001). Individual differences in compensatory decision-making style and need for closure as correlates of subjective decision complexity and difficulty. Personality and Individual Differences, 30, 699–710. Shiloh, S., Soltan, E., & Sharabi, D. (2002). Individual differences in rational and intuitive thinking styles as predictors of heuristic responses and framing effects. Personality and Individual Differences, 32, 415–429. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 59, 346–353. Simon, H. A. (1973). Organization Man: Rational or self-actualizing?. Public Administration Review, 33, 346–353. Simonson, I. (1990). The effect of purchase quantity and timing on varietyseeking behavior. Journal of Marketing Research, 28, 150–162. Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119, 3–22. Smith, S. M., & Levin, I. P. (1996). Need for cognition and choice framing effects. Journal of Behavioral Decision Making, 9, 283–290. Stanovich, R. E., & West, R. F. (1998). Individual differences in rational thought. Journal of Experimental Psychology, 12, 161–188. Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate?. Behavioral and Brain Sciences, 23, 645–726. Stanovich, K.E., & West, R.F. (2002). Individual differences in reasoning: Implications for the rationality debate?. In T. Gilovich, D.W. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 421–440). New York: Cambridge University Press. Stein, E. (1996). Without good reason: The rationality debate in philosophy and cognitive science. Oxford: Oxford University Press. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Insurance, 59, 297–323. Usher, M., Russo, Z., Weyers, M., Brauner, R., & Zakay, D. (2011). The impact of the mode of thought in complex decisions: Intuitive decisions are better. Frontiers in Psychology, 2, 1–13. Zakay, D. (1990). The role of personal tendencies in the selection of decision making strategies. The Psychological Record, 40, 207–213. [731] 123