EMOTIONAL SELF PREDICTION IN RISKY DECISION MAKING: THE ROLE OF NEGATIVE ASYMETRIES

EMOTIONAL SELF PREDICTION IN RISKY DECISION MAKING: THE ROLE OF NEGATIVE ASYMETRIES Autoria: Gazi Islam, Danny Pimentel Claro, Eduardo B. Andrade ABSTRACT Consumers often incorrectly predict psychological states and behaviors, failing to keep a planned course of action from their earlier predictions. This paper adddresses the extent to which individuals misestimate their own risk taking behavior, believing falsely that they will engage in risk averse behavior after losses. In a series of four experiments, we demonstrate that losses lead to higher than planned bets whereas bets are on average carried over after gains. This implies that participants are more successful at self-prediction after gains than after losses, increasing riskiness after the latter. Such assymetric dynamic inconsistencies emerge when (1) monetary and non-monetary incentivies are used, when participants face (2) fair and unfair gambles, and when the betting decision happens (3) individually and in groups. The reason for the assymetry lies in part on people s inability to predict how much the negative feelings generated by the loss will influence them to bet more than planned in an attempt to restablish a homeostatic state in the prospect of winning. These feelings affect post-planning behavior, while, we argue, they are inaccessible at the pre-decision planning phases. We discuss the implications of our results in terms of the cognition-emotion linkage, negative emotional asymetries and individual versus group decision making. INTRODUCTION Consumers often incorrectly predict psychological states and behaviors, failing to keep a planned course of action (e.g.van Boven & Kane, 2006; Van Boven, Loewenstein & Dunning, 2005). Although they plan to skip dessert, not drink a second glass of wine or not limit their buying behavior, they end up eating (Wardle and Beales, 1988), drinking (Allsop and Saunders 1989) and spending in a supermarket (Gilbert, Gill, and Wilson 2002, Nisbett and Kanouse 1968) more than they had initially planned. Anecdotal evidence suggests that this phenomenon might also be true in gambling environments. We have all overhead stories about those who have lost a blouse, a house, and, perhaps as a result, a spouse on the gambling table. Moreover, given the skyrocket growth of the gambling industry from casinos to internet gambling, the size and scope of the phenomenon may have increased markedly in the past decades. Las Vegas, for example, represents the top tourist destination in the US, with thirty nine million visitors in 2006 (American Gaming Association, 2006). Many of them gambled, and of those who did, most lost. In fact, the gross gaming revenue in Las Vegas mounted to 10.6 billion dollars in the same year, more than a quarter of the total visitor spending. Interestingly, if the anecdotes hold, a significant fraction of gamblers probably bet and eventually lost more than they had anticipated. However, empirical data to support the intuition that non-pathological gamblers (i.e., the great majority of consumers) are actually not good at keeping their plans in a gambling environment is still scant. Although prediction of future states of the self has been shown as problematic in the experimental social psychology literature (e.g. Loewenstein, Hsee, Weber & Welch, 2001), only recently have researchers paid systematic attention at what has been called the dynamic inconsistencies of gambling i.e., people s tendencies to bet differently from what they had originally planned to bet in anticipation of gains and losses. Moreover, as we will describe latter, the patterns of deviations as well as the explanations are not yet conclusive (e.g., Andrade and Iyer 2007, Barkan and Busemeyer 2003). The current paper attempts to fill this 1

lacuna in a series of four experiments, assessing the presence of dynamic inconsistencies in sequential gambling and provide further insights into the potential underlying processes. DYNAMIC INCONSISTENCIES IN GAMBLING That consumers deviate from their plans in a gambling scenario could be explained by non psychological factors, such as lack of experience or information about the environment. For instance, imagine Paul, a consumer who decides to go to Las Vegas for the first time and has put $300 aside to spend in the casinos. As the trip ends, Paul realizes he has spent twice as much and cannot quite figure out why. A simple explanation is that, as a naïve consumer, Paul did not know much about the city, the availability of the games (even at the airport!), the different types of games, the surrounding cues, the free drinks, etc. In other words, at the planning stage Paul did not have enough information about his decision making context. When additional pieces of information about the gambling environment were made available, he changed his initial plans. The rationale above would imply, however, that as information about the characteristics of the gambles and surroundings become available prior to a planning phase, dynamic inconsistencies should disappear. Recent experimental research has shown that this is not necessarily the case. Even in fully controlled environments (labs) where people have sufficient information about the gambles prior to the planning phase, they tend to deviate from the initial plan in sequential gambles (Andrade & Iyer, 2007). However, the pattern of deviations and the accounts provided to explain the inconsistencies have varied in the literature. Reversed Dynamic Inconsistencies and Changes in Reference Points Barkan and Busemeyer (2003) asked participants to plan and play several times a sequence of 2 gambles (the gambles varied in expected value). The first gamble was always mandatory. Participants had to indicate their preferences on whether or not to take the second gamble prior ( If I win the first gamble, I will take/reject the second gamble ) and after the outcome of gamble 1 ( I will take/reject the second gamble ). The authors found that losses and gains did not produce any differences in subsequent bets at the planning phase. In other words, participants seemed to treat the gambles independently at this stage. However, in practice, after gamble 1, participants were more likely to reject gamble 2 after a gain was observed and to take gamble 2 after a loss was experienced, showing reversed dynamic inconsistencies. This behavioral result seems consistent with a line of research in behavioral decision making showing that actors tend to become risk averse in the domain of gains, and risk seeking in the domain of losses (e.g. Khaneman & Tversky, 1979), but adds the important observation that people do not realize that this effect will happen prior to the risk taking episode. The authors argue that when planning a sequence of gambles individuals treat each gamble independently from one another, which leads to no change in reference points (i.e., when planning a second bet, people do not consider the impact of the outcome of the first planned bet). During the actual gambles, however, the previous outcome is integrated into the subsequent decision making process. As a result, shifts in reference points take place due to gains and losses, and deviations from initial plans follow the predictions of prospect theory (e.g. Khaneman & Tversky, 1979). Once losses are experienced in the first gamble individuals become more riskseeking i.e., positive deviations from the plan become more likely in a second gamble. Whereas when gains are experienced in the first gamble, individuals become more risk-averse i.e., negative deviations from the plan become more likely in a second gamble. Such rationale would explain (1) the null effect of a prior outcome on a future bet at the planning phase of the gamble as well as (2) the reversed dynamic inconsistencies at the actual phase of the gamble (see also Barkan et al. 2005). Recently, however, there has been evidence that the patterns of 2

planning and of deviations as well as the potential account for the phenomenon may differ from prior suggestions. Asymmetric Dynamic Inconsistencies and the Hot-Cold Empathy Gap Similar to Barkan and colleagues, Andrade and Iyer (2007) asked participants to plan and act on a sequence of 2 gambles. However, among other procedural differences from the earlier suty, participants were given a choice as to whether and how much to bet in each of the 2 gambles. The results show that within this paradigm two effects emerge. First, at the planning phase, the gambles are not necessarily assessed in isolation. In a series of four studies the authors showed that participants pre-committed to betting less in gamble 2 in anticipation of a loss and the same amount in anticipation of a gain relative to their chosen bet in gamble 1. In other words, while planning a sequence of 2 gambles and allowed to make contingent plans, participants not only took into account a previous outcome, but they actually behaved quite conservatively, planning to betting less when wealth was expected to decline (e.g., If I lose $X in gamble 1, I will bet $X - $Y in gamble 2 ). However, when a loss or a gain was actually experienced, asymmetric dynamic inconsistencies emerged. After a gain was realized, participants bet on average the same amount they had anticipated, whereas, after a loss was observed, they bet more than they had initially planned to bet in anticipation of such loss. Note that this effect emerged even in a scenario where participants had full information about the characteristics of the gamble, they were led to believe that the planned bets would have to be carried over (i.e., pre-commitment), the time delay between planning and actual betting was short, and people were reminded of their planned bet prior to actual betting decisions. The hot-cold empathy gap. To explain the asymmetric dynamic inconsistencies, the authors adopted a hot-cold empathy gap rationale. It has been shown that people in a cold (relatively neutral) state tend to underestimate how their own feelings will influence their future behavior when they experience a hot (strong visceral drive) state (Loewenstein, 1996; Loewenstein and Schkade 1999). Moreover, when deprived of a given resource, the aversive state that is experienced might lead people to react and usually overdo in an attempt to restore a homeostatic state. This would explain, for example, why hungry consumers buy more food than they ve initially planned (Gilbert, Gill, and Wilson 2002; Nisbett and Kanouse 1968), why curious individuals care more about the missing information than they had initially predicted (Loewenstein, Prelec, and Shatto 1996), and why drug users underestimate the impact of craving (Badger et al. 2007). Applied to gambling, it means that people at the planning phase might be underestimating the intensity and impact of post-outcome feelings on their subsequent bets. Precisely, when losses are felt in gamble 1 and participants become deprived of money, positive deviations take place in gamble 2. Since for gains, no sense of deprivation is experienced, no specific pattern of behavior is observed (i.e., both positive and negative deviations are likely). That would explain the asymmetric dynamic inconsistencies (i.e., people betting more than planned after losses, but on average not doing so after gains). Andrade and Iyer (2007) provided initial evidence consistent with this rationale. They showed that positive deviations from the plan after losses were associated a prior underestimation of negative affect (experiment 3). In other words, those who felt worse than they expected were the ones more likely to bet more than planned after a loss. Also, when a pleasant cooling off period was inserted between gambles, it mitigated participants tendency to bet more than planned in gamble 2 after losing gamble 1 (experiment 4). That is, when negative feelings associated with the loss were reduced by a distracting task, so 3

did the positive deviations. All these moderating factors suggest that the behavioral inconsistencies observed are, at least in part, affect driven. OVERVIEW In the current study, we build on Andrade and Iyer s (2007) initial findings to further investigate the presence and shape as well as the role of feelings in dynamic inconsistencies in gambling. Precisely, across four experiments where people are asked to plan and bet in a sequence of 2 gambles, we test (1) whether dynamic inconsistencies hold when a non-monetary incentive is used, (2) whether people can control for the impact of feelings, if any, on deviations from the plan, (3) whether the length of the pre-outcome period modify emotional states, and as result, change the pattern of the dynamic inconsistencies, (4) whether similar inconsistencies can be established at the group level of analysis, when groups instead of individuals are asked to make the decisions. Consequential Incentives. Andrade and Iyer (2007) showed that people reacted to a financial loss and bet more than planned when the loss was experienced. An open question is whether this pattern of results is contrived to monetary losses.(find cite!) Since negative emotions are critical to produce deviations from the plan after losses, we hypothesize that not only a monetary incentive, but other consequential incentives (i.e., incentives meaningful enough to trigger affective reactions) might generate the same pattern of inconsistencies. Experiment 1 addresses this issue. Affective Correction. If negative feelings instigate positive deviations from the plan, can people control for its impact when asked to do so? If the asymmetric dynamic inconsistencies result from the hot-cold empathy gap i.e., the underestimation of post-outcome feelings and the need to restore a homeostatic state (after losing gamble 1), participants might bet more than planned because of their lack of conscious awareness and control of their shift in emotional state. When explicitly instructed to control for the impact of feelings on subsequent behavior, the positive deviations after losses could be mitigated and the asymmetric dynamic inconsistency, consequently, disappear. It requires however, first, that people recognize that their negative feelings are actually leading them to bet more than planned after a loss, and second, that they are capable of controlling the urge to overreact. This proposition is tested in experiment 2. Pre-Outcome Period. In gambling environments, waiting is a common experience, particularly during the time delay between betting and outcome, the so-called the pre-outcome period of the gamble (Pope 1983). Pre-outcomes can take a few seconds (e.g., roulette), a few minutes (e.g., horse race), or a few hours (e.g., sports betting). Importantly, there is robust evidence that arousal builds up during this pre-outcome period (Dickerson and Adcock 1987; Dickerson et al. 1992; Wulfert et al. 2005). Moreover, within a certain range, longer time delays lead to more intensity levels of arousal (Nomikos et al. 1968). If longer time delays increase the intensity of pre-outcome feelings, and if pre-outcome feelings influence the intensity of postoutcome feelings, it is possible that people might feel worse when a longer (vs. shorter) preoutcome period precedes the loss. Moreover, since negative feelings instigate positive deviations from the plan, one would observe stronger (weaker) deviations when participants have experienced a longer (vs. shorter) pre-outcome period even tough the amount lost remains constant across groups. Experiment 3 addresses this issue. Consensual Decision Making. To date, the literature on pre-post planning discrepancies, and on asymmetries in risk taking more generally, has focused on the individual level of analysis (Hastie, 2001). However, an important question in group decision making is whether individual level models are isomorphic across levels, and whether group decision processes show similar 4

patterns to individual processes. Because consumers are also known to bet in groups (Pruitt and Teger 1969), buying lottery tickets together, meeting to bet on their favorite teams, and even playing together in casinos. The open question is whether the asymmetric dynamic inconsistencies will hold when groups must reach a consensus on the bets. Two potential hypotheses are contrasted. Since groups, in some contexts, tend to amplify the pre-existing attitudes of their members, as in the risky shift or group polarization literatures (Pruitt and Teger 1969; Myers, 1978, Myers & Lamm, 1976), and if contagious affect tends to aggregate and converge in group settings (George, 1990, Ilies, Wagner & Morgeson, 2007), the risky tendencies in individuals should also be isomorphic and observable at the group level. However, another possibility is that if risky behavior were over- amplified, then the risk taking tendencies of groups might lead to higher than planned bets for gains and losses alike, thereby eliminating the asymmetry. Experiment 4 addresses this issue. EXPERIMENT 1 In the first experiment we adopt a procedure similar to Andrade and Iyer s. Participants are asked to plan a sequence of bets contingent on winning/losing the previous gamble. Precisely, they plan a bet in gamble 1, and then are asked plan a bet in gamble 2 given either a win or a loss a loss in gamble 1. Within a certain budget, participants are free to decide on whether and how much to bet in each gamble. Then, during the gambles, they are unexpectedly given the opportunity to confirm or revise the planned bets. Contrary to Andrade and Iyer who used a monetary incentive, participants (here, undergraduate students) in this experiment bet course credits they received in exchange for their participation (i.e., a 10% increase in the final exam grade). We hypothesize that with a consequential yet non-monetary incentive the results will replicate Andrade and Iyer s findings and contradict Barkan and colleagues results. Precisely, we expect participants to behave conservatively at the planning phase, betting less in case wealth declines (i.e., after an anticipated loss). Moreover, when an opportunity to revise/confirm the plan is provided, asymmetric dynamic inconsistencies should emerge. That is, participants will bet on average the same planned amount after a gain is experienced but will bet more than planned after a loss is felt. Method Participants and design. Eighty one students participated in this experiment. They were given 1 course credit in exchange for their participation in the experiment. The experiment employed a two (bet 2: planned vs. actual; within) by two (outcome 1: gain vs. loss; between) mixed design. Procedure. The experiment was conducted in a computer-based environment. The cover story stated that the study was about gambling preferences, and that they would be playing a series of two identical and fair gambles. Each gamble offered a 50% chance of doubling the amount bet and had a 50% chance of losing the bet (EV = 0). Participants began the experiment with 1 course credit for their participation in the experiment (which corresponded to 5% increase in the final exam grade). At the beginning of the experiment, however, they were told that the experimenter had received authorization from the school to allow them to use this course credit in the subsequent gambles if they felt like. It was entirely up to them to decide on whether and how much to bet in the following gambles. The 1 course credit was then converted into 100 electronic chips, and participants were told that they could bet any amount from 0 to 50 chips (i.e., 0 to 2.5% increase in the final exam grade) in each 5

of the two gambles. Thus, over the two bets they could potentially increase their grade by up to 10%. The procedure followed three steps: trial, planning, and actual phase. In order to provide participants with information about the gamble, they were first asked to practice the gamble in a trial phase (no betting involved). Then, participants were told that the gamble comprised of two additional phases. During the planning phase, they would have to plan their bets in both gambles. Whatever decision made during the planning phase, they were told, would be carried over (no changes allowed). Participants then bet in gamble 1 and were given the opportunity to make bets in gamble 2 in anticipation of a gain and in anticipation of a loss in gamble 1. Then, the actual phase started. To avoid memory decay effects, they were reminded of the planned bet 1 and were unexpectedly informed that they could either confirm or revise the planned bet. They made the final bet in gamble 1 and then the gamble started. After 15 seconds of flashing in the gambling board the outcome was revealed (see below). They were then reminded of their planned bet in gamble 2 and were asked as in gamble 1 to confirm or revise it. Finally, after a few final questions participants were properly debriefed and thanked for their participation in the study. Gambles. The gambles had the following characteristics. A gambling board consisting of 20 red and 20 blue squares appeared on the screen. A X sign flashed randomly on the board every ½ sec for 15 sec. Each flash was independent of the previous one so that it could flash more than once in the same square. At the end of the fifteen second period, the flashing stopped. If the X sign landed on a blue square, the participant would double the amount bet; otherwise, s/he would lose the bet. The probabilities, payoffs, and the remaining time were displayed on top of the gambling board. To avoid potential objective mistakes, the board was constructed to present visual and easy-to-assess probabilities. Also, the winners were required to raise their hand so the experimenter could double-check each outcome. This procedure was added to simply allow all participants to observe the actual distribution of gains and losses in the room and avoid suspicion of outcome manipulation. Finally, to bring knowledge about this type of gambling to a common real-life baseline, participants also were told at the beginning of the experiment that the probabilities and payouts in the current gamble presented a slightly better deal compared to the black/red options in the American roulette (which offers a 47.4% of doubling the amount bet) (cite!). Results Planning phase. Participants planned the betting amount in gamble 1, and then, in gamble 2 in anticipation of a gain and in anticipation of a loss. The results showed that previous outcomes influenced subsequent planned bets (F(2, 79) = 4.08, p <.05). Planned bets in gamble 2 were lower in anticipation of a loss (vs. gain) in gamble 1 (M L = 24.9 vs. M G = 28.7; F(1, 80) = 3.96, p =.05). Moreover, compared to their planned bets in gamble 1 (M = 28.2), participants reported lower planned bets in gamble 2 after an anticipated loss in the previous gamble (F(1, 80) = 8.25, p <.01), but reported similar planned bets in gamble 2 after an anticipated gain in the previous gamble (F(1, 80) =.10, p >.10). In other words, at the planning phase, losses discouraged participants from betting in subsequent gambles, whereas gains had no effect on bets placed on subsequent gambles. Remember that in the planning stage participants were led to believe that the planned bets would be carried over. Therefore, these results do not represent instances of intentions, but actual forward looking behavior based on anticipated contingencies. Actual phase. In gamble 2, a significant interaction emerged between betting phase (planned vs. actual) and the outcome of gamble 1 (F(1, 79) = 4.85, p <.05). Participants who 6

won gamble 1, bet on average the same amount they had initially planned in anticipation of such gain (M p = 29.0 vs. M a = 28.7; F(1, 79) =.02, p >.10). Participants who lost gamble 1, however, bet on average more than they had initially planned to bet in anticipation of such loss (M p = 23.2 vs. M a = 30.2; F(1, 79) = 7.96, p <.01). Frequency of deviations. An open question is whether the results displayed above are a function of magnitude only (i.e., a few people bet a lot more after losing) or if the frequency of positive and negative deviations also changed as a function of outcome. Sixty percent of participants deviated from the plan. Within this group, preference for positive (61.5%) versus negative deviations did not differ from chance after a gain (z = 1.17, p >.10), whereas preference for positive deviations (78.3 %) was significantly greater than chance after a loss was experienced (z = 2.71, p <.005). The frequency of deviations in gamble 2, therefore, seemed contingent on the outcome of the gamble, although a Chi-square analysis did not reach significance (χ 2 (1; N=49) = 1.60, p =.20). Discussion Experiment 1 produced several initial findings. First, at the planning phase, individuals chose to bet less after an anticipated loss compared to an anticipated gain and compared to a previous bet. This effect shows that individuals do not necessarily disregard the previous outcome when planning the next gamble, but simply believe that losses will affect their behavior in a conservative manner i.e., spend less as wealth declines. Second, asymmetric dynamic inconsistencies emerged at gamble 2 when planned and actual bets are contrasted. For losses, participants bet on average more than they had initially planned, whereas, for gains, planned bets were on average carried over. This effect results from the fact that positive deviations became by far the dominating option after losses, whereas positive and negative deviations were as likely after gains. These findings, at least within this paradigm, are inconsistent with the rationale that in a sequence of gambles people make isolated planned bets, and integrated actual bets. Unlike in the work of Barkan and colleagues, at the planning phase previous outcomes did influence subsequent bets in our experiment. It is again worth noting, however, that in Barkan and Busemeyer s procedure participants (a) were forced to take gamble 1 and (b) had to decide on taking versus not taking gamble 2 (dichotomous variable). Our experiment used a more general procedure which allowed a full possible range of bet choices and no restrictions on whether or not to take the initial gamble. In other words, as in most real gambling scenarios, participants were free to decide on whether and how much they would want to bet in both gambles. Our results replicate Andrade and Iyer s findings with a non-monetary incentive. In other words, whether betting with money or course credit the pattern of results does not change. These findings are consistent with the logic that, ceteris paribus, as long as the incentives are consequential and therefore produce affective reactions, asymmetric dynamic inconsistencies might emerge. There are at least two major concerns. First, the gambles presented null expected values and a 50% chance of winning. Although it certainly facilitated participants straightforward assessment and computation of probabilities and payouts, such gamble structure seems at odds with what is observed in the real world. Would the same asymmetric effects be observed if the gamble displayed negative expected values and lower than 50% chance of winning? Since and Andrade and Iyer also restricted their studies to fair gambles, this question is yet to be addressed. Second, although evidence of asymmetric dynamic inconsistencies is consistent with a hot-cold empathy gap rationale, evidence for the impact feelings on betting decisions cannot be observed 7

from experiment 1. Also, if feelings do matter, we wonder whether people would be capable of correcting for their impact when explicitly asked to do so. Experiment 2 tackles these issues. EXPERIMENT 2 This experiment adopted a procedure similar to the one used in experiment 1, except for the following changes. First, each of the two gambles offered a 47.5% chance of doubling the amount bet (EV<0) and a 52.5% of losing the amount bet. Also, participants were allowed to use up to $10 out of the $15 participation fee i in the gambles if they felt like. Precisely, they could bet from $0 to $5 per gamble. As in experiment 1, the incentives were converted into 100 chips (from 0 to 50 chips per gamble). Second, an affect-based judgment correction (hereafter, affective correction) was implemented. Just before the actual bet in gamble 2, half of participants were instructed to make sure that potential feelings generated by the previous gamble, if any, would not influence the betting decision in gamble 2. Notice that this manipulation goes beyond simply highlighting the source of feelings (Schwarz and Clore 1983). Given that normative assessments about the role of feelings in gambling might differ, simply pointing out the presence of affect could produce varying effects. For instance, it is possible that some people, even while recognizing their negative emotions, might not feel it inappropriate to use such feelings to make decisions about subsequent bets. To avoid variance in normative judgments about the role of feelings in decision making, the affective correction manipulation was meant to assure that if feelings were playing a role in participants decisions, they should be corrected. Note that for a correction to take place participants must have a lay theory of about the direction of the effect, believe that it might be happening to them, and be able to control for its impact. Results Three participants bet more than the amount allowed per gamble and were deleted from the sample. Planning phase. Moreover, compared to their planned bets in gamble 1 (M = 23.8), participants reported lower planned bets in gamble 2 after an anticipated loss in the previous gamble (F(1, 178) = 15.87, p <.001), but reported similar planned bets in gamble 2 after an anticipated gain in the previous gamble (F(1, 178) =.02, p >.10). In short, the results replicate the findings in experiment 1 using gambles with negative expected value actual monetary incentives. When compared either to gains or to a previous bet, losses in the planning phase reduced betting amounts in subsequent gambles. Actual phase. To test the impact of feelings on dynamic inconsistencies, we assessed the interaction between betting phase (planned vs. actual) and previous outcomes (gain vs. loss) in gamble 2 when participants were either not provided with any additional instructions (control condition) or instructed to avoid using their feelings prior to bet 2 (affective correction condition). A significant interaction emerged in the control condition (F(1, 95) = 5.46, p <.05). For gains, there was no significant deviation from the plan. Participants who won gamble 1 bet on average the same amount they had previously planned to in anticipation of such gain (M p = 22.8 vs. M a = 21.5; F(1, 95) =.60, p >.10). However, there was a significant deviation from the plan for losses. Participants who lost gamble 1 bet on average more than they had previously planned to bet in anticipation of such loss (M p = 19.3 vs. M a = 23.3; F(1, 95) = 7.1, p <.01). Thus, in the control condition, the interaction replicates the findings of experiment 1 using unfair gambles, negative expected value, and actual monetary incentives. When participants were asked to correct for potential affect-based judgments just before actual bet 2, the interaction between betting phase and outcome disappeared (F(1, 80) =.12, p <.10). Consistent with the predictions, pairwise comparisons showed that the absence of the 8

interaction was driven mainly by a reduction in the deviations from the plan in the loss conditions. When participants were asked to avoid using their feelings at the actual phase in gamble 2, there was no difference between a bet in anticipation of a loss (M p = 18.8) and a bet after the loss was realized (M a = 19.9; F(1, 80) =.75, p >.10). Also important, the affective correction manipulation had no influence in participants betting patterns after a gain. Participants who won gamble 1 bet on average the same amount that they had initially planned to bet in anticipation of such gain (M p = 25.7 vs. M a = 26.2; F(1, 80) =.19, p >.10). This is important as it shows that the manipulation produced localized effects as a function of outcome in the first gamble, and not simply a general drop in betting patterns at gamble 2. A marginally significant three-way interaction emerged among betting phase (planned vs. actual), outcome 1 (gain vs. loss), and affective correction (yes vs. no) on betting patterns in gamble 2 (F(1, 175) = 2.74, p <.10). Frequency of deviations. Thirty-eight percent of participants deviated from the plan in gamble 2. An open question is whether, within the loss conditions, the affective correction manipulation reduced the magnitude and/or frequency of positive deviations from the plan. Within the control condition, a chi-square analysis was conducted to assess if the distribution of positive and negative deviations was contingent on the outcome of gamble 1. The results yield a significant interaction (χ 2 (1; N=37) = 6.13, p =.01). After a gain, positive (50%) and negative deviations were as likely (z = 0, p >.10), whereas after a loss preference for positive deviations (88.2%) was significantly greater than chance (z = 3.15, p <.001). Within the affective correction condition, the impact of previous outcome on type of deviation was mitigated (χ 2 (1; N=32) = 2.74, p =.10). However, the effect was not necessarily eliminated. After a loss, preference for positive deviations (75%) was still greater than chance (z = 1.73, p <.05), whereas after a gain, positive (55%) and negative deviations were again as frequent (z =.41, p >.10). Therefore, it seems that the affective correction did not necessarily eliminate the positive deviations after losses as much as it reduced its magnitude. Magnitude of deviations. To provide a clearer assessment of the impact of the affective correction manipulation on the magnitude of the deviations, we selected the 69 participants who deviated (positively or negatively) from the plan and conducted an analysis of variance with outcome 1 (gain vs. loss) and affective correction (yes vs. no) as the two factors and change at gamble 2 (i.e., actual bet 2 (-) planned bet 2) as the main dependent variable. The results yield a marginally significant interaction (F(1, 65) = 3.62, p =.06). Most importantly, the interaction was mainly driven by a clear reduction in the magnitude of positive deviations within the loss domain. When participants were not told to control their emotions during bet 2 (control condition), positive deviations averaged 12.9 chips. However, when asked to correct potential affect-based judgments prior to bet 2, positive deviations reduced to 3.2 chips (F(1, 65) = 3.17, p = 08). After a gain, the affective correction had no impact on the magnitude of the deviations (M control = -2.8 vs. M affective correction = 1.1; F(1, 65) =.69, p <.10). Similarly, within the control condition, there was a significant impact of outcome 1 on the magnitude of deviations in gamble 2 (F(1, 65) = 10.74, p <.01). This difference disappeared within the affective correction condition (F(1, 65) =.16, p >.10). Discussion This second experiment makes three additional contributions. First, it shows that the main findings of the previous experiment replicate in a scenario where the gambles are unfair, the EV is negative, and there are monetary consequences. Once again, at the planning phase, participants bets were lower after anticipated losses (vs. anticipated gains and vs. previous bets). 9

Also, at the actual phase in the control condition, participants bet on average more than they had initially planned to bet after a loss, but showed no differences after a gain. In other words, it seems that within this paradigm, as long as the gambles are consequential (monetary or not) and as a result trigger affective reactions, asymmetric dynamic consistencies emerge. Second, the affective correction manipulation mitigated the magnitude of deviations after an anticipated versus actual loss. This suggests, consistent with a hot-cold empathy gap account, that the attempt to reduce an aversive state contributed to over betting in gamble 2 after participants experienced a loss in gamble 1. Third???Also, there was no difference between planned and actual bets for gains, and the affective correction, as expected, did not produce any effects. This null effect is also an important demonstration that the manipulation did not reduce betting patterns in general. Instead, it produced localized effects based on the directional impact of feelings on betting after a loss. In other words, participants seem to (1) acknowledge and (2) be able to correct for the impact of negative feelings on betting after a loss by reducing the extent of positive deviations from the planned bet. Since positive feelings (i.e., after a gain) did not produce significant effects, the affective correction had no impact on the extent of the deviations. A potential criticism toward experiment 2 is the use of an exogenous and heavy-handed manipulation (i.e., affective correction). Moreover, the manipulation does not change people s current states as much as it controls for the impact of these states. In this third study, we address these concerns by focusing on an endogenous manipulation of affect. EXPERIMENT 3 The procedure followed similar to the one used in experiment 1, with two main exceptions. First, participants were asked to report their feelings on a 101 point scale ( Right now, I feel. [0=very bad; 50=neutral; 100=very good; any number from 0 to 100 is allowed] ). Precisely, they were asked to report (1) their current feelings just prior to the betting decision at gamble 1, (2) their expected feelings after winning and after losing their chosen bet, and (3) their actual feelings after the gain or loss was observed. Second, a pre-outcome time delay manipulation was introduced. The flashing period in the gambling board lasted either 3 seconds (short pre-outcome period) or 21 seconds (long pre-outcome period). Note that participants would be exposed to the same time delay on the trial (where information about the gamble is provided) and actual phases of the gamble. Thus, there would not be any information missing about the characteristics of the gamble across the two conditions. Results Planning phase. Participants planned the betting amount in gamble 1, and then, in gamble 2 in anticipation of a gain and in anticipation of a loss. The results showed that previous outcomes influenced subsequent planned bets (F(2, 114) = 4.80, p =.01). Planned bets in gamble 2 were lower in anticipation of a loss (M L = 33.6) and in anticipation of a gain (M G = 33.9) compared to their planned bets in gamble 1 (M = 36.8; F(1, 115) = 6.07, p <.05 and F(1, 115) = 5.03, p <.05, respectively). Planned bets in gamble 2 did not differ from one another when participants anticipated a gain versus a loss (F(1, 115) =.02, p >.10). In other words, at the planning phase, losses and gains discouraged participants from betting in subsequent gambles, showing very conservative planned behavior. Actual phase. First, the betting patterns in gamble 1 did not vary as a function of preoutcome period. Those in the short pre-outcome period condition bet, on average, the same amount (M = 38.6) as those in the long pre-outcome period condition (M = 34.7; F(1, 114) = 1.88, p >.10). This allowed us to compare across pre-outcome conditions. 10

In gamble 2, the results varied as a function of the length of the pre-outcome period. Within the long pre-outcome period condition, an interaction emerged between outcome and betting (F(1, 52) = 6.27, p <.05). Participants who won gamble 1, bet, on average, the same amount they had initially planned in anticipation of such gain (M p = 33.7 vs. M a = 31.4; F(1, 52) =.62, p >.10). Participants who lost gamble 1, however, bet more than they had previously planned in anticipation of such loss (M p = 33.0 vs. M a = 40.2; F(1, 52) = 8.55, p =.005). However, when the pre-outcome period was reduced to 3 seconds only, the interaction between outcome and betting phase disappeared (F(1, 60) =.01, p >.10). Participants who won gamble 1 bet on average the same amount they had initially planned in anticipation of such gain (M p = 32.5 vs. M a = 35.4; F(1, 60) = 1.78, p >.10). Moreover, participants who lost gamble 1 also bet on average the same amount they had previously planned to bet in anticipation of such loss (M p = 40.0 vs. M a = 43.4; F(1, 60) = 1.64, p >.10). A marginal three-way interaction emerged among betting phase (planned vs. actual), outcome of gamble 1 (gain vs. loss) and length of the preoutcome period (3 sec vs. 21 sec; F(1, 112) = 3.27, p =.07). Frequency of deviations. A similar analysis was conducted on the frequency of deviations. Thirty percent of participants deviated from the plan in the long pre-outcome period condition and the frequency of deviations in gamble 2 was contingent on the outcome of gamble 1 (χ 2 (1; N=16) = 4.75, p <.05). Within this group, positive (60%) and negative deviations were as frequent after a gain (z =.44, p >.10), whereas preference for positive deviations (91%) was significantly greater than chance after a loss was experienced (z = 2.71, p <.01). In the short preoutcome period condition, thirty one percent of participants deviated from the plan. However, within this condition, the frequency of deviations in gamble 2 was not contingent on the outcome of gamble 1(χ 2 (1; N=19) =.71, p >.10). (Mis)estimation of affect. The subsequent analyses are based on the difference between predicted affective changes and experienced affective changes. First, we hypothesized that after waiting a long (vs. short) period for the outcome to be revealed would influence the affective intensity. Indeed, in the long pre-outcome period condition, people not only felt worse than they have anticipated prior to the loss (M pc_long = -44.5 vs. M ec_long = -38.7, F(1, 30) = 8.12, p <.01) but they also felt worse than those in the short pre-outcome period condition (M ec_short = -20.3, F(1, 55) = 10.01, p <.01). Interestingly, this pattern emerged despite the fact that, on average, participants in the long pre-outcome period condition lost in gamble 1 less (M = 35.5) than those in the short pre-outcome period condition (M = 42.9; F(1, 55) = 4.33, p <. 05). There was no significant difference between expected and experienced negative feelings in the short preoutcome period condition (M pc_short = -15.6 F(1, 25) =.63, p >.10). After a gain, there was, on average, no evidence of misestimation of positive feeling in either the long (M pc_long = 30.6 vs. M ec_long = 29.2, F(1, 22) =.18, p >.10) or in the short (M pc_short = 17.1 vs. M ec_short = 21.5, F(1, 35) = 1.82, p >.10) pre-outcome period condition. Moreover, those who waited 3 seconds for the outcome to be revealed felt as happy as those who waited 21 seconds (F(1, 57) =.80, p >.10). On average, participants in the long pre-outcome period condition won in gamble 1 the same amount (M = 33.5) as those in the short pre-outcome period condition (M = 35.7; F(1, 57) =.21, p <.10). In short, participants who waited 21 seconds for the outcome to be revealed felt significantly worse than they had anticipated when a loss was eventually experienced. They also felt worse than those who waited only 3 seconds before the outcome was observed, despite the fact that those in the long per-outcome period condition lost fewer chips. For gains, participants did not on average misestimate their on feelings in either the short or long pre-outcome period 11

conditions. Moreover, winners in the long pre-outcome period condition also did not feel better than those in the short pre-outcome period condition. Positive deviations and (mis)estimations of affect. We argue, in line with a hot-cold empathy gap that positive deviations after losses might be due in part to people s tendency to underestimate of the intensity and impact of negative feelings on positive deviations from the plan. To assess this rationale, we tested a potential association between type of affect (mis)estimation (underestimation vs. accurate estimation vs. overestimation) and length of the pre-outcome period (short vs. long). Consistent with our intuitions, after losses, positive deviations were significantly more frequent in the long pre-outcome-underestimation of affect cell than in the other conditions (expected frequency = 16.6%; observed frequency = 50%; χ 2 (2; N=14) = 6.04, p <.05). Also, this association was contrived to the loss condition. Among those who won gamble 1, there was no association between type of affect (mis)estimation and length of the pre-outcome period on frequency of positive deviations (χ 2 (2; N=12) = 2.40, p >.10). In short, an experienced loss after a long pre-outcome period not only increased the frequency of underestimation of affect, but the underestimation of affect was also associated with more positive deviations from the plan. DISCUSSION Experiment 3 shows that the length of the pre-outcome period can modify people s negative feelings after the outcome. Within a certain range, a long (vs. short) time delay between bet and outcome led to stronger than expected negative feelings after the loss was realized. Also, participants felt worse when the loss was preceded by a long (vs. short) pre-outcome periods, despite the fact that those in the long pre-outcome period condition lost on average fewer chips compared to those in the short pre-outcome period condition. Moreover, consistent with a hotcold empathy gap reasoning, underestimation of affect after a loss was positively correlated with frequency of positive deviations from the plan. In other words, those who experienced strong and worse than expected negative feelings (i.e., the underestimation-long pre-outcome period cell) were the ones more likely to bet more than planned once a loss was felt. In the final experiment we further assess the robustness of asymmetric dynamic inconsistencies by testing the impact of consensual decision making. Since risk taking behavior is often done by groups as well as individuals, would the same pattern of results found in the three previous experiment replicate when a group consensus on the betting amount has to be reached beforehand? Experiment 4 addresses this topic. EXPERIMENT 4 Participants were randomly teamed up in groups of 3 and each group had access to one laptop only. The rules of the gamble as well as the trial, planning and actual phases were identical to the ones used in previous experiments. The major difference was that the betting decisions at the planning and actual phases had to be given by the group, and their impacts would be felt by each individual equally. In other words, each individual had his/her on budget (0 to 50 chips per gamble) but had to jointly decide as a group on the bets at the planning and actual phases. For example, if the group decided to bet 30 chips in gamble 1 and lost it, each member would lose 30 out of their own 50 chips. Individual reporting of feelings. Since the impact of gains and losses would be individually felt, it was also possible to assess affective forecasting. Thus, as in the previous experiment each member of the group was asked to report their current, anticipated, and postoutcome feelings. The same scale used in experiment 3 was used in this experiment. The individual affective forecasting would be then average to form the group affective forecasting. 12

Results Planning phase. The first question was whether group planning would be as conservative as the individual planning observed in the previous experiments. Participants planned the betting amount in gamble 1, and then, in gamble 2 in anticipation of a gain and in anticipation of a loss. The results showed that contrary to the preceding experiments, on average, the previous outcome did influence subsequent planned bets (F(2, 64) =.26, p >.10). In other words, groups were not behaving as conservatively as individuals did. Planned bets in gamble 2 were the same in anticipation of a loss (M L = 34.7) and in anticipation of a gain (M G = 34.5) compared to their planned bets in gamble 1 (M = 35.6; F(1, 65) =.21, p >.10 and F(1, 65) =.31, p >.10, respectively). Planned bets in gamble 2 did not differ from one another when participants anticipated a gain versus a loss (F(1, 65) =.01, p >.10). Actual phase. Interestingly enough, a different pattern of behavior at the planning phase did not interfere with the pattern of behavior in the actual phase of the gamble. Like in the previous experiments, in gamble 2 there was an interaction between outcome of gamble 1 and betting phase (F(1, 64) = 3.83, p =.05). Groups who won gamble 1, bet on average the same amount they had initially planned in anticipation of such gain (M p = 34.8 vs. M a = 35.9; F(1, 64) =.52, p >.10). Groups who lost gamble 1, however, bet on average more than they had previously planned to bet in anticipation of such loss (M p = 36.8 vs. M a = 42.3; F(1, 64) =.41, p =.001). Frequency of deviations. Twenty six percent of groups deviated from the plan. Within this sample, positive (67%) and negative deviations were as frequent after a gain (z =.83, p >.10), whereas preference for positive deviations (91%) was significantly greater than chance after a loss was experienced (z = 2.72, p <.01). The frequency of deviations in gamble 2, therefore, seemed contingent on the outcome of the gamble, although a chi-square analysis did not reach significance (χ 2 (1; N=17) =1.57, p =.21). Misestimation of affect. Group s feelings were obtained by averaging individuals feelings. The subsequent analyses are based on the difference between the group s predicted and experienced affective changes. Similar to experiment 3, the result show that whereas the groups accurately predicted their feelings after a gain (M pc = 22.9 vs. M ec 21.6, F(1, 64) =.16, p >.10) they felt significantly worse than they had anticipated to feel as a result of a loss (M pc = -33.5 vs. M ec = -41.9, F(1, 64) = 7.80, p <.01). A marginal interaction between the outcome of the gamble and the affective measure (predicted vs. experienced) also emerged (F(1, 64) = 2.74, p =.10). Positive deviations and misestimations of affect. The open question is whether those groups who positively deviated from the plan after a loss were more likely to have underestimated their negative feelings in the first place. The results confirmed this intuition. Ten groups bet more than planned after a loss. Out of those, 80% had underestimated their negative feelings prior to the betting decision in gamble 2, a number significantly greater than chance (z = 1.90, p <.05). In line with our hypothesis, the number of groups who bet more than planned after a gain was quite small (N=4) and half of those had underestimated their positive feelings whereas the other half had actually overestimated them. Discussion This final experiment demonstrates that the assymetric dynamic inconsistencies apply to consensual decision making as well. When participants in groups of 3 were required to jointly decide on the amount bet, they bet more than planned after a loss and, on average, carry over the planned bets after a gain. Note that this pattern emerged despite the fact that the groups did not plan to behave conservatively (i.e., anticipate to bet less after a loss). In other words, the fact 13

people bet more than planned after a loss seems to not be confined to scenarios where in the planning phase they anticipate betting less after a loss. Furthermore, in groups as in individuals, the results suggest that this effect is linked to a misestimation of affect after a loss, with groups underestimating their own aggregate negative affect after losing a gamble.. Importantly, such underestimation of negative feelings was associated with positive deviations from the plan. The groups which felt worse than expected were the ones more likely to bet more than planned. No misestimation of positive feelings emerged. Moreover, there was no association between misestimations and type of deviations for those groups which won gamble 1. GENERAL DISCUSSION Consumers misestimate betting behavior. Even in a scenario where participants have full information about the characteristics of the gamble prior to a planning phase, losses lead to higher than planned bets whereas bets are on average carried over after gains. Our result substantiate Andrade and Iyer s initial findings and shed further light into the underlying processes and the impact of negative feelings. In a series of 4 experiment, we show that, within this paradigm, assymetric dynamic inconsistencies are particularly robust. They emerge when (1) monetary and non-monetary incentivies are used, when participants face (2) fair and unfair gambles, and when the betting decision happens (3) individually or in groups. The reason for the assymetry lies on people s inability to predict how much the negative feelings generated by the loss will influence them to bet more than planned in an attempt to restablish a homeostatic state in the prospect of winning. Since for gains, there is no sense of deprivation, no specific pattern of deviation becomes dominant. Consistent his this rationale, Experiment 2 shows that when partiticipants are asked to correct for the impact of feelings on subsequent bets, the asymmetry dissipates. Experiment 3 demonstrates that a very short pre-outcome period alleviates the negativity associated with the loss, and hence, reduces people s decision to bet more than planned after a loss is felt. In short, it seems that negative feelings attached the losses are critical to trigger overreactions in an attempt to elimate the unpleasant feeling. Our results are also in line with recent propositions that loss aversion is in fact a negative emotion (e.g., fear), which might explain why people tend to overreact to it (Camerer 2005). LIMITATIONS AND FUTURE RESEARCH However, a few lmitations and open questions remain. One might argue that our results emerge because our sample (undergrad students) is relatively naïve. More experienced gamblers would not make the same mistake. Indeed, it is an interesting question whether learning can help mitigate the asymmetric dynamic inconsistencies. In other words, can someone overtime learn when to stop after a loss is experienced? Our experiment 2 shows that people are capable of recognizing the impact of negative feelings on future bets and are able to correct for it. Thus, on the one hand, one might speculate that learning will reduce dynamic inconsistencies as long as people realize during the process that they are underestimating the intensity and impact of their negative feelings on future betting decisions. On the other hand, the fact that people have in general a limited capacity of anticiapting actual feelings, might make it difficult to learn about its future impact. This is certainly an important question to be addressed in the literature. Another potential criticism to our gambling paradigm, and to most lab experiments, is the lack of external validity. We suspect, however, that the effects we observed in the lab actually be conservative relative to what might be seen in the real world. A clear implication of our findigns is that endogenous and exogenous manipulation of feelings influence betting patterns indenpendently of actual changes in probabilities and pay outs. In our third experiment, 14

we have showed this by varying the amount of time people waited for the outcome to be revealed. Longer (vs. shorter) time delay produced stronger affective reactions, and more deviations from the plan after losses. Multiple other cues might and have been used in the gaming environments. From noisy machines to free drinks, Casinos adopt several tactics to instigate people s feelings during the games. Therefore, it is not unreasonable to suggest that in actual gambling environments the gap between cold (netural feelings at planning) and hot states (actual feelings when losses are felt) might be even wider than in our labs. As a result, stronger deviations from the plan could materialize. According to the american gaming association, consumer spending in commercial casinos alone went from $17.10 billion in 1996 to $32.42 billion in 2006. How much of this amount represents deviations from the plan is far from clear. However, if our results can at least in part be extrapolated, it seems that a signficant proportion of consumer spending in casinos may actually represent unplanned (uncontrolled?) behavior. To the extent that the unplanned expenditures capture a significant chunck of one s discretionary income, our research also raises public policy questions about potential negative impact of gaming even among non-pathological gamblers (i.e., the vast majority of consumers). Future research should further address this issue. REFERENCES Allsop, S. and B. Saunders (1989), Relapse and Alcohol Problems. In Relapse and Addictive Behaviour. Ed. M. Gossop (pp. 11-40). New York: Tavistokc/Routledge. Andrade, Eduardo B. & Ganesh Iyer (2007), Dynamic Inconsistencies in Gambling and the Role of Feelings, Working Paper. University of California, Berkeley. Badger, Gary J., Warren K. Bickel, Louis A. Giordano, Eric A. Jacobs, George Loewenstein, and Lisa Marsch (2007). Altered States: The Impact of Immediate Craving on the Valuation of Current and Future Opioids. Journal of Health Economics, 26 (September), 865-876. Barkan, Rachel and Jerome R. Busemeyer (2003), Modeling Dynamic Inconsistency with a Changing Reference Point, Journal of Behavioral Decision Making, 16 (May), 235-255. Barkan, Rachel, Shai Danzinger, Guy Ben-Bashat, and Jemore R. Busemeyer (2005), Framing Reference Points: The Effect of Integration and Segregation on Dynamic Inconsistency, Journal of Behavioral Decision Making, 18, 213-226. Camerer, Colin (2005). Three Cheers Psychological, Theoretical, Empirical for Loss Aversion, Journal of Marketing Research, XLII (May), 129-133. Dickerson, Mark, John Hinchy, Stephanie L. England, John Fabre, and Ross Cunningham (1992), On the determinants of persistent gambling behaviour. I. High-frequency poker machine players, British Journal of Psychology, 83 (May), 237-248. Elfenbein, H. A., Marsh, A., & Ambady, N. (2002). Emotional intelligence and the recognition of emotion from the face. In L. F. Barrett & P. Salovey (Eds.), The wisdom of feelings: Processes underlying emotional intelligence (pp. 37 59). New York: Guilford. Gilbert, Daniel T., Michael J. Gill, and Timothy D. Wilson (2002), The Future is Now: Temporal Correction in Affective Forecasting, Organizational Behavior and Human Decision Processes, 88 (May), 430-444. Hackman JR, ed. 1990. Groups That Work andthose That Don t. San Francisco: Jossey-Bass Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1994). Emotional contagion.new York: Cambridge University Press. Hess, U., Banse, R., & Kappas, A. (1995). The intensity of facial expression is determined by underlying affective state and social situation.journal of Personality and Social Psychology, 69, 280 288. 15

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