Psychon Bull Rev DOI 10.3758/s13423-014-0688-0

BRIEF REPORT

Does ambiguity aversion influence the framing effect during decision making? Anaïs Osmont & Mathieu Cassotti & Marine Agogué & Olivier Houdé & Sylvain Moutier

# Psychonomic Society, Inc. 2014

Abstract Decision-makers present a systematic tendency to avoid ambiguous options for which the level of risk is unknown. This ambiguity aversion is one of the most striking decision-making biases. Given that human choices strongly depend on the options’ presentation, the purpose of the present study was to examine whether ambiguity aversion influences the framing effect during decision making. We designed a new financial decision-making task involving the manipulation of both frame and uncertainty levels. Thirty-seven participants had to choose between a sure option and a gamble depicting either clear or ambiguous probabilities. The results revealed a clear preference for the sure option in the ambiguity condition regardless of frame. However, participants presented a framing effect in both the risk and ambiguity conditions. Indeed, the framing effect was bidirectional in the risk condition and unidirectional in the ambiguity condition given that it did not involve preference reversal but only a more extreme choice tendency. Keywords Ambiguity aversion . Framing effect . Loss aversion . Decision-making under ambiguity . Decision-making under risk A. Osmont and M. Cassotti contributed equally to this article. A. Osmont : M. Cassotti (*) : O. Houdé : S. Moutier Laboratory for the Psychology of Child Development and Education, CNRS UMR 8240, Paris Descartes University, Sorbonne Paris Cité, Laboratoire A. Binet, 46 rue Saint Jacques, 75005 Paris, France e-mail: [email protected] A. Osmont : M. Cassotti : O. Houdé : S. Moutier Caen University, Caen, France M. Agogué Centre de Gestion Scientifique, Mines ParisTech, Paris, France O. Houdé Institut Universitaire de France, Paris, France

Introduction In most situations of everyday life, people make decisions in conditions where some information about the probabilities of the potential outcomes is lacking. Typically, theorists have distinguished between these situations of decision making under ambiguity and situations of decision making under risk in the context of when decision makers can assign a probability of occurrence to each of the outcomes (Ho, Keller, & Keltika, 2002; Loewenstein, Rick, & Cohen, 2008; Rubaltelli, Rumiati, & Slovic, 2010; Smith, Dickhaut, McCabe, & Pardo, 2002; Tymula et al., 2012). Previous works converged in showing that decision making often is biased by intuitive heuristics (Cassotti et al., 2012; De Neys, 2012; De Martino, Kumaran, Seymour, & Dolan, 2006; Kahneman & Frederick, 2007). The phenomenon of ambiguity aversion is one of the most striking decision biases in which people show a systematic tendency to avoid options for which the level of risk is unknown (Camerer & Weber, 1992; Ellsberg, 1961; Keren & Gerritsen, 1999; Pulford & Colman, 2008). Ambiguity aversion was first illustrated by the Ellsberg paradox (Ellsberg, 1961). In this paradigm, two urns were placed in front of the participant: urn A, containing 50 red and 50 black balls; and urn B, containing an unknown ratio of 100 balls, either red or black. The decision maker must choose between the two urns knowing that he will be paid $10 if he draws a ball of a specified color. The results showed that participants preferred the risky urn associated with clear probabilities (urn A) over the ambiguous one (urn B), regardless of the winning color. This ambiguity aversion effect has been replicated using different types of paradigms, and the phenomenon has been confirmed even when the ambiguous option was mathematically more advantageous (Keren & Gerritsen, 1999). To explain such decision biases, dual-processes theories have postulated the existence of two distinct systems of

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thinking (Cassotti et al., 2012; De Neys, 2006, 2012; Evans, 2010; Kahneman, 2003; Kahneman & Frederick, 2007). Thus, decision-making results from the interplay of an intuitiveheuristic System 1 and a deliberate-analytic System 2. According to these models, ambiguity aversion may arise from an affective and intuitive heuristic belonging to System 1, leading the decision makers to consider issues with a lack of critical information as dangerous. In accordance with this affective heuristic hypothesis, functional magnetic resonance imaging (fMRI) studies revealed that ambiguity level detection involved activities of emotionrelated brain regions, such as the amygdala and the orbitofrontal cortex (Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005; Huettel, Stowe, Gordon, Warner, & Platt, 2006; see also Levy, Snell, Nelson, Rustichini, & Glimcher, 2010). These results suggest that a neural circuit for ambiguity evaluation would systematically alert organisms about a lack of information to avoid the potential costs associated with high-uncertainty situations. In addition, using attractiveness and feelings ratings, Rubaltelli et al. (2010) recently confirmed that ambiguity aversion depends on participants’ affective reactions. Although these results suggest that ambiguity aversion is a robust affective bias, few studies have examined how ambiguity aversion influences another well-known decision bias: the framing effect. This is regrettable given that numerous studies in the domain of decision making under risk have demonstrated that people often make decisions based on whether a particular choice is presented as a loss or gain and consequently as a negative or positive affective frame (De Martino et al., 2006; Kahneman & Tversky, 1983; Kahneman & Frederick, 2007; Reyna, 2004, 2012; Tversky & Kahneman, 1981; Wang, 1996; Zheng, Wang, & Zhu, 2010). Typically, the framing effect leads decision makers to favor uncertain options when alternatives are framed in terms of loss, whereas sure options are preferred when the alternatives are presented in terms of gain (Kahneman & Tversky, 1983). Like ambiguity aversion, the framing effect occurs during monetary decision-making because of an affective and intuitive heuristic that is part of System 1 processing. According to Kahneman and Frederick (2007), this affective heuristic operates on the principle that sure gains are especially attractive and sure losses are especially aversive, leading participants to systematically reject options associated with a sure loss. Initial support for this dual process view came from neuroimaging studies showing that the classic framing effect involves specific brain regions that have been implicated in emotional processing (De Martino et al., 2006). More specifically, framing effects have been associated with increased activation of the amygdala, and a neuropsychological study of patients with lesions to the amygdala reported a decrease in the monetary-loss aversion (De Martino, Camerer, & Adolph, 2010). Furthermore, healthy participants exhibited more

pronounced skin conductance responses (used as a measure of emotional reactivity) when an option was described as a potential loss rather than a mathematically equivalent potential gain (De Martino, Harrison, Knafo, Bird, and Dolan, 2008). Additional empirical evidence of the dual process model came from behavioral studies of the influence of emotional context on framing susceptibility (Cassotti et al., 2012). Specifically, the results indicated that exposure to positive emotional primes reduced risk-taking in the loss frame and led to a suppression of the framing effect. Previous studies examining how ambiguity aversion modulates the impact of framing manipulations have yielded discrepant results. Using an adaptation of the famous “Asian diseases problem,” Reyna (2012) demonstrated that framing effect is preserved when information about probabilities are deleted. Similarly, others studies showed a reflection effect with more ambiguity avoidance in gain domain than in loss domain. More specifically, when consumers have to choose between a well-defined product and an ambiguous product, they are ambiguity-averse in gain conditions and ambiguityseeking in loss conditions (Kahn & Sarin, 1988; see also Ho et al., 2002). On the contrary, using adaptations of the Ellsberg paradox, several studies reported a lack of sign effect (gain vs. loss) on ambiguity aversion showing that ambiguous options are avoided in both gain and loss domains (Inukai & Takahashi, 2009; Keren & Gerritsen, 1999; Smith et al., 2002). In the present study, we aimed to clarify the potential impact of ambiguity aversion on the framing effect during decision-making. Thus, we designed a new financial decisionmaking task involving a choice between a sure option and a gamble option. In the risk condition, the gamble option was represented with a bicolor “wheel of fortune” depicting the probability of winning or losing an amount of money, whereas this probability information was lacking in the ambiguity condition. The sure option was formulated in terms of either gain or loss. Consistent with the dual process framework, we assumed that ambiguity aversion would be more pronounced within the gain frame, given that sure gains are particularly attractive and the ambiguous option is particularly aversive. Critically, the dual process model predicts a conflict between loss aversion and ambiguity aversion when the sure option is described in terms of loss. Therefore, the following two hypotheses can be formulated regarding the influence of ambiguity aversion on the effect of presentation format in terms of loss. 1) If the ambiguous option triggers a stronger aversive response than that triggered by contemplating a sure loss, then participants should avoid the gamble option in the ambiguity condition, irrespective of the frame. 2) However, if ambiguity aversion does not influence the framing effect, then participants should show a preference for the gamble option during both the risk and the ambiguity conditions.

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Methods

amounts of money (€10, €30, and €50). Thus, a total of 36 trials were pseudo-randomly presented.

Participants Thirty-seven undergraduates from Paris Descartes University participated in this study (mean age = 20.5, SD = 1.34). All of the participants provided written consent and were tested in accordance with national and international norms governing the use of human research participants. They received partial credit towards the completion of their introductory psychology course. Design and procedure All of the participants completed a French version of a computerized monetary decision-making task inspired by De Martino et al. (2006) and Stanton et al. (2011). The participants were tested individually in a quiet room. They were initially given verbal instructions and shown examples of trials to ensure that they understood the instructions. The task was divided into two runs, each composed of 18 trials. Participants were given the opportunity to take a short break after the first run. At the start of each trial, participants received an initial amount of money in “Monopoly money” form (e.g., “You receive €50”; Fig. 1). Subsequently, participants were required to choose between a sure option (i.e., allowing participants to save a part of the initial amount) and a gamble option (i.e., allowing participants to potentially gamble the whole initial amount) by pressing one of two buttons on a keyboard. These two options remained available until the participant’s response. Each trial was framed in terms of either gain or loss. In the gain-framed condition, the sure option allowed participants to keep a part of the initial amount (e.g., “You keep €25.”), whereas in the loss-framed condition, the sure option represents a certain loss of a part of the initial amount (e.g., “You lose €25”). The gamble option did not change depending on frame conditions and was represented as a bicolor “wheel of fortune” depicting the probability of winning or losing the entire starting amount (e.g., 50 % chance to keep the €50 and 50 % chance to lose the €50). For each trial, the two framing conditions were mathematically equivalent and the sure and gamble options had the same expected value. Critically, two uncertainty levels (risk and ambiguity) were added to the original task. In the risk condition, the probabilities of winning or losing in the gamble option were available (blue slices for gains and red slices for losses), whereas a grey circle masked the “wheel of fortune” in the ambiguity condition. Trials were constructed by factorially combining the two uncertainty levels (risk or ambiguity) with the two framing manipulations (i.e., 18 trials in the loss frame and 18 trials in the gain frame): the part of the initial amount proposed in the sure option (30 %, 50 %, or 70 %) and the three starting

Results To examine whether the number of gamble choices varied according to the uncertainty level, the framing presentation, and the portion of the initial amount proposed in the sure option, we performed an analysis of variance (ANOVA) with uncertainty level (risk vs. ambiguity), frame (gain vs. loss), and the part of the initial amount proposed in the sure option (30 %, 50 %, or 70 %) as the three within-subject factors. First, ANOVA revealed a main effect of frame (F(1,36) = 60.18, p < .001, ηp2 = 0.63), indicating that the participants chose the gamble option more frequently in the loss frame than in the gain frame (mean ± SD: loss: 50.3 % ± 18.52, gain: 32.28 % ± 17.06). This analysis also revealed a main effect of uncertainty level (F(1,36) = 9.68, p < .01, ηp2 = 0.21), showing that participants chose the gamble option less often in the ambiguity condition than in the risk condition (mean ± SD: ambiguity: 36.48 % ± 16.38, risk: 46.09 % ± 21.03). However, there was no interaction between frame and uncertainty (F(1,36) = 2.05, p = 0.16, ηp2 = 0.05). To support that framing manipulation influenced gamble choices in both the risk and the ambiguity conditions, we examined the number of gamble choices between the gain and the loss frames separately for the two uncertainty levels. These complementary analyses revealed that participants were more likely to choose the gamble option in the loss frame compared with the gain frame in the risk condition (t(36) = 5.95, p < 0.001; loss: 56.7 % ± 20.75, gain: 35.4 % ± 26.3) and in the ambiguity condition (t(36) = 5.04, p < 0.001; loss: 43.8 % ± 20.94, gain: 29.1 % ± 16). In addition, we used one-sample t tests with 50 % as the test value to further examine whether participants showed a preference for the sure option or for the gamble option across frames and uncertainty levels, defined with respect to neutral behavior (i.e., when a decision maker was indifferent to the gamble option and the sure option) (Fig. 2). In the risk condition, participants presented a preference for the sure option in the gain frame (t(36) = 3.37, p < 0.001) and the gamble option in the loss frame (t(36) = 1.98, p < 0.05). Crucially, in the ambiguity condition, participants preferred the sure option in both the gain frame (t(36) = 7.93, p < 0.001) and the loss frame (t(36) = 1.79, p < 0.05). Interestingly, the ANOVA revealed a significant interaction between the uncertainty level and the portion of the initial amount proposed during the sure option (F(2,72) = 85.16, p < 0.001). For the risk condition, post-hoc comparisons (all pvalues were corrected using a Bonferroni procedure) indicated that participants chose the gamble option more frequently when the sure option represented 70 % of the initial amount

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Fig. 1 English translation of the sequence of a trial presented at two uncertainty levels (risk and ambiguity) and under two framing conditions (gain and loss frames). Participants received an initial amount of money in “Monopoly money” form. Then, they had to choose between a sure option (allowing the retention of a part of the starting amount of money) and a gamble option (allowing the participant to keep or lose the whole

initial amount). The sure option was presented in terms of gain or loss, but these two presentations were mathematically equivalent. In the risk condition, the gamble’s winning or losing probabilities were available (blue slices for gain and red slices for loss), whereas a gray circle masked the “wheel of fortune” in the ambiguous condition

(M±SD: 60.36 % ± 36.46) compared with 50 % (39.18 % ± 33.26, t(36) = 3.87, p < 0.01) or 30 % (38.73 % ± 36.19, t(36) = 3.17, p < 0.05). In contrast, for the ambiguity condition, the results indicated that participants chose the gamble option less frequently when the sure option represented 70 % of the initial

amount (10.36 % ± 22.01) compared with 50 % (22.97 % ± 28.09, t(36) = 3.95, p < 0.01) or 30 % (76.12 % ± 29.98, t(36) = 14.7, p < 0.001). There was not an interaction between the frame and the portion of the initial amount proposed during the sure option (F(2,72) = 0.6, p = 0.55, ηp2 = 0.05) or among the frame, uncertainty level, and portion of the initial amount proposed during the sure option (F(2,72) = 1.12, p = 0.33, ηp2 = 0.05).

Discussion

Fig. 2 Percentage of test trials in which the subjects decided to gamble in the gain and loss frames under the risk and ambiguity conditions. Error bars represent 95 % confidence intervals. *p < 0.05; ***p < 0.001

The purpose of the present study was to uncover the potential impact of ambiguity aversion on framing effect in decision making. Two major findings emerged from this investigation: 1) Participants showed a clear preference for the sure option in the ambiguity condition regardless of frame. 2) However, participants presented a framing effect in both the risk and the ambiguity conditions. In line with numerous previous studies, participants were ambiguity-avoidant; they presented a lower preference for gamble options in the ambiguity condition than in the risk condition (Camerer & Weber, 1992; Ellsberg, 1961; Keren & Gerritsen, 1999; Inukai & Takahashi, 2009; Pulford & Colman, 2008). This result confirms that adults prefer a clearly defined situation over an ambiguous situation that could potentially be more advantageous. Missing information seems

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evoke an emotional response driving participants to automatically reject ambiguous situations. Moreover, our results also replicated the impact of gain and loss frames in decision under risk (Cassotti et al., 2012; De Martino et al., 2006; Tversky & Kahneman, 1981). When gamble probabilities were available, participants exhibited a preference for the sure option in the gain frame and a preference for the gamble option in the loss frame (De Martino et al., 2006; Kahneman & Tversky, 1983), demonstrating a bidirectional framing effect (i.e., classic framing effect: risk-aversion in the gain frame and risk-seeking in the loss frame; see Wang, 1996). In contrast, when information about gamble probabilities was masked, participants showed an ambiguity aversion effect regardless of the frame: they preferred the sure option to the ambiguous gamble option in the gain and in the loss frame. This result is consistent with previous works showing ambiguity aversion in both gain and loss situations (Inukai & Takahashi, 2009; Keren & Gerritsen, 1999; Smith et al., 2002). However, despite an ambiguity aversion that was present regardless of the frame, frame also affected the choices of the participants during ambiguous situations. The absence of an interaction between the incertitude level (risk or ambiguity) and the frame (gain or loss) indicated that the presentation format (in terms of gain or loss) still affected the decision-making of the participants during the ambiguity condition. Therefore, when information on probabilities was missing, the number of gamble choices increased during the loss frame relative to the gain frame in a fashion that was similar to a situation in which information on the probabilities was available. This susceptibility of framing in decision making under ambiguity is consistent with the findings of a previous study showing that the framing effect is preserved when information about probabilities is withdrawn (Reyna, 2012, see also Kahn & Sarin, 1988; see also Ho et al., 2002). Nevertheless, our results extend the findings of previous studies by showing that the framing effect was bidirectional during the risk condition (i.e., risk-aversion in the gain frame and risk-seeking in the loss frame), whereas participants presented a unidirectional framing effect during the ambiguity condition did not present a preference reversal but rather a more extreme choice tendency (i.e., a stronger preference for the sure option under the positive frame compared with that under the negative frame). In agreement with the dualprocess view, this shift from bidirectional to unidirectional framing suggests that ambiguity aversion induces a global tendency to avoid the option associated with a lack of information about probability and to consider it to be particularly aversive. In addition, the prospect of a sure loss triggers a stronger negative emotional response than does the prospect of a sure gain, leading participants to choose the sure loss less often despite the lack of information during the gamble option.

An alternative interpretation of the framing effect observed during the ambiguity condition might be that the participants formed assumptions about the ambiguous gamble based on their experiences during the risk condition. However, further inspection of the data revealed that the number of gamble choices increased depending on the portion of the initial amount proposed during the sure option of the risk condition, whereas the opposite trend was observed during the ambiguity condition. Given that the sure and gamble options had the same expected value during the risk condition, the portion of the initial amount proposed in the sure option is equal to the winning probability of the gamble option. Therefore, these results suggest that participants did not infer the winning probability of the gamble option during the ambiguity condition relative to those depicted during the risk condition. As a limitation, the present study only described choices between a sure and a completely masked gamble. However, in daily life situations, ambiguity does not necessarily imply the absence of any information but also can indicate partially missing information. Given that ambiguity aversion has been reported in situations of partial ambiguity (i.e., a possible estimation of a range of potential probabilities) (Levy, Snell, Nelson, Rustichini, & Glimcher, 2010; Tymula et al., 2012), further investigations will be necessary to examine how partial ambiguity influences the framing effect. Overall, our data provide empirical evidence that ambiguity aversion induces a shift from a bidirectional to unidirectional framing effect during monetary decision making.

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Does ambiguity aversion influence the framing effect during decision making?

Decision-makers present a systematic tendency to avoid ambiguous options for which the level of risk is unknown. This ambiguity aversion is one of the...
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