Mindfulness and Decision Making under Risk

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Mindfulness and Decision Making

under Risk

Jorrit Jan Grolleman, 11421398

Supervisor: Prof. dr. A.J.H.C. Schram

Behavioural Economics and Game Theory

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Statement of Originality

This document is written by student Jorrit Jan Grolleman who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

This thesis studied the effect of mindfulness on decision making under risk. Financial decision making is often biased and sub-optimal, since investors tend to make decisions intuitively and automatically. Conversely, mindfulness concerns an attentive and aware focus on the present moment and has been found to reduce several decision biases. Mindfulness was hypothesized to also improve decision making under risk. 151 participants residing in the United States participated in an online survey distributed through the Amazon Mechanical Turk platform. However, no significant relationship was found between mindfulness and loss aversion and narrow framing.

Keywords

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1. Introduction

Research on mindfulness, by the widespread definition of “a receptive attention to and awareness of present events and experience” (Brown & Ryan, 2003; as cited in Brown, Ryan, & Creswell, 2007, p.212), has increased enormously in recent years (see figure 1). The almost exponential rise of its popularity was initiated by the clinical psychology research. Kabat-Zinn (1982) started in the early 1980s to successfully implement mindfulness for the treatment of patients with chronic pain. Mindfulness also showed good results in the treatments of anxiety and depression (Kabat-Zinn et al., 1992). These results lead to study mindfulness also in an increasing number of laboratory and workplace settings. It has been found to be beneficial for mental and

Figure 1. Number of publications containing the word mindfulness in Google Scholar

1980-2013.

physical health and interpersonal relationships (Brown, et al., 2007) and also to increase job satisfaction (Hülsheger et al., 2013). Smartphone applications such as Headspace make mindfulness very accessible and bring it to an even broader public, counting over 10 million downloads.

On the other hand, decision biases often produce adverse effects and may thereby influence an individual’s well-being in a negative way. Individuals tend to overly rely on heuristics, which leads to biased and suboptimal outcomes (Tversky & Kahneman, 1974; Kahneman, 2011). This is caused by the fact that individuals most

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of the time use fast, intuitive and automatic responses (Stanovic & West, 2000; Kahneman, 2011).

Since decision making biases can have costly impacts on decision outcomes, researchers are looking for ways to increase human judgment in such situations (Milkman, Chugh, & Bazerman, 2009) and enhance their financial capability (De Meza, Irlenbusch, & Reyniers, 2008). Especially, Glomb et al. (2011) point out the importance of mindfulness on workplace outcomes.

Only a few studies have linked mindfulness to decision biases. The effects of mindfulness on the negativity bias, the sunk cost bias, and overconfidence have been found to be positive (Kiken & Shook, 2011; Hafenbrack, Kinias, & Barsade, 2014; Lakey et al., 2007). Investors are also found to be prone to biases, which may lower their overall welfare. Although an important part of decision making for businesses and investors is concerned with risk, no research has yet studied the effects of mindfulness and decision making under risk. This thesis endeavors to close this gap by studying the effects of mindfulness on two important aspects of decision making under risk, namely loss aversion and narrow framing. Hereby it adds to the literature on improving decision making, but also studies the impact that mindfulness can have.

Although mindfulness has showed positive effects in a variety of research areas, the results in this thesis show no significant relationship with loss aversion and narrow framing. The regressions even point in the opposite direction, indicating that mindfulness rather increases the tendency to be loss averse and frame narrowly.

This thesis is organized as follows: Chapter 2 gives a definition of mindfulness and reviews its effects on well-being, it also discusses related research of mindfulness and decision making. Next, decision biases are discussed and their impacts on behavioural finance, focusing on loss aversion and narrow framing. Chapter 3 presents the methodology and formulates the hypotheses, where Chapter 4 gives the results. Chapter 5 concludes the thesis and discusses its limitations and implications for future research.

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2. Literature Review

2.1 Mindfulness

Over the past 30 years, mindfulness has received a lot of attention in the psychology literature, primarily due to its positive effects on psychological well-being (Brown et al., 2007). Mindfulness has been defined as “paying attention in a particular way: on purpose, in the present moment, nonjudgmentally” (Kabat-Zinn, 1994, p.4), which originated in Buddhism (Kabat-Zinn, 1994). This definition is in line with the three axioms of the model of mindfulness by Shapiro et al. (2006), namely intention, attention and attitude. The Western view on mindfulness mostly focuses on the quality of information-processing (Weick & Sutcliffe, 2006). Mindfulness is used to increase awareness and handle mental processes in such a way that it decreases stress, by leading the individual to be attentive to the situation and its context and aware of the effects it has. The first major program to practice mindfulness is Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1982, 1990). Furthermore, Mindfulness-Based Cognitive Therapy (MBCT; Segal, Williams & Teasdale, 2002), Dialectic Behavior Therapy (DBT; Linehan, 1993) and Acceptance and Commitment Therapy (ACT; Hayes, Strosahl, & Wilson, 1999) also have proven to be effective mindfulness therapies.

Several self-report questionnaires have been developed to measure mindfulness. Mindfulness can be viewed as a trait since every human being is mindful to some degree, but also as a state since this degree may vary from moment to moment within the same individual (Brown & Ryan, 2003). Furthermore, it can be seen as a skill that can be developed (Bishop et al., 2004). As noted by Feldman et al. (2007), it may be beneficial to have several tools for measuring mindfulness. This is useful for the operationalization of the concept of mindfulness, as well as for giving researchers the possibility to use the instrument with the best fit to their research. Some of the most important mindfulness scales are:

1. Freiburg Mindfulness Inventory (FMI; Walach et al., 2006)

The FMI was originally developed as a 30-item instrument to assess mindfulness in experienced meditators. Walach et al. (2006) reduced the FMI to a shorter 14-item version, taking it out the meditation context and thereby making it broader applicable. It measures the approach to negativity and to what extent individuals non-judgmentally observe the present moment. However, it remains focused on the differences between

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beginner and experienced meditators and therefore requires participants with meditation experience.

2. Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003)

The MAAS consists of 15 items such as “I find myself doing things without paying attention”. It is designed to measure the attentiveness to and awareness of the present-moment and captures the use of the automatic pilot. Brown and Ryan (2003; as cited in Brown et al., 2007, p.212) define mindfulness as “a receptive attention to and awareness of present events and experience”. Although it leaves out some of the Eastern aspects of mindfulness such as non-judgmental acceptance, it has been found to be a very reliable scale (Brown & Ryan, 2003).

3. Kentucky Inventory of Mindfulness Skills (KIMS; Baer, Smith, & Allen, 2004) The KIMS is closely related to DBT, especially in the conceptualization of mindfulness as a skill. It measures four different components of mindfulness: observing, describing, acting with awareness and accepting without judgment. Each component consists of 9 or 10 items, totaling the KIMS in 39 items. It captures a broad area of mindfulness, but it does not capture some elements relevant for MBCT.

4. Cognitive Affective Mindfulness Scale-Revised (CAMS-R; Feldman et al., 2007) The CAMS-R consists of 12 items. It measures attention, present focus, awareness and acceptance separately, but results in one final factor. Furthermore, it includes psychological distress, making it especially applicable to clinical samples.

5. Five Facet Mindfulness Questionnaire (FFMQ; Baer et al., 2008)

The FFMQ adds a fifth component to the previous discussed instruments. Not only observing, describing, awareness and non-judgment are measured, but also nonreactivity by items such as “I perceive my feelings and emotions without having to react to them”. It results in five different factors for each component, which makes it a very comprehensive scale. The FFMQ was constructed by combining other scales, which makes it rather an empirical than a theoretical construct. This may make the scale arbitrary.

6. Toronto Mindfulness Scale (TMS; Lau et al., 2006)

The TMS is designed, unlike the other tools, to measure mindfulness as a state directly after meditation. Its focus is on curiosity and decentering.

Mindfulness gained popularity by its positive effects on clinical samples, showing that it decreases anxiety and increases the ability to cope with pain and stress (Kabat-Zinn, 1982, 1990; Kabat-Zinn et al., 1992). However, also for non-clinical

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samples mindfulness is found to increase mental health and psychological well-being, physical health and social relationships (for a review see Brown et al., 2007). Furthermore, mindfulness also improves work outcomes by increasing task performance, resiliency, the quality of relationships at work (Glomb et al., 2011) and job satisfaction (Hülsheger et al., 2013).

2.2 Previous Studies

Lakey et al. (2007) recruited undergraduate students who gambled at least weekly to test the effects of mindfulness on the severity of their gambling behavior. Participants filled in the MAAS as a measurement for mindfulness, the Self-Control Scale (Tangney, Baumeister, & Boone, 2004) for self-control and the Diagnostic Interview for Gambling Severity (Winters, Specker, & Stinchfield, 2002) to measure the severity of the participant’s gambling behavior. This survey indicated that mindfulness lowers gambling pathology. In the second study the Georgia Gambling Task (Goodie, 2003) was used to assess overconfidence and risk-willingness, and the Iowa Gambling Task (Bechara et al., 2003) to measure the narrow focus on reward. Mindful individuals performed better on both gambling tasks, indicating a mediating role of mindfulness on overconfidence, on the acceptance of risky bets, and on the narrow focus on reward. Kiken and Shook (2011) studied the effects of mindfulness on the negativity bias, the tendency to overweight negative information in relation to positive information (for a review see Baumeister et al., 2001). In a laboratory setting they let students play the game BeanFest (Fazio, Eiser, & Shook, 2004), in which they had to associate beans to be positive or negative1_{. The experimental treatment consisted of a 15-minute}

mindfulness recording and the control group received instructions on mind wandering (Arch & Craske, 2006). The students in the mindfulness condition scored significantly higher on a state version of the MAAS than those in the control treatment, indicating the effectiveness of the recording. Participants filled in the Future Events Scale (FES) as a measure of the negativity bias (Anderson, 1990). Furthermore, the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988) was used to

1_{Beans differed in their size and the number of speckles, half of the beans were ‘positive’ and the}

other half was ‘negative’. 36 beans from 6 different regions from a 10x10 matrix were presented one by one. After each trial the participant learned whether it was a positive or negative bean. Each trial the participant could choose to select or not to select the bean. A selected positive bean would increase payouts, and selecting a negative bean would decrease payouts (for more details, see Fazio et al., 2004)

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check that the results were not influenced by differences in mood. The treatment group showed less negativity bias and were more accurate in assessing the positive stimuli. However, mindfulness had no effect on the negative judgments. It is theorized that mindfulness may allow for a greater awareness of positive stimuli, thereby increasing well-being.

Hafenbrack et al. (2014) tested in four studies the influence of mindfulness in resisting the sunk cost bias. First they used a survey to test whether trait mindfulness helps to resist the sunk cost bias. The MAAS was used as a measure for trait mindfulness and respondents had to fill in a questionnaire on the resistance of sunk costs from the Adult Decision-Making Competence Inventory (Bruine de Bruin, Parker, & Fischhoff, 2007). Secondly, two laboratory studies were held in which the impact of state mindfulness, induced by a 15-minute recording, in resisting the sunk cost bias is studied. The same recording treatments as in Kiken and Shook (2011) were used. Participants then faced two different sunk cost scenarios, in which the decision for the project indicated a sunk cost bias or resistance. Lastly, mediating mechanisms are studied with a survey, since it was hypothesized that temporal focus and negative affect could play a role. The first survey showed a positive correlation between trait mindfulness and resisting the sunk cost bias. In both the laboratory experiments, also state mindfulness was found to positively affect resistance. In the last survey it was showed that mindfulness decreased the focus on the past and future, leading to a higher resistance.

2.3 Decision Making Biases

In decision making under uncertainty individuals often make use of heuristics. Although this helps to simplify complex tasks, it may lead to systematic biases (Kahneman & Tversky, 2011). The biases are often the result of automatic and intuitive responses, whereas reasoning is more effortful. Stanovic and West (2000) developed a theory with System 1 and System 2 processes, which was also adopted by Kahneman (2003). System 1 relates to the automatic, intuitive and emotional responses, whereas System 2 concerns effortful reasoning. Bargh and Chartrand (1999) noted that most of our actions are made automatically and sub-consciously. An example of System 2 behavior is taking the ‘outsider’s view’: taking a step back to view the problem in a wider context (Kahneman & Lovallo, 1993).

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The findings of these biases and heuristics question the rationality assumption of neo-classical economics. Although some biases may have been evolutionarily advantageous (Haselton & Nettle, 2006), they may have costly impacts in the modern financial world. These negative outcome effects have drawn the attention of decision making biases to the new field of behavioural finance. Behavioural finance studies decision making under risk, and especially the effects of decision biases on security prices and investors behavior.

In the classical Capital Asset Pricing Model (CAPM) it is assumed that

investors hold well-diversified portfolios. Furthermore, the Efficient Market Hypothesis (EMH; Fama, 1970) states that all information available is already included in the security prices. However, Kahneman and Tversky (1984) noted that investors are biased. Examples of biases in the behaviour of individual investors are the disposition effect (Odean, 1998), where investors tend to sell their winning stocks too early and hold on to their losing stocks for too long, and the home bias (French & Poterba, 1991; Coval & Moskowitz, 1999) which shows that investors tend to overinvest in domestic and local stocks. Another phenomenon in finance is the Equity Premium Puzzle, the fact that stocks have had a much higher return than bonds for almost a century (Mehra & Prescott, 1985). The annual real return on the Standard and Poor 500 index yielded about 7 percent, whereas the real return on treasury bills was less than 1 percent. Although the treasury bills are relatively risk-free and the Standard and Poor 500 bears some risk, this premium is too large to be explained by classical economics models.

Managers and individual as well as mutual fund investors are prone to

decision biases (Bazerman & Moore, 2008; Barber & Odean, 2013; Bailey, Kumar &, Ng, 2011). This thesis will focus on two important aspects that have received a lot of attention in the behavioural finance literature and may explain some of the mentioned biased behaviour of investors; loss aversion and narrow framing.

2.3.1 Loss Aversion

In contrast to the assumption of neo-classical economics, individuals often evaluate situations relative to a reference point, instead of focusing on total welfare (Kahneman & Tversky, 1979; Camerer, 2000). Given this reference point many individuals exhibit loss aversion, that is, they find losses to have a more negative

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impact than the positive impact of gains of the same amount (Kahneman & Tversky, 1984). This leads to the rejection of even small favorable gambles (Rabin, 2000; Tom et al., 2007). Loss aversion also holds for riskless choice concerning goods, in which it is called the endowment effect (Kahneman, Knetsch, & Thaler, 1990; Thaler, 1980). When participants received a mug they asked a much higher price to sell the mug than the bidding price of participants that did not get a mug. This gap can be explained by loss aversion for the mug (Thaler, 1980). Gächter, Johnson and Hermann (2010) show that there exists a strong positive correlation between loss aversion in riskless and risky choice problems. Furthermore, they found that loss aversion is increasing in age, income and wealth, and decreasing in education in both treatments. Since most research is done with young university students, the importance of loss aversion may be underestimated. Loss aversion can explain many seemingly irrational real-life situations (Camerer, 2000) and even improve the understanding of the equity premium puzzle (Benartzi & Thaler, 1995). However, individuals seem not to be aware of this bias, they do not expect themselves nor others to exhibit loss aversion (van Boven, Dunning, & Loewenstein, 2000).

Loss aversion is an important part of the prospect theory of Kahneman and Tversky (1979). Relative to the reference point, the impact of losses is found to be about twice as large as for gains (Tversky & Kahneman, 1992; Knetsch & Thaler, 1990). Loss aversion is found to occur in capuchin monkeys (Chen, Lakshminarayanan, & Santos, 2006) and young children (Harbaugh, Krause, & Berry, 2001). Furthermore, it is supported by neuroscientific research (Tom et al., 2007). Loss aversion is affected by socio-demographic variables (Gächter et al., 2010), outcomes of previous gambles (Barberis, Huang, & Santos, 2001; Thaler & Johnson, 1990) and an induced shift in perspective where participants had to ‘think like a trader’ (Sokol-Hessner et al., 2009). This shows that it is not a constant and that its perception may be changed.

2.3.2 Narrow Framing

When people are faced with a problem they often view it as unique and evaluate it in isolation, thereby disregarding other important information. This bias is called narrow framing by Kahneman and Lovallo (1993). Economic theory would assume that individuals maximize their utility by taking into account their total welfare, integrating a

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new problem together with all the other income and risks they already face. However, this is often not the case. The classic example of narrow framing by Tversky and Kahneman (1981, p. 454) illustrates this bias:

Imagine that you face the following pair of concurrent decisions. First examine both decisions, then indicate the options you prefer.

Decision (i) Choose between: A. a sure gain of $240 [84%]

B. 25% chance to gain $1000 and 75% chance to gain nothing [16%]

Decision (ii) Choose between: C. a sure loss of $750 [13%]

D. 75% chance to lose $1000 and 25% chance to lose nothing [87%]

The percentages in brackets show that most individuals choose A and D. According to prospect theory, people are loss averse and they overweight certain outcomes (Kahneman & Tversky, 1979). Furthermore, people tend to be risk averse in the gain domain, and risk seeking in the loss domain. However, when both outcomes are combined B and C dominate A and D:

A & D: 25% chance to win $240 and 75% chance to lose $760. B & C: 25% chance to win $250 and 75% chance to lose $750.

This shows that most individuals view the decisions in isolation, thereby lowering their total welfare.

Narrow framing is closely related to loss aversion, as argued by Barberis, Huang and Thaler (2006). Most individuals reject the gamble

(550, ½; -500, ½),

which means “gain $550 with probability ½, lose $500 with probability ½” (Barberis et al., 2006, p.1071). Barberis et al. (2006) show that when losses loom twice as large as gains, the gamble would have an expected value of 550(½) - 2(500)(½) = -225. Thus,

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when loss averse individuals value this gamble in isolation it would be rejected. However, if the individuals were framing broadly, they would also include all their other risk, such as labor income risk and housing risk. In the example of Barberis et al. (2006) the individual faces other risks of (30,000, ½, ; -10,000, ½). This gives a total combined gamble of

(30,550, ¼ ; 29,500, ¼ ; -9,450, ¼ ; -10,500, ¼ )

so the agent has to decide between the gambles

30,000(½) - 2(10,000)(½) = 5,000

and

30,550(¼) + 29,500(¼) - 2(9,450)(¼) - 2(10,500)(¼) = 5,037.5

so therefore the 550/500 gamble should be favorable and accepted even by loss averse individuals. When the individual does not recognize these other existing risks that would benefit from the “diversification” insurance the small gamble would offer and is averse to losses, the gamble is likely to be denied.

An important factor that influences narrow framing is the reliance on System 1 (Kahneman, 2003). When decisions are made intuitively, individuals do not reason broadly (Kahneman, 2003). People tend to make use only of the information that is most readily available, which Kahneman (2003) termed the accessibility of information. As is the case for the Tversky and Kahneman (1981) example, the individual options are easily accessible, but the combination of both options takes some effort.

Another factor is the possibility of future regret, as hypothesized by Barberis et al. (2006). If the gamble turns out negatively, the individual may feel the pain of regret that he would have been better off if he had rejected the gamble. When this non-consumption utility of regret is included in the evaluation, an individual may abstain from an investment with positive expected value. Furthermore, the feeling of regret places the focus on the specific outcome, instead of taking the overall welfare into account. This may lead the individual to feel regret for specific choices, and not to only evaluate the combined outcome.

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Many studies have found evidence for narrow framing. Camerer et al. (1997) found that taxi drivers framed their working days narrowly. Taxi drivers can choose every day when they want to stop or go on working. It was found that drivers quit early on good days, and made long days on bad ones. It would be advantageous for the drivers to take the easier income on the good days and quitting early on low-income days. However, the drivers tended to frame their earnings narrowly and focused only on attaining specific targets. Camerer et al. (1997) calculated that the drivers could have increased their earnings by about 20% if they allocated their hours differently.

Lottery choices are also found to depend on how the problem is ‘bracketed’. A problem is bracketed broadly when the set of individual choices is large and is considered in its entirety, including the relation between all individual choices. Narrow bracketing happens when the set of individual choices is very small. When lottery choices are bracketed narrowly the lotteries are likely to be viewed one by one instead of integrating their total outcome (for a review see Read et al., 1999). Framing is also important in portfolio choices and security prices (Barberis & Huang, 2001, 2009). Kumar and Lim (2008) developed a way to measure the impact of the mode of framing on investment choices of investors in a real-life setting. Experimental research suggests that when the total set of choices is larger, the problem is more likely to be broadly framed. Therefore, Kumar and Lim (2008) used the degree of trade clustering to serve as a proxy for choice bracketing, where more clustered trades would indicate a broader frame and more separate trades indicate a narrow frame. It is found that investors who traded more clustered also diversified their portfolios better and were less prone to the disposition effect.

The investment horizon of investors may influence their willingness to take risks (Gneezy & Potters, 1997; Thaler et al., 1997). This myopic view regarding investments may, in combination with loss aversion, lead investors to turn down positive opportunities. Benartzi and Thaler (1995) named this concept myopic loss aversion (MLA) and used it to explain the equity premium puzzle. It is plausible that a portfolio may yield a negative return after one year. After 30 years, however, it is highly unlikely that a portfolio has a negative return. When investors focus their attention on the term, loss aversion may abstain them from investing. The fear of this loss in the short-term may explain the high premium demanded by investors (Benartzi & Thaler, 1995). Haigh and List (2005) tested whether professional traders also exhibited MLA and found that they were even more prone to this bias than students.

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2.4 Literature Summary

Mindfulness is found to have several positive effects on individuals lives, not only personal but also in the field of decision making. Although cognitive biases are important in this field, the fact that mindfulness may decrease them have received little attention. Kiken & Shook (2011) showed that mindfulness could decrease the negativity bias. Hafenbrack et al. (2014) found that mindfulness increases the resistance to the sunk cost bias. Besides those studies, the effects of mindfulness on decision biases have not been studied so directly. Lakey et al. (2007) find evidence that mindfulness decreases overconfidence, although their main focus is on pathological gambling.

Behavioural finance has found evidence that investors are also prone to biases and that it may have negative effects for their financial wealth. Since mindfulness is able to influence decision biases, this thesis studies whether those effects also hold for decision making under risk. It does so by studying the effects of mindfulness on loss aversion and narrow framing. Loss aversion and narrow framing are important aspects in the behavioural finance literature and may explain certain behaviour of investors.

This research will add to the literature since it studies two areas that have not been linked before. Hereby it will not only broaden the understanding of mindfulness and its effects, but also answer the call for research into ways to improve financial decision making. Furthermore, a deeper understanding of decision making under risk will have practical relevance for managers and investors.

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3. Methodology

This study investigated the relationship between trait mindfulness and loss aversion and narrow framing. The survey used was created in Qualtrics and participants were recruited through the online Amazon Mechanical Turk (MTurk) platform. Although there may be disadvantages of conducting internet-based experiments (Reips, 2002), MTurk is found to meet the quality of psychometrics standards of traditional samples (Buhrmester, Kwang, & Gosling 2011). Experiments in MTurk can have the same internal and external validity as a laboratory experiment and MTurk subjects are also prone to priming and framing (Horton, Rand, & Zeckhauser, 2011). MTurkers perform significantly better on attentive checks than an undergraduate sample, implying that they are more attentive and read instructions more carefully (Hauser & Schwarz, 2016). The MTurk population is older and earns a higher salary than a student sample (Paolacci, Chandler, & Ipeirotis, 2010), which makes financial and investment behavior more relevant for them. Furthermore, the sample size obtained through MTurk would be significantly larger than by a laboratory experiment. 151 participants residing in the United States participated in this study on July 7th_{, 2018.}

The survey consisted of four parts. The first part concerned narrow framing, the second loss aversion, the third mindfulness, and a set of demographic questions lastly. The survey ended with a code that participants had to enter in MTurk to receive a small fixed payment fee. Since it was only possible to reward participants in MTurk and not to punish them, all gambles were hypothetical and all the participants were given the same fixed payment. No differences were found in risky choices with hypothetical or real payoffs (Beattie & Loomes, 1997; Kühberger, Schulte-Mecklenbeck, & Perner, 2002). Camerer and Hogarth (1999) found that financial incentives often did not improve the performance, especially not in risky choices. However, higher incentives were likely to reduce the variance of those choices. Furthermore, Rabin and Weizsäcker (2009) tested narrow framing in several forms and found no statistically significant difference between the hypothetical small-stake condition in which participants only received a fixed fee and the small-stake condition with real payouts.

To measure narrow framing, an altered small real-stake version of the example by Tversky and Kahneman (1981) was used, which is also used as a measurement for narrow framing by Rabin and Weizsäcker (2009) in a similar form:

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Imagine that you face the following pair of concurrent decisions. First examine both decisions, then indicate the options you prefer:

Decision (i). Choose between: A. a sure gain of $2.40

B. 25% chance to gain $10.00 and 75% chance to gain nothing

Decision (ii). Choose between: C. a sure loss of $7.50

D. 75% chance to lose $10.00 and 25% chance to lose nothing

Combined option A-D is first-order stochastically dominated (FOSD) by the combined option B-C and therefore indicated that the individuals that pick this combination framed the choices narrowly:

A & D: 25% chance to win $2.40 and 75% chance to lose $7.60 B & C: 25% chance to win $2.50 and 75% chance to lose $7.50

Let θ be a dummy indicating whether an individual picks the FOSD option. Individuals that picked the A-D option would always have been better off by picking the B-C option. However, prospect theory predicts that people are risk averse in the gain domain and risk seeking in the loss domain. This would lead to most individuals picking A and D when decision (i) and decision (ii) are presented in isolation.

Since individuals that choose A and D were framing narrowly (this option is dominated by B and C) they may be compared to the rest of the sample, although it is not possible to control for risk preferences. θ will serve as a proxy for narrow framing and therefore be used as dependent variable. Although socio-demographics seemed to have little effect on narrow framing, gender and age may influence the propensity to frame narrowly (Rabin & Weizsäcker, 2009).

A small-stake lottery choice task was used to measure loss aversion (see table 1; this was developed by Gächter et al., 2010). Gächter et al. (2010) showed that this measurement for loss aversion is highly correlated with loss aversion for riskless

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choice2_{and that both are influenced by the same factors, indicating that it really}

captures loss aversion.

Several authors show that a small-stake lottery task such as table 1 measures loss aversion and not risk aversion (Rabin, 2000; Rabin & Thaler, 2001; Fehr & Goette, 2007). Risk aversion regards to the concavity of the utility function in expected utility theory. Expected utility theory cannot explain the turning down of small favorable gambles, since this would imply an unrealistically high level of risk aversion in high-stake gambles (Rabin, 2000). Rabin & Thaler (2001) show that an individual that turns down the 50/50 bet to lose $10 or win $11 would, as suggested by risk aversion, turn down a 50/50 bet to lose $100 no matter how large the winning amount would be. Rabin (2000) points out that everyone should be risk neutral in such small-stake gambles. Risk aversion therefore suggests that everyone will accept all non-negative lotteries, that is, lotteries #1 to #5. When individuals do turn down some of those lotteries, this would therefore rather suggest that they are loss averse than risk averse.

Following the cumulative prospect theory of Tversky and Kahneman (1992), an individual will be indifferent between accepting and rejecting the gamble if 𝑤+(1

2) 𝑣(𝐺) = 𝑤

−₍1

2) λ𝑣(𝐿). 𝐺 stands for the gain in the lottery and L for the loss, 𝑣(𝐺)

and 𝑣(𝐿) denote the utility of the outcome for the gain and loss respectively, 𝑤+(1

2) and

𝑤−(1

2) are the probability weights for the 0.5-chance to win 𝐺 or lose 𝐿, respectively.

Assuming that the weight function is the same for gains as for losses, 𝑤+ = 𝑤− (Prelec, 1998) and assuming that linearity holds for small stakes, 𝑣(𝑥) = 𝑥, this will result in λ = 𝐺/𝐿. This loss aversion factor is thus simply the gains divided by the losses at the individuals switching point, the last lottery that is accepted. Since loss aversion was correlated with age, education, gender and household income, those variables will be controlled for (Gächter et al., 2010; Brooks & Zank, 2005).

2_{Loss aversion in riskless choice (the endowment effect) is measured by letting participants elicit}

willingness-to-ask (WTA) for a good with which they are endowed and willingness-to-pay (WTP) for the good if they do not own it. The WTA to WTP ratio is a measurement for the loss aversion in riskless choice.

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Table 1. Small-stake lottery choice task by Gächter et al. (2010).

Lottery Accept Reject

#1. If the coin turns up heads, then you lose $2,-; if the coin turns up tails, you win $6,-. ⃝ ⃝ #2. If the coin turns up heads, then you lose $3,-; if the coin turns up tails, you win $6,-. ⃝ ⃝ #3. If the coin turns up heads, then you lose $4,-; if the coin turns up tails, you win $6,-. ⃝ ⃝ #4. If the coin turns up heads, then you lose $5,-; if the coin turns up tails, you win $6,-. ⃝ ⃝ #5. If the coin turns up heads, then you lose $6,-; if the coin turns up tails, you win $6,-. ⃝ ⃝ #6. If the coin turns up heads, then you lose $7,-; if the coin turns up tails, you win $6,-. ⃝ ⃝

Trait mindfulness was measured by the Mindful Attention Awareness Scale (α = 0.9173_{) (MAAS; Brown & Ryan, 2003), a scale consisting of 15 items with a 6-point}

Likert scale of which the mean will indicate the individual’s mindfulness index (for the entire list of items see Appendix part 3). Although many different mindfulness scales exist, the MAAS is used due to its focus on attention, awareness and the use of the ‘automatic pilot’. These aspects are important for decision making, the use of the automatic pilot is also closely related to System 1 processes. Furthermore, this scale is easily to understand for non-meditators. Trait mindfulness was hypothesized to be negatively correlated with loss aversion and narrow framing:

Hypothesis 1: Mindfulness is negatively correlated with loss aversion (λ) Hypothesis 2: Mindfulness is negatively correlated with narrow framing (θ)

3_{A Cronbach’s Alpha of 0.917 for a one-dimensional scale consisting of 15 items suggests a good}

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4. Results

151 participants residing in the United States completed the survey on July 7th_,

2018. 26 participants displayed non-monotonicity in the loss aversion part and were therefore taken out of the analysis altogether4_{. This resulted in a total of 126}

respondents (59 women and 66 men; mean age = 37.24, SD = 13.4, age range = 19-80). The mean score of the MAAS was 4.17 (SD = 0.91, range = 1.33-6, α = 0.917).

As shown in table 1, 81.6% of the respondents accepted only lotteries with a positive expected value and were therefore loss averse (implied loss aversion >1.00). 6.4% accepted all lotteries with a non-negative expected value and were therefore risk neutral (λ = 1). 12% accepted lottery #6 which has an negative expected value (λ ≤ 0.87). The median respondent accepted lottery #1 and #2, which results in a median λ of 2.

Table 2. Small-stake lottery choice task acceptance rates and implied λ

Percent Implied acceptable loss

Implied loss aversion

7) Reject all lotteries 4.8 $<2 >3.00

6) Accept lottery #1, reject lotteries #2 to #6 9.6 $2 3.00 5) Accept lotteries #1 and #2, reject others 37.6 $3 2.00 4) Accept lotteries #1 to #3, reject others 16 $4 1.5 3) accept lotteries #1 to #4, reject others 13.6 $5 1.2 2) Accept lotteries #1 to #5, reject other 6.4 $6 1.00

1) Accept all lotteries 12 $≥7 ≤0.87

Median = 2.00

In the second part of the survey, almost 80% (98/125) choose the sure gain of A over the risky B, and almost 60% (73/125) choose the risky loss of D over the sure loss option C. This is in line with prospect theory, in which individuals are risk averse in the gain domain, and risk seeking in the loss domain. 39% of the participants choose the dominated A-D combination, whereas only 2% choose the dominating B-C combination (see table 2). Since only 3 participants choose B-C, this sample size was too small to directly compare this with θ.

4_{Participants that switched more than once in the loss aversion task have non-monotonic preferences}

that are incompatible with any economic theory. They were therefore assumed to have not understood the task correctly. Since the narrow framing task also assumes monotonicity, those participants were entirely taken out of the analysis.

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Table 3. Choice combination frequencies

A and C A and D B and C B and D

0.39 0.39 0.02 0.19

Figure 2 shows the relation between the mean MAAS index scores with 95% confidence intervals and the loss category. Although the MAAS index and λ may be slightly positively correlated, this effect is not clear. This is confirmed by a Spearman rank order correlation (ρ = 0.0858; p < 0.34).

The MAAS index seems also not to be related to a difference in θ, as can be seen in figure 3. A Mann-Whitney U test gives p < 0.682, indicating that the means are not different.

Figure 2. Relationship between loss aversion and MAAS index

Figure 3. Relationship between dominated/non-dominated choice and MAAS index

<0.87 1 1.2 1.5 2 3 >3 3 4 5 6

Imp

li

ed

l

o

ss

av

ersi

o

n

MAAS index

4.21 4.15

MAAS index

θ 1 -θ

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Additionally, regressions have been run. Model (1) is an ordered probit of the MAAS index on the lottery choice category, in Model (2) also all control variables are included. Model (3) is a probit of the MAAS index on the dominated choice, Model (4) includes all available control variables. All participants reported to have obtained at least a high school degree, so the category ‘less than high school degree’ is not relevant (see Appendix part 4).

Table 4. Regression estimates of Lottery choice and Dominated choice

Dependent variable Method

Lottery choice category Ordered probit Dominated choice Probit (1) (2) (3) (4) MAAS index 0.148 (0.111) 0.101 (0.110) 0.045 (0.128) 0.074 (0.144) Female 0.433 (0.198)** 0.286 (0.242) Age 31-49 -0.123 (0.220) -0.477 (0.273)* Age 50+ 0.544 (0.292)** -0.274 (0.349) Income $40k - $70k 0.113 (0.225) -0.080 (0.288) Income $70k+ 0.076 (0.240) 0.334 (0.303) Bachelor degree -0.398 (0.214)* -0.109 (0.269) Master degree or higher 0.019 (0.259) 0.094 (0.352) N 125 125 125 125

The numbers in parentheses are robust standard errors; *significant at 10%; **significant at 5%

As indicated by the relation graph, the MAAS index seems to have a positive but insignificant effect on the lottery choice category. More mindful individuals tend to be more loss averse. This is in contrast with hypothesis 1, which predicted a negative relation. In line with previous research, females were more loss averse and the oldest age category as well (Gächter et al., 2010; Brooks & Zank, 2005). A Bachelor degree

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decreases loss aversion, but individuals with a Master degree or higher score the same as individuals with a high school degree. Income was found to have no effect.5

The MAAS index has no significant effect on the likelihood to choose the dominated option. The mean of θ is slightly higher than that of 1 – θ (figure 3), which points in the opposite direction of hypothesis 2. In model (4) there is a slightly significant negative effect of the middle aged on choosing the dominated option. For older individuals the sign is also negative, but not significant.

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5. Discussion and Conclusion

This thesis studies the effects of mindfulness on loss aversion and narrow framing. It was hypothesized that there would be a negative relation between those factors. In previous studies mindfulness was found to aid in coping with different decision biases, thereby increasing decision making. However, in this thesis mindfulness was found to have no significant positive impact on loss aversion and narrow framing and the regressions even pointed in the opposite direction. This shows that mindfulness is a less promising way of improving decision making in the field of decision making under risk, although future research is needed.

Several factors may have influenced the validity of this thesis. The fact that the study was conducted through an online platform allowed for less control than a

laboratory setting. Furthermore, this also denied the possibility to use real incentives, since it was not possible to play out the gambles and pay accordingly through the online platform. In a laboratory setting respondents could have been given an initial cash endowment, which would allow for the possibility of negative payouts of the gambles. Although Rabin and Weizsäcker (2009) showed that real payouts had no significant effects in comparison to hypothetical payouts for the narrow framing task, it may affect the decisions in the loss aversion task.

Besides the MAAS many other mindfulness scales exist which all have a different operationalization of mindfulness. Although the focus on the ‘automatic pilot’ in the MAAS seems relevant for the problems in this thesis, some items regarding mindfulness that are not captured in this scale me play a role as well (Brown & Ryan, 2003). The FFMQ, for example, has a broader measurement of mindfulness through five different facets (Baer et al., 2008). The addition of the facets “nonjudging of inner experience” and especially “nonreactivity to inner experience” may be relevant since feelings such as regret may play a role in loss aversion and narrow framing.

However, since this thesis found no effects of mindfulness, there was no follow-up study assessing the underlying factors.

The design of both tasks could also partly explain the results. The loss aversion task in this thesis by Gächter et al. (2010) seem to really capture loss aversion. Future research may, however, replicate it by using real-stakes and different mindfulness scales. The narrow framing task does show that 39% of the individuals frame narrowly, but it does not create a very good control group. Since the

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preferences of the other individuals cannot be controlled, a large proportion of them may be framing narrowly as well. Although those individuals do not choose a

dominated option, it does not show that they are aware of this fact. This

approximation may weaken the results of this tasks. Future research should study the effects of mindfulness on narrow framing in a different set-up. Researchers could develop a game comparable to the Iowa Gambling Task (see section 2.2) to measure narrow framing. Although Kumar and Lim (2008) developed a novel way to study narrow framing in a real-life situation, it is harder to also gather related mindfulness data. Especially the induction of mindfulness to study causation is complicated in a real-life setting as opposed to a laboratory environment.

Despite the fact that no significant effects of mindfulness on loss aversion and narrow framing have been found, this thesis hopes to bring the two fields of research closer together. Given that investments are related to cognitive biases and emotions, mindfulness may still improve other areas of decision making under risk. On the other hand, this thesis also points to the possible limitations of mindfulness. Almost all mindfulness research studies its positive effects. However, there may also be

disadvantages of mindfulness on decision making. At this point, this is open for future research.

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7. Appendix

Part 0. Introduction

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Part 2. Loss aversion question (Gächter et al., 2010)

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Mindfulness and Decision Making under Risk