Does a Higher Level of Cognitive Empathy Make People More Tolerant
Towards Unfair Behavior in the Ultimatum Game?
Ciska van der Zijden – Student number: 11143819 Thesis submitted for: MSc Economics, track BE> (15 ECTS)
University of Amsterdam
Supervisor: G. Romagnoli PhD Second examiner: Dr. J.B. Engelmann
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Abstract
This thesis focuses on the relationship between cognitive empathy and respondents’ behaviour in the Ultimatum Game. 112 respondents were randomly sampled reacting to offers from 23 real proposers, and filling in an IRI (Interpersonal Reactivity Index) questionnaire. In the study, proposers made their offer to a robot, and were asked after their choice if the offer could be used with a human respondent. Respondents accepted lower offers in the treatment and their level of cognitive empathy did not have a significant influence on their probability to accept. However, the data indicates that respondents with lower cognitive empathy are more affected by the treatment than those with high cognitive empathy scores. More research on this interaction is necessary.
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Table of contents
Introduction ... 4
Literature ... 6
Empathy ... 6
Empathy and Prosocial Behavior ... 7
Measuring Empathy ... 11 Method ... 12 Participants ... 12 Design ... 13 Variables ... 15 Hypotheses ... 15 Results ... 16 Proposers ... 16 Respondents ... 18 Discussion ... 21 Appendices ... 31
Appendix A – Proposer experiment ... 31
Appendix B – Respondent experiment ... 34
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Introduction
People dislike being treated unfairly. To signal their disapproval, some are even willing to give up their own payoffs. This is true even when their signal cannot lead to potential higher payoffs in the future as in one round anonymous games. (Güth,
Schmittberger, & Schwarze, 1982; Bolton, 1991; Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). Clear examples of this behavior can be found in the Ultimatum Game. In this game two players interact. The proposer is given an endowment and has to decide how to divide the money between himself and the respondent who can either accept or reject the offer. A rejection results in zero payoff for both players (Kahnemann, Knetsch, & Thaler, 1986; Thaler, 1988). Although rational behavior for the respondent would be to accept any payoff greater than zero, in reality offers lower than 30% of the endowment are generally rejected because they are perceived as unfair. On the other hand, proposers also show a preference for fairness as around 25% of them offer an equal split. (Forsythe, Horowitz, Savin, & Sefton, 1994; Camerer & Thaler, 1995; Page & Nowak, 2002; Camerer, 2003; Rotemberg, 2008). Many examples of Ultimatum Games occur every day. From children rejecting candy when someone else got more or better candy, to striking workers that reject an unfair wage.
However, fairness is an abstract concept and can strongly depend on both the context of the situation and one’s capacity for perspective-taking or empathy for others which could influence the idea of fair behavior in interaction (Kahnemann et al., 1986; Schotter, Weiss & Zapater, 1996; Kirman & Teschl, 2010). Research has found that more empathetic proposers offer fairer divisions of the endowment (Eisenberg & Miller, 1987; Page & Nowak, 2002; Artinger, Exadaktylos, Koppel & Sääksvuori, 2010; Edele, Dziobek, & Keller, 2013). Equally, the behavior of the respondent might also be affected by their empathic awareness, as it can affect the desire to punish unfair behavior (Bowles & Gintis, 2003).
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This paper researches if more empathic respondents will (more willingly) accept lower offers and if this effect is stronger when they know the proposer was influenced by
externalities during their choice. The empathic respondent might have an understanding that they would have made a similar choice if they would have been the proposer, and therefore accept behavior that they would normally classify as unfair and unacceptable.
Several variables that are already shown to have an influence on acceptance in the Ultimatum Game might be moderated by (cognitive) empathy. Intentions of the proposer are an important factor to consider during bargaining and interaction. When someone meant to do good, it is easier to forgive them for a bad decision. Identical offers trigger different responses in the Ultimatum Game based on the proposer’s environment (Falk & Fischbacher, 2006). This effect might be stronger when the respondent has more cognitive empathy, since they will have a better understanding of the proposer’s intentions rather than judging them solely on the result of their decision.
Similarly, reciprocal preference, the idea that people want to be good to those who were good to them (and the vice versa), also affects behavior in the Ultimatum Game (Rabin, 1993; Falk & Fischbacher, 2006). When someone offers an unfair proportion to the
respondent, the respondent will probably signal their discontent by rejecting the offer. In that case, the reciprocal preference is stronger than the desire for the proposed amount of money, respondents are willing to give up their payoff to punish the proposer’s unfair behavior. On the other hand, when the proposer was initially interacting with someone else or they had incomplete information about the respondent the influence of reciprocal preference is lessened (Page, Nowak, & Sigmund, 2000; Kuperman & Risau-Gusman, 2008; Xianyu, 2010).
Because empathy significantly affects reciprocal behavior, the relationship between reciprocal preference and responses in the Ultimatum Game could be moderated by empathy (Pelligra, 2011).
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Expectations also influence the acceptance level of the respondent. Those who expect a high offer reject a low offer more often than those who expected a low offer. (Sanfey, 2009; Azar, Lahav & Voslinsky, 2015). Perspective taking increases predictability of the other persons’ behaviour. When the respondent has reason to expect a low offer, it will be easier to accept a low offer.
If this study finds empathy to be significant in the Ultimatum Game, it is interesting to research its role in the relationships with these variables.
Literature Empathy
Empathy plays an important role in human relationships and society. Adam Smith noted its relevance for economic interactions saying, “empathy is the source of our fellow-feeling for the misery of others, that it is by changing places in fancy with the sufferer, [it is] that we come either to conceive or to be affected by what he feels” (Smith, 1759). In this paper, Smith describes that empathy is based on imagination and the ability to understand another person’s perspective. However, he notices that in some situations people merely adopt the emotions, and may feel actual pain, sorrow, or joy when they observe someone
experiencing any of these feelings.
The definition of empathy was rather vague for a long time. Titchener (1909)
emphasized the awareness of another person’s feelings, unconscious projection, and imitation of emotions. This definition was commonly accepted in psychology until 1934, when Mead added the cognitive component of understanding of the other’s feelings. Even though empathy had been a key concept in psychological research and therapy, the definition was still
theoretical until Rogers (1959) started using scientific methods to create a practical framework around the concept. Researchers from other fields (e.g. economics) started
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studying the effects from and reasons for empathy too, resulting in various definitions of the concept (Duan & Hill, 1996).
Although various descriptions of empathy are proposed, they can roughly be divided into two main concepts; emotional and cognitive empathy. Emotional or affective empathy is defined as the adoption of the emotions of another person (McDougall, 1908; Stotland, 1969; Mehrabian & Epstein, 1972; Batson, Fultz, & Schoenrade, 1987). Cognitive empathy or Theory of Mind is the ability to understand the perspective of the other person (Piaget, 1932; Dymond, 1949; Barrett-Lennard, 1981; Astington, Harris, & Olson, 1990; Batson, Early, & Salvarani, 1997). Furthermore, in more current literature there are also researchers who consolidate the two descriptions in their research (Hoffman, 1984; Hogan, 1969; Duan & Hill, 1996; Blair, 2005; Singer, 2006; Galinsky, Maddux, Gilin, & White, 2008; Smith, 2010). Empathy and Prosocial Behavior
Previous literature has shown that empathy is closely related to prosocial behavior. Meta-analyses on the relationship between empathy and prosocial behavior have showed that the method of empathy measurement is relevant for the strength of the relationship. For example, a common method to measure empathy is to show pictures or tell short stories and ask the participant how they feel afterwards. When tested with meta-analytic procedures, there is no significant relation found between prosocial behavior and empathy scores derived from this method. However, since pictures or stories are clearly hypothetical situations they might not trigger empathy like a real situation would. Besides, participants scored
significantly higher when the interviewer was of the same gender as themselves. These findings suggest that picture/story procedures might not give valid results and the relationship between prosocial behavior and empathy should be studied with other methods. This idea is supported by meta-analyses on the relationship between empathy scores from questionnaires
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or other self-reported empathy measurements and prosocial or altruistic behavior, which did find significant results (Eisenberg & Miller, 1987).
A longitudinal study on the consistency of a prosocial personality and its relationship with empathy also supports these ideas (Eisenberg et al., 2002). 32 participants were
interviewed 12 times, once every 2 years, starting at age 4 or 5 and finishing the experiment at age 25 or 26. Besides interviews and self-reports by several questionnaires, questionnaires were also filled in by mothers or friends during some of the testing moments. The results show strong evidence of a disposition for prosocial behavior is consistent over time.
Additionally, prosocial behavior and empathy were also found to be significantly correlated. Self-reported prosocial behavior was related to self-reported empathy scores from up to 16 years earlier.
Young children are popular and interesting subjects for research on Theory of Mind. The ability to see other perspectives normally develops around age 4 or 5 (Gopnik &
Astington, 1988; Wellman, Cross, & Watson, 2001). Comparing peers at these ages can therefore give great insights in the relationship between cognitive empathy and other
variables. In an Ultimatum Game experiment, children with developed Theory of Mind made higher offers (Takagishi, Kameshima, Schug, Koizumi, & Yamagishi, 2010). 68 preschoolers were divided into two groups, using the false belief task. During this task, they observe a short movie in which a person stores a ball in a box and leaves the room. While they are out of the room, another person comes in and moves the ball to a new location. The child is then asked where the first person will look for the ball when they come back. Children who can take perspective, understand that the first person will look in the box, since they are not aware of the replacement. After the video, the children had to play an Ultimatum Game in which they had to divide 10 candies. Of the proposers who passed the test, 83% offered 5 or more candies
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to the other person, whereas from those who failed the test only 36% made such an offer (Takagishi, Kameshima, Schug, Koizumi, & Yamagishi, 2010).
Another study researched the difference in Ultimatum Game offers made by normally developing children and children diagnosed with an Autism Spectrum Disorder. Autism is characterized by problems with Theory of Mind, especially in children (Baron-Cohen, Leslie, & Frith, 1985). Using this information, Sally and Hill (2006) researched how lack of cognitive empathy affects offers and responses in strategic games. Children with autism made
significantly lower offers in the Ultimatum Game, more than 25% of the sample made an offer of zero, while around 30% offered an equal split. On the other hand, in the control group with normally developing children, less than 5% offered nothing to the respondent and more than 50% proposed an equal division. The study concludes that a higher level of (cognitive) empathy leads to more prosocial behavior and higher offers in the Ultimatum Game.
However, some studies do not find significant evidence for this relationship or only for specific cases. Edele et al. (2013) studied the relationship between Dictator Game behavior and different measures of empathy. To avoid mood effects participants were re-invited to assess empathy scores 6 months after playing the Dictator Game. The study found that emotional empathy is a significant predictor for prosocial behavior, but cognitive empathy is not.
Conversely, Artinger et al (2010) found no evidence of emotional empathy affecting prosocial behavior. Instead they found that a disposition for cognitive empathy positively affects offers in Dictator Games. In their Ultimatum Game experiment, no relationship is found between offers and either cognitive or emotional empathy. The paper points out that cognitive empathy can both increase offers in the Ultimatum Game, when participants feel bad about treating the respondent unfairly, or decrease offers, when the proposer acts rational and tries to maximize their offer by minimizing the difference between their offer and the
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minimum acceptance level. The authors suggest that these two effects might have cancelled each other out.
Theoretical studies argue that prosocial behavior could even be a strategic and rational choice, and that empathy might benefit proposers in an Ultimatum Game in the long term. A strong desire to punish unfair behavior seems irrational, because it takes effort or other costs to punish while there are no clear benefits. However, in the long run, reciprocity can be a strategic and more beneficial strategy. Prosocial interaction can lead to higher payoffs. When an employer promises that he will pay a high wage and the employee promises that he will work very hard, it would be rational for both parties to break their promises. However, if they do keep their promises, their business will be more successful than businesses with rational employers and/or employees (Gintis, Bowles, Boyd, & Fehr, 2003; Bowles & Gintis, 2003).
Page and Nowak (2002) show that in a continuous Ultimatum Game, a small group of empathic players can lead to a dominant strategy of offering close to a fair division. In their model, empathic behavior is defined as the ability to take perspective and predict what the respondent will do. The best prediction of the minimal acceptable offer for an anonymous respondent would be the minimal acceptable offer for oneself. When proposers make an offer that is equal to the minimal offer they themselves would accept, they maximize their potential payoff. Other strategies, like expecting rational behavior, or always offer an equal split, result in lower expected payoffs. Making an offer below the minimum you would accept yourself, has a higher chance to be rejected and result in zero payoffs. Offering above your own minimal acceptable division will likely be accepted, but since a lower offer might have been accepted too, it does not maximize your own payoff.
Most literature about the effects of empathy on prosocial choices focuses on pro-active decisions, such as sharing, donating or choosing for fair divisions of payoffs. Very little research is done about the relationship between empathy and responses towards behavior of
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others. Especially unfair behavior could trigger various responses, which might be related to a disposition for perspective taking. For instance, respondents in Ultimatum Games might be predicted by their level of empathy. When one can take the perspective of the other party, they might understand that the unfair treatment may have been necessary or inevitable, and that the decision maker did not mean to do them harm.
Cognitive empathy is an especially interesting variable to research in this context. In most examples of real life Ultimatum Games the decision of the proposer was not based on emotional arguments, but on logic and strategic decision making. It happens often that ‘offers’ were not directed to a specific person or even affected a different group of people than
intended, an empathic person might understand that they were just unlucky instead of targeted victims of unfair treatment (Bowles & Gintis, 2003).
Measuring Empathy
Questionnaires measuring empathy also vary in focus. Hogan (1969) designed the Empathy Scale (EM), a 64-item scale considering both cognitive and emotional components. However, the components are not separated in the results of the test, and the scale is argued to describe general social skills rather than empathy (Davis, 1996; Baron-Cohen & Wheelwright, 2004; Gunther, Evans, Mefford & Coe, 2007). The Emotional Empathic Tendency Scale (EETS) is 33 item questionnaire measuring only emotional empathy. Participants rate each statement on a +4 (very strong agreement) to -4 (very strong disagreement) scale and the sum of all responses represents one’s level of emotional empathy (Mehrabian & Epstein, 1972). It is quite commonly used, but some argue that not all statements represent emotional empathy (Davis, 1980; Elizur & Rosenheim, 1982; Mehrabian, Young, & Sato, 1988; Williams, 1989; Gunther et al., 2007). In 1980, Davis emphasizes that empathy scales should take into account the multidimensionality of the concept. He notes that Hogan’s scale does this, but argues that combining all aspects into one empathy score would be meaningless and gives no insights
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into the influences of each aspect on social behavior. Therefore, he creates the Interpersonal Reactivity Index (IRI), a questionnaire measuring four aspects of empathy: perspective taking, empathic concern, fantasy, and personal distress. Participants report on a 5-point Likert scale how much each of the 28 statements describes them. The questionnaire takes about 5 minutes to complete, is well-validated and still regularly used in experiments (Davis, 1983;
Cliffordson, 2001; Pulos, Elison, & Lennon, 2004; Rogers, Dziobek, Hassenstab, Wolf, & Convit, 2007; Dziobek et al., 2008; Edele et al., 2013). Another highly validated empathy test is the Multifaceted Empathy Test (MET). Instead of a self-reporting questionnaire, it uses photographs of people in emotional situations, and is designed to research cognitive and emotional empathy in adults with autism. An advantage of the MET is the implicit way of measuring which minimizes social desirability bias. However, the test takes approximately 35 minutes to complete, which makes it less suitable for small experiments (Dziobek et al., 2008; Wolf et al., 2015). The most recent well-known empathy scale is the Empathy Quotient (EQ). The questionnaire has 60 statements (40 relating to empathy and 20 control questions) which are rated on a 4-point scale, and is used mostly for clinical research on autism and
schizophrenia. The authors argue that emotional and cognitive components of empathy are not easily disentangled and therefore the outcome of the questionnaire is a general empathy measure and there are no different subscales (Baron-Cohen & Wheelwright, 2004; Lawrence, Shaw, Baker, Baron-Cohen & David, 2004; Billington, Baron-Cohen, & Wheelwright, 2007; Koelkebeck, et al., 2010).
Method Participants
The data was collected by two online experiments, which could both be filled in on any computer or smartphone with internet (Appendix A and Appendix B). 135 people
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participated, a small group of 23 participants playing the proposers in the first part of the experiment, and the remaining 112 participants playing respondents in the second part. Even though there were 6 age groups in the survey, in the data the last two groups (‘51-60’ and ‘60 or older’) were combined, because together they account for only 7 participants. Further, when the participants were asked for their gender, the options were ‘male’, ‘female’ and ‘other’. All participants identified as either male or female, so the variable was made binary.
The proposer group existed of 9 males and 14 females, ages ranged from 17 and 52. Offers from real proposers are necessary to give the respondents the power to punish unfair behavior. If offers would have been hypothetical, signaling disagreement would be
meaningless since there is no proposer that could be punished and a rejection will only make the respondent lose their own payoff.
Since the research focuses on the behavior of respondents this group of participants was much larger, 71 males and 64 females. Ages ranged from under 20 to over 61, with 66% of these participants aged between 21 and 40.
Design
First, in both parts of the experiment, a short description of the Ultimatum Game is given and the participant is informed that one pair of players will be selected at the end of the study for payoff. They can leave their email address at the end of the experiment and if they are selected, they will be paid based on the earnings they made in the game.
In the first part of the experiment, the proposers were given an endowment of 10 tokens and asked to make an offer to a random respondent who they would be matched with later in the experiment. In the second part of the experiment, the respondents were informed that the proposers made their choices earlier in the study, and that they were matched with a random proposer. They then were presented with the offer, and had the option to either accept
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or reject it. To finish the experiment, the respondents had to fill in the IRI questionnaire to measure their cognitive empathy (Appendix C). The IRI questionnaire was chosen for this experiment because it is self-administered, not very time-consuming, has a separated outcome for Perspective Taking (often used for measuring cognitive empathy), and has proven validity (Davis, 1983; Cliffordson, 2001; Pulos, et al., 2004).
In the treatment, proposers were told they are playing against a robot. The robot’s characteristics were kept vague and were described by the following sentence: ‘The respondent you are paired with is a robot. The robot's behavior is ambiguous, he has some artificial intelligence’. This message was created to suggest that the robot may behave more rational than a human. Apart from that information the robot should feel anonymous, just like the respondents in the control group feel anonymous to the proposer.
After making their decision, the proposers in the treatment were asked if their offer could instead be used against a human respondent. Since they might have expected that robots are more rational than humans and therefore made a lower offer to the robot than they would have made to a human respondent, they were offered a compensation in case the human respondent would reject their offer. Proposers were informed the compensation offered to them was calculated to result in an expected weighted payoff of 5, based on previous data on acceptance rates of certain offers (Schotter, et al., 1996; Azar et al., 2015; Van ’t Wout, Kahn, Sanfey, & Aleman, 2006).
The respondents who were playing in the experiment were informed that the proposer made his offer against a robot and they were given the same description of the robot’s
characteristics as was given to the proposer. Further they were informed that after making his decision, the proposer agreed that the offer could be used against a human respondent, but not that he would be compensated in case they rejected his offer. This information was not
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revealed because it would decrease the idea of power to punish for the respondent and could therefore affect their decision.
Following this information, the offer of the proposer was revealed, and the respondent could either accept or reject it.
In the control version, both proposers and respondents played a normal Ultimatum Game.
Variables
In the first part of the experiment, only basic information was registered. Participants were asked to fill in their age (in years), gender (male, female or other), and optionally their email address. Their offer was recorded and a binary variable was created for being in the treatment or the control group.
In the second part of the experiment, the data for the research was gathered. The dependent variable is Acceptance, a binary variable which is 1 if the offer was accepted and 0 if it was rejected. The independent variable of interest is Perspective Taking, based on the PT scale of the IRI questionnaire, which has a minimum score of 0 and a maximum of 28. Further, 4 control variables are taken into account. Robot, a binary variable which is 1 if the proposer was playing against a robot, and 0 if not. Offer, a continuous variable between 1 and 5, representing the offer that was made by the proposer. Age, ranging from 1 to 5, an ordinal variable with five age groups (20 or younger, 21-30, 31-40, 41-50, 51 or older). Male, a binary variable which is 1 if the participant was male and 0 if they were female.
Hypotheses
Based on previous literature and findings, three hypotheses were formed.
1. We expect proposers to make lower offers in the treatment than in the control. Interaction between humans and robots is more rational (Blount, 1995; Moretti & Di
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Pellegrino, 2010). The human proposer might expect that the robot has no emotions and is therefore less likely to reject unfair offers.
2. We expect respondents to accept lower offers in the treatment than in the control. When a respondent is aware that it was not the intention of the proposer to treat him unfairly, he is more willing to forgive this behavior and accept lower offers. Further, the influence of reciprocal preference is only relevant in the control group. The
respondent might react with unkind behavior to an unkind offer, but this punishment is only logical when the offer was made to a human.
3. We expect that respondents with low cognitive empathy in the treatment may have difficulties understanding that the unfair behavior was not directed at them but at the robot. Therefore, we expect to find a treatment effect and see different results between respondents with low/high cognitive empathy in the treatment, but no or significantly less difference in the control group.
Results Proposers
Regression results show that both age and gender were not significantly related to treatment assignment in the group of proposers. Further, proposers in the control group offered 3,9 on average, whereas proposers in the treatment offered 3,2 on average. However, in the regression results, we do not find significant results for the relationship between offers and the treatment. Suggesting that playing against a robot does not affect offers. Further, the R2 is very small in all regressions, implying that the explanatory power of the variables is very low. These results should be interpreted with caution, there are very few control variables and the sample exists of only 23 proposers.
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Table 1: Regression results for sample of proposers. Treatment Offer Offer
(1) (2) (3) Robot -0,675 -0,706 Age -0,052 -0,129 Male 0,005 -0,013 Constant 0,766** -3,875*** 4,187*** R2 0,013 0,060 0,070 F-test 0,12 1,57 0,66 N 23 23 23 Notes. *p<0,10, **p<0,05, ***p<0,01. Robust.
To have a closer look on the differences between the treatment and control group, the offers are visualized in figure 1.
Figure 1: Proposers offers
0% 5% 10% 15% 20% 25% 30% 35% 40% 0 1 2 3 4 5 6-10 Per cen ta ge o f s amp le Offered to respondent control treatment
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There were no proposers who offered more than half of the endowment. However, in both the control and the treatment, a fair division (5-5) was proposed quite often, accounting for respectively 26,7% and 37,5% of the offers in the treatment and the control group. In the control group we observe a downward trend, a fair division being the most popular choice, less offers of 4 and 3, and only a few offers of 2. In the treatment, we observe a similar trend, but with a break at 4.
Respondents
Treatment assignment in the respondents experiment should be unrelated to the age, gender and Perspective Taking scores of the participant.
Table 2: Random assignment of data in the respondent group Treatment Perspective taking 0,011 Age -0,017 Male 0,074 Constant 0,558*** R2 0,018 F-test 0,56 N 112 Notes. *p<0,10, **p<0,05, ***p<0,01. Robust.
Regression results show that none of the pre-treatment variables is related to treatment selection in the respondent group and also the R2 is very low, implying that treatment is randomly assigned. Offer was not included in this regression, since proposers made different offers in the treatment, so offer is correlated with the treatment.
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Figure 2: Respondent reactions to offers
In the control group, all offers lower than 4 were rejected. However, in the treatment, respondents were willing to accept much lower offers. More than half of the offers of 1 were accepted. Remarkable is that 64% from the offers of 1 were accepted, whereas from all offers of 2 only 32% was accepted, and from all offers of 3 52% was accepted.
Figure 3: Average Perspective Taking scores for different subgroups
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% co ntro l tre atm en t co ntro l tre atm en t co ntro l tre atm en t co ntro l tre atm en t co ntro l tre atm en t co ntro l tre atm en t co ntro l tre atm en t 0 1 2 3 4 5 6-10 Per cen ta ge o f s amp le
Offered to the respondent
rejected accepted rejected accepted 17,3 18,0 16,4 18,1 16,4 0 2 4 6 8 10 12 14 16 18 20 PT sc or e
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Using the results of the IRI questionnaire, the score for Perspective Taking was calculated for each respondent. Seven questions were used to calculated the score, two of which are reversed-scored. Overall, perspective taking scores were between 7 and 26, on the 0-28 scale. Gender differences are significant on a 10% level. Age does not significantly affect PT scores.
A logit regression was used to find the coefficients to predict the dependent variable, resulting in the following model: 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴� = −4,517 + 2,667 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐴𝐴 – 0,094 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐴𝐴 ∗ 𝑃𝑃𝑃𝑃 + 0,077 ∗ 𝑃𝑃𝑃𝑃 + 0,918 ∗ 𝑂𝑂𝑂𝑂𝑂𝑂𝐴𝐴𝑂𝑂 + 0,052 ∗ 𝐴𝐴𝐴𝐴𝐴𝐴 − 0,229 ∗ 𝑀𝑀𝐴𝐴𝑀𝑀𝐴𝐴.
Since the dependent variable is binary, the predicted value is a log-odds unit, representing the log of the chance that the outcome is 1. Visualized by the formula:
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴� = ln (1−𝑝𝑝𝑝𝑝 ) in which p is the probability that Acceptance=1. Then, the log-odds
were translated into probabilities, using the following formula: 𝐹𝐹(𝑥𝑥) = 1
1+𝑒𝑒−(𝑦𝑦�). Graphing the
probabilities gives an interesting view into the data.
Figure 4: Perspective taking and probability of accepting the offer
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 0 5 10 15 20 25 30 Ex pe ct ed pr oba bi lit y to a cc ept Perspective taking
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The probability to accept an offer, keeping all other variables constant, is lower for
participants with lower perspective taking scores. The treatment trendline is rather flat, but in the control group, the influence of the perspective taking score seems to be quite strong. The regression results are summarized in table 3.
Table 3: Average marginal effects on probabilities.
(1) (2) (3) (4) (5) (6) (7) Robot 0,059 0,200*** 0,200*** 0,192** 0,194*** 0,474 PT 0,013 0,004 0,003 0,014 Robot*PT -0,017 Offer 0,167*** 0,166*** 0,165*** 0,163*** 0,163*** Age 0,011 0,010 0,009 Male -0,046 -0,040 -0,041 R2 0,002 0,193 0,196 0,010 0,195 0,196 0,202 F-test 0,31 20,76*** 21,44*** 1,46 21,46*** 21,94*** 23,17*** N 112 112 112 112 112 112 112
Notes. Dependent variable: Acceptance. *p<0,10, **p<0,05, ***p<0,01. Robust.
Offer is positive and highly significant in all regressions. Further, when the proposer is playing against a robot, the treatment also has a significant and positive influence on the probability of accepting in most of the regressions. The significant results for Robot are (close to) 0,200, meaning that average marginal effect, or the expected increase in the probability that the offer is accepted, is 0,2, when going from the control to the treatment, holding all other independent variables constant.
Discussion
As expected in the first hypothesis, we find that proposers make higher offers to human respondents than to robots. Remarkably few offers of 4 were made in the treatment. It
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looks like some people offer a fair division no matter the characteristics of the respondent, but the remainder of the proposers seemed to assume that the robot would behave more rationally and made offers of 3 or lower.
As expected in hypothesis 2, the respondents accepted lower offers in the treatment. This is clearly visible in figure 2, and also the regression results confirm this prediction. When reciprocal preference is less relevant, and it is clear that it was not the intention of the
proposer to treat the respondent unfair, punishment is no longer needed and the respondents’ behavior becomes more rational.
However, no proof is found for the third and most important hypothesis for this research. Level of cognitive empathy is not a significant predictor for acceptance of the offer. However, as expected, the coefficient is positive, indicating that participants with a higher score on the Perspective Taking scale could be more likely to accept offers. Although we expected the interaction term to be positive, it is negative and cancels out the effects of Perspective Taking. A possible explanation for these outcomes might be that in our hypothesis we assumed that participants with low empathy do not take perspective of the proposer in any situation and high empathic participants need a trigger for it. However, these results indicate that we have underestimated the average level of empathy. Highly empathetic people are willing to forgive in general situations because they easily take perspective and understand self-serving choices, and low empathy people just need a reason to trigger their empathy in order to agree with lower offers. More research is needed to study if this theory holds true.
A major discussion point of the experiment are the deceptions in the treatment. Proposers believed that they were playing against a robot and their decisions in this
interaction could directly lead to a payoff. However, this was only indirectly the case, as they were later asked if their offer could be used in a game with a human respondent. This problem
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could have been prevented by dividing the proposer’s experiment into two rounds. In the first round the proposer would be playing against a robot and, in case the robot accepted their offer, get payoff for this round. In the second round, they would be asked if their offer can be used against a human to have a chance to earn some extra money.
This could also be the solution for the second deception. Respondents were not aware of the compensation that was offered to the proposer in case the offer was refused. This created an illusion of more power for the respondent than was in actuality. If the proposers had been playing a first round against the robot and were given the option to enter the second round, a compensation would not have been necessary, since the second round would be bonus payment. We should still be cautious with the interpretation of the results though. The respondent should be informed that the first round has a potential payoff for the proposer (based on the robots’ decision) because could affect the respondents’ decision. When they realize that the proposer already had a chance of payment in the first round, the respondents’ willingness to accept low offers might decrease because they feel that the proposer does not deserve a (unfairly) high payoff twice.
Furthermore, the number of control variables is relatively small. This has advantages as it reduces the time required to complete the experiment, making more people might want to participate. It also reduces the risk of overfitting. In our experiment, 43 out of 112 participants rejected their offer, which means that using the one in ten rule of thumb, a maximum of four pre-specified variables should be included in the regression (Babyak, 2004). Some scientists argue that this rule is too conservative and, especially in logit regressions, one should still be cautious when lowering the ratio of events per predictor under ten (Vittinghoff & McCulloch, 2007).
On the other hand, using fewer control variables could lead to biases. For example, field of study/work might have an influence on the dependent variable, since prior knowledge
24
of the Ultimatum Game could alter opinions on fair behavior and logical responses.
Participants who had classes in economics or game theory might have a tendency to give the ‘correct answer’, and accept any positive offer like a rational person would do.
Additionally, culture has an influence on behavior in the Ultimatum Game (Roth, Prasnikar, Okuno-Fujiwara & Zamir, 1991; Henrich, et al., 2001). Since the participants were friends and family of the author, virtually all of them are from the Netherlands or other Western cultures. Still, it could be a relevant variable in the model, in which case it should have been included.
Further, it is possible that the dataset is not a representative sample of the overall population. Even though the participants had a chance to win money, it is likely that most of them decided to participate because of friendly or altruistic reasons. This self-selection could therefore be correlated with empathy, as more empathic people are more likely to want to help others or show prosocial behavior (Eisenberg & Miller, 1987; Cialdini, Brown, Lewis, Luce, & Neuberg, 1997). Repeating the experiment with a certain payoff and more participants would improve the reliability of the dataset and creates an opportunity to include more control variables.
25
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Appendices
Appendix A – Proposer experiment Underlined text was only showed to participants in the treatment.
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Cursive letters are variables based on data or previous choices of the participant.
Thank you for participating in this experiment.
You will stay anonymous for both other players and the experimenter.
The data collected in this experiment will be analyzed statistically. It will never be shared with others or used for anything else than research purpose.
Your earnings will depend on your decisions and the decisions of another player. For every point you earn in the game, you get €2. You can earn a maximum of €20.
At the end of the experiment, one pair of players is selected for payment. You can leave your email address at the end of the experiment, so we can contact you if you were selected for payment.
If you have any questions, suggestions or complaints, please send an email to: ciskavanderzijden@gmail.com.
The experiment will take about 3 minutes.
Please read the instructions carefully before making a decision.
Please answer the following two questions to start the experiment. What is your gender?
Male Female Other What is your age?
20 or younger 21-30 31-40 41-50 51-60 61 or older
During this experiment, you will be playing an Ultimatum Game.
In this game, participants are paired in couples, one playing the proposer, and the other playing the respondent.
The proposer has to decide how to divide a fixed budget between himself and the respondent. The respondent can either accept or reject this offer. If the respondent accepts the offer, the budget will be divided as proposed, if the respondent rejects the offer, both players receive nothing.
You will be playing the proposer. You have a budget of 10, and can offer a division to the respondent, which he can either accept or reject.
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The respondent you are paired with is a robot. The robot's behavior is ambiguous, he has some artificial intelligence.
You have a budget of 10 to divide between you and the respondent. How much of this budget do you want to keep for yourself?
0 1 2 3 4 5 6 7 8 9 10
You decided to keep x of the budget for yourself. Please click 'next' to finish the experiment.
We would like to use your answer for another research. This research studies how people react to an offer from a proposer that was playing against a robot.
The other player can accept or reject your offer, so his decision will affect your earnings. You might have had the expectation that a robot would accept lower offers than a human, and therefore you might have made a lower offer than you would have when playing against a human. To compensate for this, your payoff will not be zero when the human rejects your offer.
In previous research A% of the respondents would reject your offer. Your compensation is calculated based on these rates. So, we assume you have B% chance to win x, and A% chance to be compensated with y. This results in an average payoff 5, which is an equal split of the budget.
When the other participant accepts your offer, your payment will be x.
However, if the other participant rejects your offer, you will be compensated with y and the other person gets zero.
If you do not want us to use your answer against a human respondent, you can reject this option and your payoff will be zero.
Can we use your answer for an Ultimatum Game against a human? Yes No
Thank you for participating in this experiment.
We will use your answer in the following experiment, where you will be matched with a random respondent.
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When the following experiment is finished, a random pair of a proposer and a respondent will be selected and paid based on their choices. If you want to have a chance to be paid, please fill in your email address so we can contact you.
Your email address will never be shared with others and only be used to contact you when you are selected for payment.
Appendix B – Respondent experiment
Underlined text was only showed to participants in the treatment.
Cursive letters or text are variables based on data or previous choices of the participant.
Thank you for participating in this experiment.
You will stay anonymous for both other players and the experimenter.
The data collected in this experiment will be analyzed statistically. It will never be shared with others or used for anything else than research purpose.
Your earnings will depend on your decisions and the decisions of another player. For every point you earn in the game, you get €4. You can earn a maximum €20.
At the end of the experiment, one pair of players is selected for payment. You can leave your email address at the end of the experiment, so we can contact you if you were selected for payment.
If you have any questions, suggestions or complaints, please send an email to: ciskavanderzijden@gmail.com.
The experiment will take about 5 minutes.
Please read the instructions carefully before making a decision.
Please answer the following two questions to start the experiment. What is your gender?
Male Female Other What is your age?
20 or younger 21-30 31-40 41-50 51-60 61 or older
During this experiment, you will be playing an Ultimatum Game.
In this game, participants are paired in couples, one playing the proposer, and the other playing the respondent.
35
The proposer has to decide how to divide a fixed budget between themself and the
respondent. The respondent can either accept or reject this offer. If the respondent accepts the offer, the budget will be divided as proposed, if the respondent rejects the offer, both players receive nothing.
You will be playing the respondent. The proposer has a budget of 10, and will offer a division to you, which you can either accept or reject.
When the proposer made the offer, he/she was playing a game with a robotic respondent. The proposer was told that the robot has some artificial intelligence, and its behavior is ambiguous.
After making the decision about the division of the budget, the proposer was asked if their offer could be used against a human respondent.
The proposer has offered you z, leaving x to themself. Do you want to accept or reject this offer?
Accept Reject
You have decided to Accept/Reject the offer.
Please fill in the following questionnaire to finish the experiment.
IRI questionnaire as in Appendix B.
Thank you for participating in this experiment.
When the experiment is finished, a random pair of a proposer and a respondent will be
selected and paid based on their choices. If you want to have a chance to be paid, please fill in your email address so we can contact you.
Your email address will never be shared with others and only be used to contact you when you are selected for payment.
Appendix C – Interpersonal Reactivity Index
The following statements inquire about your thoughts and feelings in a variety of situations. For each item, indicate how well it describes you by choosing the appropriate letter on the scale at the top of the page: A, B, C, D, or E. When you have decided on your answer, fill in
36
the letter next to the item number. READ EACH ITEM CAREFULLY BEFORE RESPONDING. Answer as honestly as you can. Thank you.
ANSWER SCALE:
A B C D E
DOES NOT DESCRIBES ME
DESCRIBE ME WELL VERY WELL
1. I daydream and fantasize, with some regularity, about things that might happen to me. (FS) 2. I often have tender, concerned feelings for people less fortunate than me. (EC)
3. I sometimes find it difficult to see things from the "other guy's" point of view. (PT) (-) 4. Sometimes I don't feel very sorry for other people when they are having problems. (EC) (-) 5. I really get involved with the feelings of the characters in a novel. (FS)
6. In emergency situations, I feel apprehensive and ill-at-ease. (PD)
7. I am usually objective when I watch a movie or play, and I don't often get completely caught up in it. (FS) (-)
8. I try to look at everybody's side of a disagreement before I make a decision. (PT)
9. When I see someone being taken advantage of, I feel kind of protective towards them. (EC) 10. I sometimes feel helpless when I am in the middle of a very emotional situation. (PD) 11. I sometimes try to understand my friends better by imagining how things look from their perspective. (PT)
12. Becoming extremely involved in a good book or movie is somewhat rare for me. (FS) (-) 13. When I see someone get hurt, I tend to remain calm. (PD) (-)
14. Other people's misfortunes do not usually disturb me a great deal. (EC) (-)
15. If I'm sure I'm right about something, I don't waste much time listening to other people's arguments. (PT) (-)
16. After seeing a play or movie, I have felt as though I were one of the characters. (FS) 17. Being in a tense emotional situation scares me. (PD)
18. When I see someone being treated unfairly, I sometimes don't feel very much pity for them. (EC) (-)
19. I am usually pretty effective in dealing with emergencies. (PD) (-) 20. I am often quite touched by things that I see happen. (EC)
21. I believe that there are two sides to every question and try to look at them both. (PT) 22. I would describe myself as a pretty soft-hearted person. (EC)
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23. When I watch a good movie, I can very easily put myself in the place of a leading character. (FS)
24. I tend to lose control during emergencies. (PD)
25. When I'm upset at someone, I usually try to "put myself in his shoes" for a while. (PT) 26. When I am reading an interesting story or novel, I imagine how I would feel if the events in the story were happening to me. (FS)
27. When I see someone who badly needs help in an emergency, I go to pieces. (PD) 28. Before criticizing somebody, I try to imagine how I would feel if I were in their place. (PT)
NOTE: (-) denotes item to be scored in reverse fashion PT = perspective-taking scale
FS = fantasy scale
EC = empathic concern scale PD = personal distress scale
A = 0 B = 1 C = 2 D = 3 E = 4
