Social risk is a domain of risk in which the costs, benefits, and uncertainty of an action depend on the behavior of another individual. Humans overvalue the costs of a socially risky decision when compared with that of purely economic risk. Here, we played a trust game with 8 female captive chimpanzees ( Pan troglodytes ) to determine whether this bias exists in one of our closest living relatives. A correlation between an individual’s social- and nonsocial-risk attitudes indicated stable individual variation, yet the chimpanzees were more averse to social than nonsocial risk. This indicates differences between social and economic decision making and emotional factors in social risk taking. In another experiment using the same paradigm, subjects played with several partners with whom they had varying relationships. Preexisting relationships did not impact the subjects’ choices. Instead, the apes used a tit-for-tat strategy and were influenced by the outcome of early interactions with a partner.

In both humans and other species, many decisions involve risk, which is defined as uncertainty about whether an investment will result in a cost or benefit (Yates & Stone, 1992). Often, risky decisions in the animal kingdom include those involving resources, encompassing decisions about how far to travel for food or water (Smallwood, 1996). This is a form of economic risk, as animals must minimize the cost of foraging and optimize resource acquisition when a positive outcome is not guaranteed.

Social interactions can also result in costs and benefits, but here, the other individual often determines the outcome. Consequently, many social interactions are characterized by uncertainty (Bohnet, Greig, Herrmann, & Zeckhauser, 2008). Costs result from aggression (Mitani & Amsler, 2003), energy loss (de Waal & Davis, 2003), or opportunity loss as a result of partner choice (Kummer, 1978) and can decrease the value of a relationship (Cords & Aureli, 2000). Benefits range from increased access to resources to the emotional rewards of social interactions (de Waal, 1997; Kummer, 1978; Wittig et al., 2008). Thus, just as economic decisions involve risk, so too do social decisions.

Despite similarities between these risk domains, humans process social and nonsocial risk differently. Studies using functional MRI indicate that positive social interactions activate pleasurable responses in more brain areas than exchanges producing purely economic rewards (Rilling, King-Casas, & Sanfey, 2008; Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004). Conversely, in behavioral experiments, humans would rather lose money because of random chance than the actions of another person (Blount, 1995; Bohnet & Zeckhauser, 2004). Bohnet et al. (2008) replicated these results across five cultures, suggesting that this response is widespread among humans. Taken together, this suggests that social risks, both wins and losses, have a greater emotional impact than economic risks.

Unique responses to social risk may also characterize nonhuman primates’ social interactions. Several factors increase the uncertainty as to whether an encounter will result in a cost or benefit, such as the number of individuals with which nonhuman primates interact, the wide variation of social behaviors in the nonhuman primate repertoire, and the fact that nonhuman primates behave differently toward group-mates depending on relationship characteristics such as kinship and dominance status (Kummer, Daston, Gigerenzer, & Silk, 1997; Silk, 2007). Complex social behaviors such as responding appropriately to the requests of another individual (Call, Hare, Carpenter, & Tomasello, 2004; Horner, Carter, Suchak, & de Waal, 2011), inequity aversion (Brosnan & De Waal, 2003), and gaining resources through social investments (de Waal, 1997) indicate that nonhuman primates are capable of recognizing and responding to the costs and benefits of social interactions. Although primates, including humans, have specialized neural mechanisms for processing social information (Brothers, 2002), it is unknown whether social factors influence economic choices in the same way they do for humans. Given the similarities between humans and nonhuman primates, we expect nonhuman primates to also be more averse to social than nonsocial risk. Because chimpanzees diverged from the human lineage around 6 million years ago and share most of our genetic code (Steiper & Young, 2006), they make particularly good models for comparison.

Dyadic relationship quality may influence a subject’s attitude toward social risk. Chimpanzees, for example, invested more heavily in affiliative than in nonaffiliative partners in a trust game (Engelmann & Herrmann, 2016), and well-established groups with stable relationships were less averse to experimentally induced inequity (Brosnan, Schiff, & de Waal, 2005). In contrast, whereas the duration of their relationship outside of the experimental context predicted which chimpanzee dyads would work together as partners, it did not influence testing outcomes (Brosnan et al., 2015) nor did it affect prosocial choices in an experiment (Horner et al., 2011). This indicates that the daily level of affiliation within a dyad is not always predictive of behavior in experimental settings.

One way to evaluate attitude toward social risk is through the use of an economic trust game (Berg, Dickhaut, & McCabe, 1995). In humans, the social condition of a trust game begins when money is given to a participant who can either make the safe decision to keep it or make the risky decision to give it to a partner. If the partner acquires the money, the quantity is tripled, and the partner then decides how to divide the money with the subject. Thus, a subject forgoes a guaranteed small reward for a chance to gain a higher reward from a partner who may or may not cooperate.

Here, Experiment 1 was a similar economic trust game with nonhuman primates, but one that employed both a social and a nonsocial condition, allowing for a comparison between two kinds of risk taking. This has not been possible in similar experiments that lacked nonsocial controls (e.g., Engelmann, Herrmann, & Tomasello, 2015). Experiment 2 investigated whether the relationship with a partner outside of the experimental context predicted investment during the experiment. Given the parallels between our study and the Engelmann studies in this regard (Engelmann & Herrmann, 2016; Engelmann et al., 2015), we predicted that our subjects would be more willing to invest in affiliative partners. Because we used quantitative rewards, we were also able to isolate the effect of reward quantity from the effect of dyadic relationship on the subject’s risk preferences and examine socially risky decision making in a unique way.

Experiment 2: Dyadic-Variation Trust Game Method In Experiment 2, we repeatedly ran the social condition of the trust game with multiple subject–partner combinations. Each dyadic combination completed four 30-trial sessions of the social condition, and each subject played the game with a maximum of three new partners. Some subjects had fewer than three additional partners because of colony-management reasons unrelated to research. Table 1 lists subject–partner pairs as well as their measure of affiliation. For this experiment, we wanted to explore the effect of both positive and negative relationships on social risk and, hence, selected partners who were affiliative as well as pairs that were neutral and avoidant. Table 1. Relationship Index for the Subject–Partner Pairs (All Female) in Experiment 2 View larger version To obtain affiliation measures, we calculated a relationship index from an adjusted standardized Pearson’s residual on the basis of routine group observations taken two to five times per week from 2012 to 2015. To calculate this measure, we obtained the expected frequency of affiliation for each dyad on the basis of the number of individuals in the group and then calculated the actual frequency of affiliation on the basis of our behavioral observations. The adjusted standardized Pearson’s residuals produced a z score based on what we would expect their relationship to be as a result of chance. This served as the relationship index. During each 90-min observation period, we recorded all social behaviors (for additional details, see the Supplemental Material). We counted all behaviors such as directional and mutual grooming, embracing, kissing, and aggression as point behaviors and also took routine scans in which we recorded which individuals were sitting within 1 m of each other (cf. Seres, Aureli, & de Waal, 2001). We considered dyads that engaged in positive interactions significantly more than predicted by chance as affiliative, those that engaged in positive interactions at chance levels to have a neutral relationship, and those that engaged in positive interactions less often than predicted to be avoidant (Everitt, 1977). Results To explore the influence of relationship on a subject’s propensity to invest in a partner, we conducted Experiment 2 with 18 novel dyads. Combining these with the dyads in Experiment 1 produced a total of 26 combinations for our analysis. We ran a multiple linear regression to ascertain whether dyadic relationship quality or the partner’s percentage of prosocial choices more strongly predicted social risk. To ensure that each subject made choices on the basis of an adequate amount of information, we used in this analysis only subjects who chose social risk (i.e., handed the token to their partners) more than 10% of the time, which eliminated 7 dyads. Because we used the same subjects multiple times, the data points were not independent. We accounted for this in the statistical evaluation through bootstrapping (Ho, 2006). To calculate whether each subject’s choices depended on partner choice in the previous trial, we obtained the frequency of partner–subject choice outcomes and created a 2 × 2 contingency table analyzed using a chi-square test with Yates correction. The subjects chose social risk a mean of 49.00% (SEM = 8.05%) of the time, whereas their partners made prosocial over selfish choices a mean of 59.32% (SEM = 8.21%) of the occasions on which they were given a chance to make this choice. We initially calculated a multiple linear regression to ascertain whether the partner’s prosocial or selfish decisions predicted the subject’s social-risk behavior. This model was not significant (Table 2 outlines the results of the regression). We then added the relationship index into the regression to determine whether it added predictive power to the model. However, this second model also failed to predict the subject’s choices. These results imply that the subject’s choices were not influenced by either the overall number of rewards she received via her partner or her preexisting social relationship with her partner. Table 2. Results of the Multiple Regressions Predicting Subjects’ Social-Risk Behavior (Experiment 2) View larger version To fully elucidate whether subjects made risky choices on the basis of past rewards or relationship quality, we analyzed the choices of each dyad in a 60-trial testing block using multiple regression. Our findings indicate that the percentage of prosocial choices significantly predicted social risk by the subject in Blocks 1 and 4. The regression equation was not significant in Blocks 2 and 3 (see Table 3 for statistics). The preexisting relationship in the group never predicted risky choice. Table 3. Results of the Multiple Linear Regression Predicting Subjects’ Social-Risk Behavior in Each Block View larger version To determine whether the subjects’ choices were contingent on the decisions of their partners, we investigated whether each subject continued to invest in a partner who had recently chosen the selfish option. The mean percentage of social-risk choices made by the subject following a selfish choice by the partner was 16.87% (SEM = 6.03%), whereas the mean percentage of social-risk choices made by the subject following a prosocial choice by the partner was 30.63% (SEM = 10.37%). A 2 × 2 contingency table using the frequencies of partner-choice/subject-choice combinations for all choice combinations (selfish–risk, selfish–safe, prosocial–risk, and prosocial–safe) across all sessions was significant, χ2(2, N = 26) = 11.18, p = .001, 95% CI for the effect size = [11.69, 38.08], and showed a large effect (φ = .63, 95% CI = [.30, .82]). This indicates that the subjects responded to the choices of their partners in a tit-for-tat manner. We also examined whether the subjects’ nonsocial choices were contingent on whether they won or lost the previous trial. The mean percentage of nonsocial-risk choices made by the subject following a loss was 39.88% (SEM = 2.85%), whereas the mean percentage of nonsocial-risk choices made by the subject following a win was 51.15% (SEM = 4.91%). A 2 × 2 contingency table using the frequencies of outcome–subject choice combinations for all choice combinations (lose–risk, lose–safe, win–risk, and win–safe) across all sessions was not significant, χ2(2, N = 8) = 0.019, p = .991, 95% CI for the effect size = [1.24, 14.45], with a small effect size (φ = .049, 95% CI = [–.68, .73]). This nonsignificant outcome suggests that although subjects responded to the choices of their partners in a tit-for-tat manner, they did not respond in the same way to the nonsocial apparatus.

Acknowledgements The Yerkes National Primate Research Center is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care.

Action Editor

Ralph Adolphs served as action editor for this article. Author Contributions

S. E. Calcutt conceptualized the study, and S. E. Calcutt, D. Proctor, and F. B. M. de Waal designed the study. S. E. Calcutt, D. Proctor, and S. M. Berman collected the data. S. E. Calcutt and D. Proctor analyzed the data. S. E. Calcutt drafted the manuscript, and D. Proctor and F. B. M. de Waal provided revisions. All the authors approved the final manuscript for submission. Declaration of Conflicting Interests

The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article. Funding

This research was supported by a base grant to the Yerkes National Primate Research Center by the National Center for Research Resources (Grant No. P51RR165; currently supported by the Office of Research Infrastructure Programs/OD P51OD11132) and by the Living Links Center. Supplemental Material

Additional supporting information can be found at http://journals.sagepub.com/doi/suppl/10.1177/0956797618811877 Open Practices

Data and materials for this study have not been made publicly available. The design and analysis plans were not preregistered.