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"Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas shows students, researchers, and professionals how to use computation methods, rather than mathematical analysis, to answer research questions for an easier, more productive method of testing their models. Illustrations of general methodology are provided and explore how computer simulation is used to bridge the gap between formal theoretical models and empirical applications. An accompanying website supports the text"--

Abstract
A cohesive network keeps groups together and enables members to communicate about and cooperate for public goods. For ongoing cooperation, group members have to know if their group members cooperate or defect, but this information—mostly through gossip—is threatened by noise and biases. If there are redundant information channels, however, errors in monitoring and transmission in one imperfect channel can, to some degree, be corrected by information through another imperfect channel, and may lead to higher levels of cooperation. An influential conceptualization of social cohesion based on redundancy is K-connectivity: the minimum number (K) of node-independent paths connecting pairs of nodes in a group's network. In a lab experiment, we tested if higher K-connectivity yields higher levels of cooperation for public goods, controlling for a number of other network effects such as density, size, and average distance. We do not find the hypothesized effect, which might be due to a not-earlier-found shortcoming of the concept, and we propose a solution.

The growth of the sharing economy has attracted a lot of attention in recent years. These online platforms bring together individuals willing to share and those who are in need. While sharing in some of these platforms is reciprocated by money or reputation, some platforms rely heavily on individuals' good will to give their things away with no personal benefit. Why do individuals cooperate in environments where there is every incentive to free-ride instead? In this article, we examine several explanations for such peculiar behavior – namely, the hypothesis that prosocial or sharing behavior spreads from person to person, and social learning hypothesis, which tells that such behavior could be based on mimicking others. We test these hypotheses using longitudinal data from a freecycling group on Facebook with 4818 members. The group is made for people to give things away for free, thus making the group vulnerable to free-riding behavior. We find that individuals who are more active receivers are also more likely to share something with group members in the future. We also find some evidence of a positive effect of social learning on sharing behavior.

The book identifies conditions for trust and cooperation. It highlights unintended consequences of individually rational behavior, and shows how trust and cooperation change dependent on social embeddedness. Such analyses inspire experimental tests in lab conditions, but also tests through empirical applications in field studies. The results of this mixed-method approach can in turn be used to inspire further theoretical work.

Large-scale online marketplace data have been repeatedly used to test sociological theories on trust between strangers. Most studies focus on sellers' aggregate reputation scores, rather than on buyers' individual decisions to trust. Theoretical predictions on how repeated exchanges affect trust within dyads and how buyers weigh individual experience against reputation feedback from other actors have not been tested directly in detail. What do buyers do when they are warned not to trust someone they have trusted many times before? We analyze reputation effects on trust at the dyadic and network levels using data from an illegal online drug marketplace. We find that buyers' trust decisions are primarily explained by dyadic embeddedness - cooperative sellers get awarded by repeated exchanges. Although buyers take third-party information into account, this effect is weaker and more important for first-time buyers. Buyers tend to choose market exit instead of retaliation against sellers after negative experiences.

Societies are sometimes divided into groups that behave in different ways or have strongly opposing opinions. At other times, everyone seems to behave according to similar principles and opinions. It is likely that individual decisions on behavior or opinions are affected by social networks through influence and selection processes. However, the outcomes are not necessarily optimal for the society as a whole. Two types of problems might arise: (a) polarization of the society into two camps that do not reach consensus, possibly leading to conflict; (b) actors choosing suboptimal behavior, because changing behavior is too risky if done unilaterally. Simulations show that if a society is rather segregated initially, there exists a heightened probability that this situation will worsen. The effect of network density is twofold. First, density has a positive effect on reaching a uniform opinion and, therefore, decreases the likelihood of polarization. Second, density increases the likelihood that actors do not change their behavior, worsening the inefficiency of already suboptimal initial situations.

We study what determines whether someone is an early Facebook adopter in a context in which Facebook is still relatively new compared to a far more popular Dutch social networking site (SNS) (Hyves). We use representative survey data of 4363 adolescents aged 14–15 years. First, adolescents who participate in more leisure activities, who have more digital resources, and who have more friends who are SNS members are more likely to be SNS members. Second, we hypothesize and show that for adopting communication technology that highly fluctuates in popularity and is highly time-dependent, individuals are more likely to be early Facebook adopters when the number of their friends who are Facebook members increases. Finally, non-native adolescents are also more likely to be early Facebook adopters.

AbstractSharing economy platforms commonly claim to bring about positive social impacts, such as facilitating contact between individuals that would not have met otherwise. According to contact theory, such intergroup contact would change the stereotypes that individuals hold of outgroup members, such as people with a different nationality or ethnicity. We use a large‐scale online Investment Game experiment among Airbnb users to study the effect of Airbnb interactions on cross‐national trust. In contrast with common claims about the positive impact of the sharing economy, we did not find that individuals who had prior experience with a nationality as a host or a guest on Airbnb trusted persons of that nationality more. This may be because monetization, institutionalization and professionalization of Airbnb limits the intensity of contact, or because Airbnb mostly establishes contact between individuals with similar backgrounds.

AbstractReputation systems are commonplace in online markets, such as on peer-to-peer sharing platforms. These systems have been argued to be a solution to (ethnic) discrimination on such platforms. This argument is based on empirical studies showing that ethnic disadvantages are smaller for users with ratings than for users without ratings. We argue that this conclusion may be premature, because minorities have a harder time accumulating ratings. The greater benefit of ratings to minorities may be offset by their troubles acquiring any, thereby diminishing the potential for reputation systems to reduce discrimination. We tested this counterargument using a unique data set that contains information on all interactions on a peer-to-peer motorcycle rental platform. We find that the reputation system does not reduce initial inequalities between otherwise comparable renters of different ethnicity. Platforms that wish to reduce discrimination should not only make their reputation system more effective, but also help users collect ratings.