As ethnic diversity increases in Western countries, concerns about negative outgroup attitudes and interethnic polarization are growing. In diverse classrooms, processes such as peer influence and homophilic friend selection contribute to changing outgroup attitudes. This study explores if and when individual attitude shifts driven by these processes might scale up to intergroup hostility in empirical multiethnic school cohorts that initially have a rather positive interethnic climate. Using an agent-based model calibrated with longitudinal network and attitude data, we conduct a series of computational experiments to assess the effects of hypothetical macro-level shocks, such as violent incidents where a minority group is associated with the perpetrators. Our findings indicate that correlational peer influence on attitudes towards Turks, when pushed to extreme parameter values, can over time magnify even small outgroup biases, leading to interethnic hostility. This dynamic is accelerated under strong ethnic homophily. We further provide a detailed analysis of how these results emerge from the empirical context and the interacting processes modeled.

Using data from an experiment carried out by a large nonprofit organization, this paper finds that lapsed donors who received a solicitation letter referencing a relatively high donation made by another donor (high social information) were more generous in giving, but overall less likely to make a donation, relative to the baseline (low social information) group. After using the propensity score matching to correct for pretreatment differences in the two experimental groups, the estimated effect of high social information on the average donation amount is an increase of $14.95 (45 %). However, high social information is also found to reduce the probability a lapsed donor will give by 4.1 %. Thus, high social information can have potentially offsetting effects when applied to lapsed donors. Nonprofits should consider this trade-off when employing social information fundraising techniques to solicit donations from lapsed donors.

Giving circles and alumni campaigns are popular forms of nonprofit fund-raising practice that may lead to positive peer effects that promote charitable giving among similar individuals. However, do individuals always give more in the presence of similar others? Most research finds a positive social influence among similar individuals in donor–solicitor and donor–recipient dyads. However, in donor–donor dyads, the effects of social influence on charitable giving are mixed. Individuals’ giving can be positively or negatively affected by other donors’ giving. This systematic literature review (35 eligible studies) investigates how individuals’ charitable giving is affected by the giving of others. Its major contribution is to extend the previous literature by proposing a new mechanism of decision making in charitable giving through an important psychological construct (similarity). It also identifies five critical factors that could help to explain inconsistent findings of similarity effects on two measures of charitable giving (giving propensity and giving amount). It also contributes to identifying a literature gap, suggesting future research directions, and providing important implications to fund-raising practice.

Amnon Rapoport made seminal contributions to research on investment decision-making and individual decision-making under risk. To build on his seminal work, this paper explores the impact of social influence on risk-taking. First, to build predictions for experimental testing, we modify a standard expected utility model by introducing a social norm variable. Using a standard 10-decision paired lottery choice task, we report the results from three experiments with different manipulations to test whether social influence information affects subjects’ own lottery choices. In Experiment 1, we find that participants are more likely to switch to choosing the risky option earlier if they are told that a large majority (>75%) of a large group (N = 100) of others have also chosen the risky option in the past. In Experiment 2, we find there is no effect if the social influence prompt is framed as a small group (N = 10) or the choice of one (N = 1) successful lottery participant, but there is an effect when participants are provided information about the consistently risky choices of one (N = 1) person in the past. In Experiment 3, using an in-person subject pool, we find some mixed effects on risk-taking when the social information is framed as a small group (N = 10) of peers (other students). Altogether, this paper demonstrates that social influence can impact risk-taking in line with a socially normed expected utility model.

In this paper we study the effect of downward social information in contribution decisions to fund public goods. We describe the results of a field experiment run in conjunction with the fundraising campaigns of a public radio station. Renewing members are presented with social information (information about another donor's contribution) which is either above or below their previous (last year's) contribution. We find that respondents change their contribution in the direction of the social information; increasing their contribution when the social information is above their previous contribution, and decreasing their contribution when the social information is below. We hypothesize about the psychological motivations that may cause the results and test these hypotheses by comparing the relative size of the upward and downward shifts. These results improve our understanding of cooperation in public good provision and suggest differential costs and benefits to fundraisers in providing social information.

We experimentally study intention-based social influence in standard and modified Ultimatum and Impunity games. Standard games with bi-dimensional strategy vectors let individuals decide independently in the role of proposer and responder and allow fairness intentions to be role dependent. Uni-dimensional strategy vectors in modified games constrain individuals to consistent offers and acceptance thresholds. To induce social influence, we randomly match participants in groups of four, which are minimally identified by colors. Social influence is assessed by how one reacts to information about median group intention(s). The $2\times 2$ factorial experimental design varies the order of the two game types and the strategy vector dimensionality. Social influence, depending on the game type and strategy dimensionality, significantly impacts participants’ behavior compared to their own intention. At the aggregate level, however, these differences cancel each other out. As there are more constraints on the action space, uni-dimensionality increases strategic concerns.

This paper generalizes the p* class of models for social network data to predict individual-level attributes from network ties. The p* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so construct p* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.

Social network data represent interactions and relationships among groups of individuals. One aspect of social interaction is social influence, the idea that beliefs or behaviors change as a result of one’s social network. The purpose of this article is to introduce a new model for social influence, the latent space model for influence, which employs latent space positions so that individuals are affected most by those who are “closest” to them in the latent space. We describe this model along with some of the contexts in which it can be used and explore the operating characteristics using a series of simulation studies. We conclude with an example of teacher advice-seeking networks to show that changes in beliefs about teaching mathematics may be attributed to network influence.

Numerous public initiatives aim to influence individual food choices by informing about what is considered ‘healthy’, ‘climate-friendly’, and generally ‘sustainable’ food. However, research suggests that rather than public authorities, social influence is more likely to affect people’s behaviour. Using a randomised controlled trial, this study investigated if and how the two kinds of influences (factual versus social) could affect the real-life, self-reported intake of plant- and animal-based foods. In a four-month randomised controlled trial, a self-selected sample of adults living in Sweden (N = 237) tracked their daily food consumption several times per week using a tailored mobile phone app. Participants were randomised into one of three groups: two treatment groups receiving factual or social information about plant- and animal-based food consumption, or a control group receiving no information. Pre- and post-questionnaires provided additional background information about the participants. Participants’ food habits varied from week to week, and an explorative analysis pointed to a slight decrease in the consumption of animal-based food in the group that received social information. However, the longer-term patterns remained relatively constant in all groups, showing no substantial shift regardless of the kind of cues that the participants received. By investigating the roles of two common types of information about food and dietary change, the results contribute to discussions about how and by whom effective and efficient measures can be implemented to transform food habits. The results suggest there is limited potential for sustained and substantial behavioural changes through both social and factual information campaigns.

We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter’s belief into the ego’s but by adjusting the ego’s decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus.