People become friends with one another primarily due to things they have in common, like shared demographic characteristics or shared interests. But on what similarities are people becoming friends at different stages of knowing one another? To study this, we use a longitudinal dataset that followed a cohort of students of one study programme at a Swiss technical university. We model how demographic traits of nationality and gender, and less observable interest-related traits of being social, a partygoer, a smart and hard-working student, contribute to friendships. Using Stochastic Actor- Oriented Models, we find a baseline level of both demographic and interest-related friendship homophily, indicating that there are differing reasons behind friendships. We also find that homophily based on demographic traits diminishes over time when students get to know those in their study cohort better. This suggest that homophily on observable traits is mainly relevant when people first meet but becomes less important over time.

Abstract 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.

Intergroup attitudes can be positively influenced by peers, but it remains unclear whether this occurs primarily through ingroup socialization or outgroup contact. Prior studies concurrently exploring both pathways have yielded mixed results. This paper introduces two key factors, ingroup identification and interpersonal dislike, as potentially moderating or counteracting the effects of these processes. We incorporate these factors into a comprehensive statistical model that accounts for various mechanisms associated with outgroup attitude change, including peer influence, ingroup and outgroup contact, ethnic and attitudinal homophily in friendship selection, and general relationship formation dynamics. Using stochastic actor-oriented modeling (SAOM), we analyze longitudinal data on coevolving networks and attitudes among 380 German secondary school adolescents. Our findings show that both outgroup contact among German adolescents and ingroup socialization significantly influence outgroup attitude change. However, interpersonal dislike and ingroup identification do not play a meaningful role in these processes.

Intuitively, personality traits may shape friendships differently from academic connections, potentially influencing social support development. Yet, this remains understudied, while it is crucial for student's wellbeing. Therefore, this study investigates how the personality traits of the Five-Factor Model interact with the role friendships and preferred collaboration relationships. In a sample of 95 university students, socio-centric data were collected with questionnaires on personality traits and longitudinally on networks of friendship and preferences for collaboration networks. Utilizing stochastic actor-oriented models in RSiena, we showed that students more open to new experiences established more friendships, preferred more collaborations with their peers, and were also more popular in both networks than students less open-to-new-experiences. Students scoring higher on agreeableness were less likely to connect to their peers in both networks, whereas higher-achieving students were more likely to establish peer relationships in both networks. Furthermore, friends were preferred for collaboration (and vice versa), indicating an overlap. This study points to the importance of personality traits and achievement when students integrate into their social and academic environment.

In agent-based models (ABMs), the estimation of model parameters from data is much less straightforward than in traditional but more limited techniques of modelling data, such as regression. For most ABMs, the likelihood of a parameter vector given the data cannot be written down explicitly nor sampled from, ruling out commonly used techniques such as maximum likelihood estimation and Markov chain Monte Carlo sampling. This study proposes a methodology to estimate ABMs on household-level data, for an ABM tailored to primary school choice and segregation in the Netherlands. It explores the interplay between the micro-, meso, and macro-levels in school choice dynamics, highlighting the limitations of conventional methodologies in capturing such interactions. By estimating an ABM directly on household-level data using neural ratio estimation, the study enhances the realism of the ABM, shedding light on choice processes and mechanisms driving segregation. It unveils that heuristic-based models better capture household behaviours than traditional models of rational action, challenging existing assumptions. This study not only advances understanding of school choice dynamics, but also provides a estimation framework applicable to ABMs of other social systems, paving the way for more realistic and validated ABMs.

Peers are assumed to be crucial in shaping adolescents’ attitudes and behaviors in schools; what peers yield social influence over adolescents is less clear. We disentangled two mechanisms of peer influence: friendship influence and normative pressure. Friendship influence refers to the impact of friends’ attitudes and behaviors, while normative pressure stems from the influence of peers from the same social group, such as gender group. We adopted a network perspective, recognizing that peer influence can be shaped by changing relationships among peers. We investigated friendship influence and normative pressure based on sharing the same gender and ethnicity in shaping academic drive, prejudice, and religiosity. We analyzed a sample of German school grades (N = 2,838; 29 school grades) using stochastic actor-oriented modeling. Adolescents influenced each other via both friendship influence and normative pressure. These findings improve our understanding of who influences whom in schools and open new research avenues.

This paper employs a simulation model to investigate the effectiveness of cooperation selection (“selecting similar others”) and social influence (“do as others do”), since both mechanisms promote cooperation in theoretical analyses and experimental studies. However, it is unclear how effective cooperation selection and social influence are in simulation models where both mechanisms operate simultaneously alongside additional social dynamics, such as reciprocity and transitivity. This paper relies on a model loosely based on an empirical case in which students selected others based on how cooperative they perceive the other. Using existing theoretical cooperation models as a benchmark, we insert relational, behavioral, and contextual assumptions into our model and build on data from 95 students when we vary the strength of cooperation selection and social influence relative to empirically observed levels. We take co-evolution stochastic actor-oriented models as basis because the model inherently accounts for the interdependence of behavior and network selection. Our simulations reveal that cooperation benefits most when cooperation selection and social influence are strongly positive. Through the combination of cooperation selection and social influence, the simulations show that cooperators form dense local clusters, influencing their peers to keep cooperating while insulating themselves from social influence from defectors. Robustness checks confirm the stability of these findings across diverse parameter configurations.

Bounded confidence and negative influence are two of the most important micro-level mechanisms employed in computational models of opinion dynamics to explain polarization of opinions. However, empirical evidence of both mechanisms is debatable. Two common limitations in existing empirical studies are (1) the limited external validity of laboratory experiments, and (2) the inability in study designs to disentangle negative influence from bounded confidence, as well as from other social influence mechanisms like assimilation. We address both limitations, using the Stochastic Actor-Oriented Model (SAOM) with a longitudinal field data set that tracks adolescents' social network relations and opinions on a set of issues. Two new SAOM effects are introduced to represent bounded confidence and negative influence, respectively. Results show that for adolescents' preferences on rap/ hip hop clothing style, the model containing both effects, in addition to assimilative influence from friends, provides a good fit to the data, but only the effects representing negative influence and assimilative influence from friends are statistically significant. The results support that negative influence contributes to explaining observed opinion changes, but lend little weight to bounded confidence. Further simulation studies based on the empirically estimated model show that our model implies only low levels of opinion polarization at the macro-level despite negative influence at the micro-level. We conclude that our approach not only overcomes common limitations of earlier empirical work, but also bridges SAOM and agent-based modeling by offering empirically validated insights into opinion dynamics.

Formal models of opinion formation commonly represent an individual's opinion by a value on a fixed opinion interval. We propose an alternative modeling method wherein interpretation is only provided to the relative positions of opinions vis-à-vis each other. This method is then considered in a similar setting as the discrete-time Altafini model (an extension of the well-known DeGroot model), but with more general influence weights. Even in a linear framework, the model can describe, in the long run, polarization, dynamics with a periodic pattern, and (modulus) consensus formation. In addition, in our alternative approach key characteristics of the opinion dynamic can be derived from real-valued square matrices of influence weights, which immediately allows one to transfer matrix theory insights to the field of opinion formation dynamics under more relaxed conditions than in the DeGroot or discrete-time Altafini models. A few specific themes are covered: (i) We demonstrate how stable patterns in relative opinion dynamics are identified which are hidden when opinions are considered in an absolute opinion framework. (ii) For the two-agent case, we provide an exhaustive closed-form description of the relative opinion model's dynamic in the long run. (iii) We explore group dynamics analytically, in particular providing a non-trivial condition under which a subgroup's asymptotic behavior carries over to the entire population.

People are influenced by members of high-status groups and members of their ingroup. These principles of “status orientation” and “ingroup orientation” can imply opposing forces for people of lower status. Are lower-status individuals more influenced by members of higher-status outgroups or by members of their lower-status ingroup? Engaging status characteristics theory and self-categorization theory, we predict that status orientation is relatively stronger on questions about facts, which have an objectively correct answer, whereas ingroup orientation is stronger when it comes to ‘opinion questions’ that have no objectively correct answer. Results of an online survey experiment confirm that on factual questions, less-educated individuals are more strongly influenced by highly-educated outgroup individuals than by less-educated ingroup individuals. On opinion questions, we observe relatively weaker status orientation, with status orientation and ingroup orientation being about equally strong. These findings suggest that it is harder to reach societal consensus on opinion questions than on factual questions.

Culture plays a pivotal role in shaping collective level processes, with implications for public opinion on issues such as COVID-19 vaccination. Drawing on Hofstede's cultural dimension theory, we theoretically examine the influence of two key dimensions of culture, individualism/collectivism and power distance, on the opinion formation of individuals within a dynamic and evolving context. While former models suggest that collectivism promotes opinion consensus, our findings reveal a more complex relationship, particularly in scenarios where centralization or decentralization of a society is considered. By conducting multiple simulation experiments through an agent-based model, we find that collectivism can contribute to opinion consensus in a simple scenario in which only social norms work without any impact from authorities. However, a collectivist society also has the potential to experience high opinion polarization in the presence of greater decentralization among authorities, particularly in settings of high power distance. Simulation results further demonstrate that disagreement between authorities is more likely to result in opinion polarization in individualist cultures compared to collectivist cultures.

Theoretical agent-based models of residential and school choice have shown that substantial segregation can emerge as an (unintended) consequence of interactions between individual households and feedback mechanisms, despite households being relatively tolerant. However, for school choice, existing models have mostly been highly stylized, leaving open whether they are relevant for understanding school segregation in concrete empirical settings. To bridge this gap, this study develops an empirically calibrated agent-based model focusing on primary school choice in Amsterdam. Consistent with existing models, results show that substantial school segregation emerges when schools are chosen based on a trade-off between composition and distance, and also when households are relatively tolerant. Additionally, findings of (hypothetical) policy simulations suggest that it is important to understand which preferences for school composition and distance households have and how these interact. We find that the effects of policies aiming to reduce school segregation through geographical restricting mechanisms are highly dependent on those interacting preferences. Also, we assessed the contribution of residential segregation to school segregation. Our findings may have implications for methodologies aiming to estimate school choice preferences, such as discrete choice models, as these methodologies do not explicitly control for implications of these interactions and feedback mechanisms, which might lead to incorrect inference.

We formally introduce and empirically test alternative micro-foundations of social influence in the context of communication on social media. To this end, we first propose a general theoretical framework allowing us to represent by different combinations of model parameters of influence-response functions whether and to what extent exposure to online content leads to assimilative or repulsive (distancing) influence on a user's opinion. We show by means of an agent-based model of opinion dynamics on social media that these influence-response functions indeed generate competing predictions about whether personalization (defined as the extent to which users are shielded from foreign opinions) increases or decreases polarization. Next, we conducted an online experiment with respondents recruited on Facebook to estimate model parameters empirically, using Bayesian models for censored data. In the experiment, participants ' opinions were measured before and after exposure to arguments reflecting different ideological positions and moral foundations in a within-subject design. Our findings support that exposure to foreign opinions leads to assimilation towards those opinions, moderated by the extent of (perceived) ideological similarity to the source of influence. We also find weak evidence in support of repulsion (distancing), but only for very large disagreement. Feeding estimated parameter values back into the agent-based model suggests that reducing personalization would not increase, but instead reduce the level of polarization generated by the opinion dynamics of the model. We conclude that the naive interpolation of micro-processes to macro-predictions can be misleading if models are not sufficiently empirically calibrated.

Identity diversity in teams brings advantages for complex decision-making because it is associated with cognitive diversity among team members. At the same time, homophilous interactions along shared identity dimensions can hinder information exchange among dissimilar individuals and threaten successful exploitation of the team's cognitive diversity. We present an agent-based model to investigate how homophily impacts decision-making quality in diverse teams. Team members communicate information in a 'hidden profile' setting where some pieces of information are known only to single individuals while other pieces of information are known to subgroups with the same identity. While intuition may suggest that homophily impairs collective decision-making, our model reveals how homophilous environments lead to better collective decisions: homophily fosters temporary disagreements between dissimilar team members, which grant teams additional time to uncover crucial information that would not have been shared otherwise. Longer discussion time comes along with improvements in the quality of the final decision, indicating a trade-off between the time needed to deliberate and decision quality.

This perspective paper argues how a social network approach can contribute to creating a more comprehensive picture of how individual and community characteristics influence participation in community energy initiatives (CEIs). We argue how social network theory and methods for social network analysis can be utilized to better understand participation. Further, we show how this can potentially aid the implementation of interventions aimed at attracting more participants with more diverse socio-demographic backgrounds. Importantly, we argue that the structure of community social networks connecting (potential) participants could importantly influence whether and how individual and community properties affect CEI participation. Our aim is conveying the social network approach to the field of community energy researchers and stakeholders who might not be familiar with it. We discuss empirical evidence on the effect of network characteristics on CEI participation and the connection between research on CEIs and adjacent fields as a foundation for our claims. We also illustrate how a social network approach might help to overcome biased participation and low participation numbers, by providing social scientists with a tool to give empirically grounded advice to CEIs. We conclude by looking at avenues for future research and discuss how the context of CEIs might yield new theoretical insights and hypotheses.

Since the first publication of the bounded-confidence models 20 years ago, hundreds of articles studying this class of social-influence models have been written. Bounded-confidence models proposed an intriguing so-lution to a pervasive research puzzle and have helped unveil and explain intriguing phenomena. Here, we re-flect about remaining research problems and future modeling challenges, arguing that there remain counter-intuitive model implications to be understood. To illustrate that there remain uncovered model challenges, we extend the bounded-confidence model. We assume assimilative influence when agents connected by posi-tive relationships hold sufficiently similar opinions, adopting the core assumption of the bounded-confidence models. We combine this with another influential modeling approach, the notion that if agents connected by a negative social relationship disagree too much, opinion differences increase due to repulsive influence. This allows us to vary the relative strength of assimilation and repulsion in the influence dynamics, also allowing for the possibility that neither occurs in a particular interaction. Simulation experiments reveal three surpris-ing findings: Counter the intuition that stronger assimilation decreases opinion diversity, we show that in the presence of repulsion, intensifying the strength of assimilation can actually generate more opinion bipolariza-tion. Second, we show that if repulsion becomes weaker this may still result in more bipolarization. Third, it turns out that more negative social relationships between or within subgroups can result in less bipolarization. We demonstrate these effects in very simple and highly stylized settings, in order to show that intuition fails to capture the complexity arising from the interplay of assimilative and repulsive influence even in these sim-ple settings. We discuss implications of our findings for the ongoing debate about societal conditions fostering bipolarization, including in particular the design of personalized online social networks. Further, we address how our results may inform future work comparing and integrating alternative models of social-influence dy-namics.

We revisit literature about school choice and school segregation from the perspective of complexity theory. This paper argues that commonly found features of complex systems are all present in the mechanisms of school segregation. These features emerge from the interdependence between households, their interactions with school attributes and the institutional contexts in which they reside. We propose that a social complexity perspective can add to providing new generative explanations of resilient patterns of school segregation and may help identifying policies towards robust school integration. This requires a combination of theoretically informed computational modeling with empirical data about specific social and institutional contexts. We argue that this combination is missing in currently employed methodologies in the field. Pathways and challenges for developing it are discussed and examples are presented demonstrating how new insights and possible policies countering it can be obtained for the cases of primary school segregation in the city of Amsterdam.