Topic: Social Cohesion

Maintaining and enhancing social cohesion is a key challenge for modern societies. It manifests in the treaties of the European Union as well as on the agendas of most political parties. The concept of cohesion is notoriously vague and has been criticized for being a “quasi-concept” which cannot be quantified and often serves as something abstract to which any arbitrary policy can be related to. Nevertheless, its ubiquity as a policy goal makes attempts to quantify and analyze it a key challenge for public policy making. From a network perspective, high social cohesion can be conceptualized as a balance of close-knit, positive relations among individuals, on the one hand, and positive connectivity of society as a whole without antagonist subpartitions, on the other hand. From an attitude and norms perspective, cohesion can be conceived of as consensus on core societal norms and attitudes even if subgroups in society are otherwise diverse and distinct. Both a high degree of network segregation, as well as a high degree of attitude polarization between subgroups would be indicators of a strong lack of societal cohesion, and could also be conceived of as interrelated phenomena. Dragolov et al. (2016) provide a measurement concept in line with this definition accompanied by a dataset of cohesion indicators for 34 Western countries over the years 1989-2012.

One area in particular in which computational modelling has proven to foster our understanding of dynamics that affect social cohesion is the modelling of residential ethnic or racial segregation. Schelling (1969, 1971, 1978) provided the perhaps most prominent example of micro-macro interaction in complex social systems. His computational model of interdependent residential choices shows how the macro-outcome of strong ethnic residential segregation can emerge as unintended consequence of a self-organization process driven by individuals’ “mild” ethnic preferences. Under this dynamic, integrated residential distributions are typically unstable, even when individuals are largely tolerant of other ethnic groups in their neighborhood, but prefer to not belong to too small a minority. If residents of a small local minority leave a neighborhood, this changes the local ethnic mix such that members of their ethnic group become even more likely to leave, which eventually pushes the residential distribution away from unstable integration towards stable segregation. This self-reinforcing “preference dynamic” (Clark & Fosset, 2008) can generate a degree of residential segregation that exceeds by far what would be needed to satisfy ethnic preferences of individuals, an undesired outcome from the perspective of individuals, with potentially severe negative societal consequences.

Numerous follow-up studies using computational modelling in economics, sociology, and physics considerably advanced Schelling’s model, and confirmed its fundamental insight. Economists showed analytically how segregated outcomes are stochastically stable under a large range of conditions (Zhang 2004a,b; Young 1998), physicists linked Schelling’s preference dynamic to the Ising model of ferromagnetism and coarsening phenomena well understood in statistical mechanics (e.g. Stauffer & Solomon 2007; Vinkovic & Kirman 2006). Further studies explored a wider range of conditions and specifications of preference functions and migration dynamics  (e.g. Helbing & Yu, 2009; Macy & Van de Rijt 2006; Fosset, 2006; Hegselmann & Flache, 1998) and applications to other realms, such school segregation (Stoica & Flache, 2014). Importantly, extensions calibrated to empirical data on residential preferences, population composition and socio-demographic composition observed in U.S. cities showed that “complex and sometimes subtle segregation patterns seen in real urban environments” (Clark & Fosset 2008) could be well reproduced by advanced computational models of self-organizing preference dynamics (cf. Benenson et al., 2009).

Despite promising first steps, hitherto the great theoretical advances made by work drawing on Schelling’s model of the self-organization of ethnic segregation stand in stark contrast to the dearth of studies that use empirical data to either calibrate model assumptions or test model predictions, or combine both elements. The work of Fosset and Clark (2008), drawing on the “Metropolitan Study of Urban Inequality” conducted in the Los Angeles Area in the US, is one of the few studies using empirical data to inform model assumptions about the ethnic preferences that drive ethnic segregation dynamic in a Schelling-type model. One promising future research area to be further exploited in the summer school on social cohesion, is to also use both data on residential preferences and data on a fine-grained scale to model initial distributions of ethnicities (e.g. Cable, 2013).

Another research area is the emergence and dynamics of opinion polarization in society, often modelled as being accompanied by emergent social segregation between groups. Recently, many societies shifted towards more polarization and volatility in opinions, for example in attitudes about immigration or climate policy, but underlying reasons are poorly understood. A key obstacle is that opinion dynamics in society involve a complex micro-macro interaction between fundamental interpersonal processes of social influence, meso-level conditions like network structures, and macro-level outcomes, like polarization in opinion distributions. Agent-based simulation models (ABM) offer powerful tools to bridge theoretically micro-level processes and macro-level dynamics. However, as a recent review of the field has argued, ABM research on modelling of social influence is in need of more theoretical integration of the large variety of models that have been proposed, and needs to make progress on empirically testing model assumptions and predictions against data from different sources, like opinion surveys, social influence experiments or social media (Flache et al, 2017).

A third example of a relevant area is exploitation of the increasing availability of data on stated residential preferences and moving histories that inform the estimation of discrete choice models for residential decisions (Bruch & Mare, 2012). With this, it can be explored how local migration behavior based on empirically assessed residential preferences would affect the makeup of real cities under advanced Schelling-type models, and participants can explore the effects of different sets of assumption on moving behavior and possible policies.

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