Societies are inherently conflictual. Party competition, cleavage structures, social movements, more or less disruptive protests, riots, revolts, revolutions and (civil) wars are among the prominent forms social conflict take in current societies. These different forms of conflict are usually addressed from different perspectives with distinct theoretical traditions and widely differing normative evaluations. International relations scholars focus mainly on violent, usually militarized inter- and intra-state conflicts with a strong focus on how to avoid, solve or at least mitigate these conflicts (Bercovitch, Kremenyuk, Zartman 2008). Social movement scholars, on the other hand, usually are interested in non-militarized conflicts and interpret them as principal driving forces of social change (della Porta and Diani 2006). A call to unite the different perspectives in a shared research on dynamics of contention (McAdam, Tarrow, Tilly 2003) has been applauded but remained largely without consequences. Even within the various perspectives, a focus on dynamic processes is still the exception.

Within the field of social movement studies, conflict dynamics are explicitly addressed in studies on protest waves (Koopmans 2004), in studies on mobilization networks (Diani and Kousis 2014) and discourse dynamics (Johnston and Alimi 2013). Computational Social Science provides promising tools especially for the analysis of conflictual discourse dynamics. Computer linguistic tools for text mining and content analysis unlock large text corpora (in particular newspaper articles and social media posts) that enable analyses of discourse dynamics that so far were limited by time and personnel-intensive human coding requirements. Combinations of stochastic actor-oriented (Snijders, Lomi, und Torló 2013) or temporal exponential random graph network models (Krivitsky and Handcock 2015) with frame analytical methods allow for the modeling of discourse dynamics as dynamic discourse networks (Haunss, Dietz, and Nullmeier 2013, Leifeld 2013, Leifeld and Haunss 2012).

International relations scholars have focused on inter- and intra-state conflicts. Ex-post investigations of armed conflicts on an annual basis take country characteristics as the main explanatory factors (e.g. Blattman & Miguel 2010, Levy & Thompson 2010). More recent data collection projects included non-state actors and non-violent campaigns (Chenoweth  & Stephan 2011). Additionally, geographic information has been recently made available on political actors (Wucherpfennig et al. 2011; Weidmann et al. 2010), infrastructure (Hansen et al. 2000; Nelson 2008; Toellefsen et al. 2012), and conflict events (Raleigh and Hegre 2005; Sundberg et al. 2010; Leetrau and Schrodt 2013). Just focussing on violent conflicts introduces a persistent selection bias into the empirical models derived. Understanding the driving factors for violence requires a more gradual analysis of different conflict levels. The alternative to violent conflict is typically not ‘no conflict.’ In our summer schools we are going to investigate both, violent and non-violent, conflicts putting a specific focus on trajectories that have a large risk to result in violent actions.

More generally, differences in policy preferences can evolve in societies and create bipolarized patterns. Further on, distributions of resources such as income, education, wealth, and health in a society typically have a tendency to evolve toward more skewed distributions and consequently towards more inequality. This needs to be incorporated into forecasting models. In autocratic as well as in democratic systems such tensions can give rise to conflicts which can lead to oppression, emigration, or even take a violent route. They can be mitigated or they can escalate. Such processes shall be modeled in a dynamical way either with a data-driven focus on existing or easy to collect behavioral data, or based on behavioral assumptions which are defined in a way that some macroscopic properties of the derived dynamical model can be validated with data.

From a methodological perspective, the traditional logistic regression analysis used to model the empirical data lacks predictive power to adequately describe the underlying relationships. Data-driven classification methods from machine learning provide a framework for improved predictions (Muchlinski et al 2016, Helbing et al. 2014). They make standard use of out-of-sample error considerations and model evaluations. Utilizing the increasing number of data sets, such principles should be routinely employed for forecasting models.

A related and vivid area of research into which our projects will tie is that of computational modelling of opinion dynamics in the emergence of potentially conflictual issues in society. While experimental and survey research has yielded much insight into determinants of opinion formation at the individual level, researchers have not yet a sufficient understanding of how such processes add up to generate opinion dynamics in society as a whole. Identifying the conditions and the mechanisms of consensus, diversity and polarization in large-scale opinion dynamics is a major scientific issue with a long tradition of vivid debate (Mason, et al. 2007). In recent years, agent-based computational modelling work made great advances in understanding how micro-level processes link with societal outcomes. Theoretical work (Hegselmann & Krause 2002; Lorenz 2006; Flache & Macy 2011a,b; Mäs, Flache, Takács & Jehn, 2013; Mäs & Flache, 2013) highlighted how the evolution of an opinion distribution at population level results from numerous simultaneous interactions between individuals, connected by heterogeneous social networks and embedded in diverse local and socio-demographic contexts. However, this work also showed that the way how micro-level mechanisms are exactly specified in such dynamics may crucially affect the macro-level implications of these processes (e.g. Flache & Macy, 2011a,b; Schutte 2009; Mäs et al 2013). This points to the pressing need to inform assumptions about the psychological and micro-social processes in modelling large-scale opinion dynamics in a much more precise way with available theoretical and empirical knowledge than in previous work. Recently, there is a small but growing number of studies that aim to obtain from on- and offline experiments, analysis of social media data, and large scale survey empirically more accurate quantitative models of how individuals are influenced by exposure to opinions of others, and how they selectively interact or consider others’ opinions based on similarity. In the schools, research projects will be conducted that tie into this frontier of research, addressing empirical methods of obtaining data about social influence and social selection processes at the micro-level, and showing how to utilize these data to improve computer models of public opinion formation by better grounding models in available theoretical work on microprocesses, new theory-guided experiments and field studies. This will allow participants to elaborate and test predictions from models about the association between macro-social characteristics and predictions about related societal developments in terms of consensus, pluralist diversity or polarization on key environmental, social and political issues. Such tests will be conducted using data from large scale opinion surveys, such as European Social Survey or the World Values Survey available in the stock of data sets we will utilize.

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