ToRealSim
Towards realistic computational models of social influence dynamics
Andreas Flache (PI), Michael Mäs, Vincenz Frey, Dieko Bakker (until July 2021)
In recent years, our societies are polarizing in central political and social attitudes, for example about immigration, European integration or climate policy (1–5). The identification of conditions and mechanisms underlying the emergence of consensus, opinion clustering and polarization in large-scale opinion dynamics continues to be a formidable scientific challenge (6). A particular challenge is that societal tendencies at the macro-level, like trends towards polarization or increasing extremism, need not reflect that individuals intend to bring about or prefer a polarized society (7, 8). In the other direction, knowing the individual-level factors like education, party preferences, news diet, or religion is insufficient to explain the dynamics of macro-level opinion distributions (9). Opinion dynamics in society emerge from simultaneous interactions between numerous individuals, connected by heterogeneous social networks within diverse local and sociodemographic contexts. A key challenge is thus to understand the complex link between micro-processes of social influence and their societal macro-outcomes.
ToRealSim combines theoretical and empirical work. Ultimately, we want to know which mix of empirically validated assumptions about micro-processes of social influence and macro- and meso-level contextual conditions (such as network structures, demographic heterogeneity, or ethnic residential segregation) is best suited to explain empirically observed opinion dynamics in an empirical realm of interest. To achieve this, we will use a social simulation approach, theoretically comparing existing models to identify which differences in their assumptions are responsible for differences in their predictions. To facilitate these comparisons, a joint simulation framework has been developed in which models can easily be (re)constructed and aligned. Next, we test these so-called “critical” assumptions in targeted empirical work, and then finally integrate our insights again with social simulation to develop models for selected empirical contexts that can explain observed opinion dynamics and provide insights useful for public debate and policy. The empirical contexts we address in this project are selected from two key realms: issues related to globalization (e.g. migration) and issues related to environmental policy (e.g. climate policy) (10-13), varying across a number of European countries.
Consortium
The consortium contains researchers from the Netherlands, the UK, France, and Germany, leading experts on agent-based computational models of social-influence dynamics and on the empirical research methods we employ. This unique combination of skills comprises the necessary ingredients to clearing the roadblocks and bringing the power of computational modeling to bear on our understanding of empirical social influence dynamics.
MORE INFORMATION
defSim
Joint Simulation Framework
Social Influence Wiki
Community Resource
Models of Social Influence
Foundational paper
References
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6. Mason WA, Conrey FR, Smith ER (2007) Situating social influence processes: Dynamic, multidirectional flows of influence within social networks. Personal Soc Psychol
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7. Macy M, Flache A (2009) Social Dynamics from the Bottom up. Agent-Based Models of Social Interaction. The Oxford Handbook of Analytical Sociology, eds Hedström P, Bearman P (Oxford University Press, Oxford), pp 245–268.
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12. Espenshade TJ, Hempstead K (1996) Contemporary American Attitudes Toward U.S. Immigration. Int Migr Rev 30(2):535.
13. Davidov E, Semyonov M (2017) Attitudes toward immigrants in European societies. Int J Comp Sociol 58(5):359–366.