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The cultural impact on social cohesion: an agent-based modeling approach

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Abstract

Social processes in modern multicultural societies require a better conceptual understanding of the mechanisms of cultural events’ impact on social welfare. Due to a number of objective reasons, one of the critical challenges in this complex research area is the lack of empirically based predictive models. The current paper provides an alternative approach—a bottom-up (from agents to social systems) modeling how cultural events can shape social cohesion measured by social capital and cultural features probabilistic clustering in the population. In this paper, based on prior empirical observations, proposed agent-based modeling can help (i) understand and interpret some empirical findings, and (ii) foresee outcomes of otherwise very costly real-life social experiments. To this end, this paper presents an agent-based simulation model to demonstrate the simple mechanism of how cultural events can impact the empirically observed complex dynamics of social capital. Presented model is implemented in the NetLogo simulation environment, where simple agents’ behavioral properties are simulated following basic empirical observations. Implemented simulation approach upgrades Axelrod's classical model of cultural dissemination in three main ways. First, it models agents' neighborhood interactions not only in the simulated agents' physical space but in the cultural features space as well. Second, the model simulates the dissemination of cultural events’ impact (not only) through pair-based neighborhood interaction but also through wide-range social media and networks broadcasting. Third, it implements some agents’ inherent propensity toward differentiation (uniqueness) that generates divergence in the virtual space of characteristic behavioral cultural features. Simulation results provide not only proof of concept but also reveal underlying cultural conditions for the emergence of different behavioral patterns of social capital cohesion or fragmentation.

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Notes

  1. The concept of social capital should not be confused with the concept of cultural capital, which has been used in sociology to study the impact of cultural reproduction on social reproduction (Lamont and Lareau 1988) and another forms of intangible capitals like intellectual.

  2. Broadly speaking, SC refers to factors that include not only interpersonal relationships, but also a shared sense of identity, understanding, norms, values, trust, cooperation, and reciprocity (Adler and Kwon 2002).

  3. The four OECD SC components are (i) personal relationships, which refers to the structure of individual networks and the social behaviors that contribute to establishing and maintaining those networks, such as spending time with others or exchanging information in communication; (ii) social network support, which is a direct outcome of an individual’s relationships and refers to the resources—emotional, material, practical, financial, intellectual, or professional—that are available to each individual through their personal social networks; (iii) civic engagement, which comprises the activities and networks through which individuals contribute to civic and community life, such as volunteering, political participation, group membership, and different forms of community action; and (iv) trust and cooperative norms, which refers to the trust, social norms, and shared values that underpin societal functioning and enable mutually beneficial cooperation. The types of trust that are most often considered as forms of social capital are generalized trust (i.e., trust in “others”, including strangers) and institutional trust, which can refer to political institutions as well as the judiciary, police, media, and other institutions (Scrivens and Smith 2013).

  4. OECD (2013), "The question ‘databank’", in The OECD measurement of social capital project and question databank, OECD Publishing, Paris, http://www.oecd.org/sdd/social-capital-project-and-question-databank.htm

  5. OECD (2005), "Social Cohesion Indicators", in Society at a Glance 2005: OECD Social Indicators, OECD Publishing, Paris, https://doi.org/10.1787/soc_glance-2005-8-en.

  6. Particular scenarios (sequences) o f cultural events can be implemented using consistent cultural politics and agendas.

  7. As we mentioned in the introduction, this idea originates from numerous researchers who claim that instead of joining groups in our neighborhoods, we are now joining social groups made up of people who share our interests and beliefs. These cultural groups may exist only virtually, such as on Internet-based social networks (Nie 2001; Wang & Wellman 2010; Zhao 2006; Brian 2007). This has implications for the acceleration of a new form of networking capital—social capital (Schuller 2000; Scrivens and Smith 2013).

  8. Some other well-known models can be employed for this matter, but they lack some properties that are important in our model setup. Examples include the voter model (a voter's opinion at any given time can take only one of two values), Schelling’s physical segregation model (used for topological distribution), and the Markov chain model (describes a sequence of possible events depending only on the state attained in the previous event).

  9. Culture plays an essential part in SC development. Empirical studies have revealed that culture and SC are correlated, and some argue that they can be causally related (Delaney and Keaney 2006; Li et al. 2015; Mouw 2006).

  10. There are several later advancements of the classical Axelrod model that pertain to some of our assumptions. For instance, an alternative mechanism based not on the form of the interaction, but on the structural properties of the social network (Battiston et al., 2017); a computational model of the co-evolution of cultural, social, and physical space (Pfau et al. 2013); and an empirically plausible assumption that influence is social—people can be simultaneously influenced by several network neighbors (Flache and Macy 2011).

  11. As Axelrod put it: “With recent advances in transportation, mass media, and information technology, many interactions are now largely independent of geographical distance. With random long-distance interactions, the heterogeneity sustained by local interaction cannot be sustained” (Axelrod 1997; Reia and Fontanari 2016).

  12. Given a set of points in some space (agents in our case), DBSCAN groups together points that are closely packed together (points with many nearby neighbors), marking as outliers’ points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.

  13. In this experiment, we have set the social capital weight value parameter to 0.1 (see the previous section). However, the same tendency is followed when applying higher values.

  14. There are many agent-based social research disciplines such as agent-based social simulation, computational sociology, social complexity, social simulation, agent-based computational economics, artificial society, etc. For instance, agent-based social simulation (ABSS) is a scientific discipline concerned with the simulation of social phenomena using computer-based multiagent models (Davidsson 2002). In these simulations, persons or groups of persons are represented by agents. ABSS is a combination of social science, multiagent simulation, and computer simulation. Social science is a mixture of sciences and the social part of the model. It is where social phenomena are developed and theorized. The main purpose of ABSS is to provide models and tools for agent-based simulation of social phenomena. With ABSS, one can explore different outcomes of phenomena where it may not be possible to view real-life outcomes. It can provide us valuable information on society and the outcomes of social events or phenomena.

  15. It is important to add that in information science, a conceptualization is an abstract simplified view of some selected part of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them.

  16. OECD (2013), " The question ‘databank’", in The OECD measurement of social capital project and question databank, OECD Publishing, Paris, http://www.oecd.org/sdd/social-capital-project-and-question-databank.htm

    OECD (2005), "Social Cohesion Indicators", in Society at a Glance 2005: OECD Social Indicators, OECD Publishing, Paris, https://doi.org/10.1787/soc_glance-2005-8-en.

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Acknowledgements

This research was funded by a grant (No. P-MIP-17-368) from the Research Council of Lithuania.

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  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Darius Plikynas, Arūnas Miliauskas, Rimvydas Laužikas, Vytautas Dulskis & Leonidas Sakalauskas

  2. Department of Natural Sciences, Klaipeda University, Klaipeda, Lithuania

    Leonidas Sakalauskas

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  1. Darius Plikynas
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Plikynas, D., Miliauskas, A., Laužikas, R. et al. The cultural impact on social cohesion: an agent-based modeling approach. Qual Quant 56, 4161–4192 (2022). https://doi.org/10.1007/s11135-021-01293-6

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