About this Research Topic
Empirically-oriented analyses of social inequalities in sociology, economics and demography are dominated by what one may call “attribute-oriented” view on social inequalities, according to which the analysis typically amounts to describe how given context- and/or individual-level features systematically impact on the likelihood an individual experiences a given social outcome (and how this likelihood is structured across social groups). Multivariate statistics for large survey data is the favored methodological support for this approach.
A complementary view is possible, however. As noted by Christakis and Fowler (2011: 300) in Connected (NY: Back Bay Books): “(...) there is an alternative view of understanding stratification and hierarchy that is based on how people are positioned with respect to their connections. Positional inequality occurs not because of who we are but because of who we are connected to. (…) Network inequality creates and reinforces inequality of opportunity”. Although scattered across several research domains, interaction-based approaches to job market opportunities, income inequalities, social mobility, spatial segregation or prestige distribution can indeed be clearly identified in sociology, economics, and social psychology (for an overview, see DiMaggio & Garip 2012).
The present Research Topic tries to foster and give visibility to the line of investigation that use algorithmic models formalizing clearly-spelled mechanisms that connect the dynamics of social interactions to macroscopic patterns of social inequalities. Recent examples can be found in Manzo (2013), Stoica & Flache (2014), Manzo & Baldassarri (2015), Pluchino & alii (2018, Huet & alii (2020), Garip and Zhao & Garip (2021) or Schulz & alii (2022). Particular attention will be devoted to proposals investigating how social interaction dynamics favor/impede the activation of heuristics (like “status beliefs”, imitation and reciprocation) that in turn may increase/decrease the probability of activation of specific micro-level cumulative advantage mechanisms (like “deferential cascades” or “poverty traps”), which in the end lead to higher/lower levels of group-based differentiation in the access to socially valued goods.
From a methodological point of view, papers based on (a combination of) social network analysis, web-based analytics, computational and mathematical models, and experiments will be especially appreciated. Although purely theoretical analyses may be of interest, papers trying to connect algorithmic models to empirical data are strongly encouraged.
Relatively short papers –no longer than 12 000 words (including references, tables and figures)– will be especially appreciated. Submissions will be accepted until the end of August 2024, and should be addressed to gianluca.manzo@sorbonne-university for a first assessment.
References
- DiMaggio P. Garip F. (2012). “Network Effects and Social Inequality”, Annual Review of Sociology, 38: 93-118.
- Manzo G. (2013) “Educational Choices and Social Interactions: A Formal Model and A Computational Test”, Comparative Social Research, 30, 47-100.
- Manzo G., Baldassarri D. (2015) “Heuristics, Interactions, and Status Hierarchies: An Agent-based Model of Deference Exchange”, Sociological Methods and Research, 44, 3, 329-387.
- Stoica V. I., Flache A. (2014) “From Schelling to Schools: A Comparison of a Model of Residential Segregation with a Model of School Segregation”, Journal of Artificial Societies and Social Simulation, 17, (1), 5.
- Huet S, Gargiulo F, Pratto F (2020) “Can gender inequality be created without inter-group discrimination?” PLoS ONE 15(8): e0236840.
- Schulz J., Mayerhoffer D. M., Gebhard A. (2022). “A network-based explanation of inequality perceptions”, Social Networks, 70, 306–324
- Zhao L., Garip, F. (2021). “Network Diffusion Under Homophily and Consolidation as a Mechanism for Social Inequality”, Sociological Methods & Research, 50(3), 1150–1185.
- Pluchino A., Biondo A. E., Rapisarda A. (2018) “Talent versus Luck: The Role of Randomness in Success and Failure”, Advances in Complex Systems, 21, 3 & 4, 1850014.
A complementary view is possible, however. As noted by Christakis and Fowler (2011: 300) in Connected (NY: Back Bay Books): “(...) there is an alternative view of understanding stratification and hierarchy that is based on how people are positioned with respect to their connections. Positional inequality occurs not because of who we are but because of who we are connected to. (…) Network inequality creates and reinforces inequality of opportunity”. Although scattered across several research domains, interaction-based approaches to job market opportunities, income inequalities, social mobility, spatial segregation or prestige distribution can indeed be clearly identified in sociology, economics, and social psychology (for an overview, see DiMaggio & Garip 2012).
The present Research Topic tries to foster and give visibility to the line of investigation that use algorithmic models formalizing clearly-spelled mechanisms that connect the dynamics of social interactions to macroscopic patterns of social inequalities. Recent examples can be found in Manzo (2013), Stoica & Flache (2014), Manzo & Baldassarri (2015), Pluchino & alii (2018, Huet & alii (2020), Garip and Zhao & Garip (2021) or Schulz & alii (2022). Particular attention will be devoted to proposals investigating how social interaction dynamics favor/impede the activation of heuristics (like “status beliefs”, imitation and reciprocation) that in turn may increase/decrease the probability of activation of specific micro-level cumulative advantage mechanisms (like “deferential cascades” or “poverty traps”), which in the end lead to higher/lower levels of group-based differentiation in the access to socially valued goods.
From a methodological point of view, papers based on (a combination of) social network analysis, web-based analytics, computational and mathematical models, and experiments will be especially appreciated. Although purely theoretical analyses may be of interest, papers trying to connect algorithmic models to empirical data are strongly encouraged.
Relatively short papers –no longer than 12 000 words (including references, tables and figures)– will be especially appreciated. Submissions will be accepted until the end of August 2024, and should be addressed to gianluca.manzo@sorbonne-university for a first assessment.
References
- DiMaggio P. Garip F. (2012). “Network Effects and Social Inequality”, Annual Review of Sociology, 38: 93-118.
- Manzo G. (2013) “Educational Choices and Social Interactions: A Formal Model and A Computational Test”, Comparative Social Research, 30, 47-100.
- Manzo G., Baldassarri D. (2015) “Heuristics, Interactions, and Status Hierarchies: An Agent-based Model of Deference Exchange”, Sociological Methods and Research, 44, 3, 329-387.
- Stoica V. I., Flache A. (2014) “From Schelling to Schools: A Comparison of a Model of Residential Segregation with a Model of School Segregation”, Journal of Artificial Societies and Social Simulation, 17, (1), 5.
- Huet S, Gargiulo F, Pratto F (2020) “Can gender inequality be created without inter-group discrimination?” PLoS ONE 15(8): e0236840.
- Schulz J., Mayerhoffer D. M., Gebhard A. (2022). “A network-based explanation of inequality perceptions”, Social Networks, 70, 306–324
- Zhao L., Garip, F. (2021). “Network Diffusion Under Homophily and Consolidation as a Mechanism for Social Inequality”, Sociological Methods & Research, 50(3), 1150–1185.
- Pluchino A., Biondo A. E., Rapisarda A. (2018) “Talent versus Luck: The Role of Randomness in Success and Failure”, Advances in Complex Systems, 21, 3 & 4, 1850014.
Keywords: mechanism-based models, social inequalities, stratification, social hierarchies, income inequalities, spatial segregation
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