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Reinforcement generates systematic differences without heterogeneity
Alexandros Gelastopoulos1,2,3,4, Lucas Sage5,6, Arnout van de Rijt5
1Department of Business and Management, University of Southern Denmark, Odense 05230, Denmark.
Summary
Inequality in outcomes can arise from reinforcement processes or unobserved differences. Our findings show that reinforcement alone can explain observed systematic differences in longitudinal data, challenging traditional heterogeneity explanations.
Area of Science:
- Social sciences
- Network analysis
- Sociology
Background:
- Inequality in outcomes can stem from reinforcement processes or unobserved factors.
- Longitudinal data analysis often attributes systematic differences to unobserved heterogeneity.
Purpose of the Study:
- To demonstrate that reinforcement processes can generate data previously attributed solely to unobserved heterogeneity.
- To reconcile findings across diverse research areas using a unified data-generating process.
Main Methods:
- Theoretical modeling of data generation processes.
- Analysis of longitudinal data structures.
- Comparison of reinforcement models with heterogeneity models.
Main Results:
- Longitudinal data exhibiting systematic differences can be fully explained by a reinforcement-driven process.
- The study reconciles findings in science of science, personal culture, and sexual networks.
- Reinforcement provides an alternative explanation for interpersonal differences.
Conclusions:
- Unobserved heterogeneity may not be the sole driver of systematic differences in longitudinal data.
- Reinforcement processes offer a parsimonious explanation for emergent inequalities.
- Future research should differentiate the roles of heterogeneity and reinforcement through empirical measures.
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