Reinforcement generates systematic differences without heterogeneity

IF 9.1 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Alexandros Gelastopoulos, Lucas Sage, Arnout van de Rijt
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引用次数: 0

Abstract

Inequality in outcomes may emerge through a reinforcement process in which stochastic variation in values is determined by prior values but may also originate in preexisting differences in unobserved factors. A common approach toward differentiating between these origins in longitudinal data is to attribute systematic differences between units—differences in means or differences proportional to a time-varying group average—to unobserved heterogeneity. We show that any longitudinal data with systematic differences can also be produced by a reinforcement-driven data generating process. This result reconciles findings in three distinct research areas—science of science, personal culture, and sexual networks—where reinforcement is a strong theoretical prior, yet longitudinal data analyses advance an explanation of interpersonal differences based on heterogeneity. Future studies may bound the role of heterogeneity and reinforcement from below by measuring fixed traits that systematically vary with the outcome and isolating random events that trigger emergent differences.
强化产生系统差异,但不存在异质性
结果的不平等可能通过一个强化过程出现,在这个过程中,值的随机变化是由先前的值决定的,但也可能源于先前存在的未观察到的因素的差异。在纵向数据中区分这些来源的一种常见方法是将单位之间的系统差异归因于未观察到的异质性,即平均值的差异或与时间变化的组平均值成比例的差异。我们表明,任何具有系统差异的纵向数据也可以通过强化驱动的数据生成过程产生。这一结果与三个不同的研究领域——科学科学、个人文化和性网络——的发现相一致,在这些领域,强化是一个强有力的理论前提,然而纵向数据分析提出了基于异质性的人际差异的解释。未来的研究可能会通过测量随结果系统变化的固定特征和隔离触发紧急差异的随机事件来约束异质性和自下而上强化的作用。
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来源期刊
CiteScore
19.00
自引率
0.90%
发文量
3575
审稿时长
2.5 months
期刊介绍: The Proceedings of the National Academy of Sciences (PNAS), a peer-reviewed journal of the National Academy of Sciences (NAS), serves as an authoritative source for high-impact, original research across the biological, physical, and social sciences. With a global scope, the journal welcomes submissions from researchers worldwide, making it an inclusive platform for advancing scientific knowledge.
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