Untangling Synergistic Effects of Intersecting Social Identities with Partial Information Decomposition

Abstract

The theory of intersectionality proposes that an individual’s experience of society has aspects that are irreducible to the sum of one’s various identities considered individually, but are “greater than the sum of their parts”. In recent years, this framework has become a frequent topic of discussion both in social sciences and among popular movements for social justice. In this work, we show that the effects of intersectional identities can be statistically observed in empirical data using information theory, particularly the partial information decomposition framework. We show that, when considering the predictive relationship between various identity categories such as race and sex, on outcomes such as income, health and wellness, robust statistical synergies appear. These synergies show that there are joint-effects of identities on outcomes that are irreducible to any identity considered individually and only appear when specific categories are considered together (for example, there is a large, synergistic effect of race and sex considered jointly on income irreducible to either race or sex). Furthermore, these synergies are robust over time, remaining largely constant year-to-year. We then show using synthetic data that the most widely used method of assessing intersectionalities in data (linear regression with multiplicative interaction coefficients) fails to disambiguate between truly synergistic, greater-than-the-sum-of-their-parts interactions, and redundant interactions. We explore the significance of these two distinct types of interactions in the context of making inferences about intersectional relationships in data and the importance of being able to reliably differentiate the two. Finally, we conclude that information theory, as a model-free framework sensitive to nonlinearities and synergies in data, is a natural method by which to explore the space of higher-order social dynamics.

Generalized decomposition of multivariate information

“…A similar argument can be made here: depending on the specific system being analysed, different information decompositions may be more or less appropriate. If there is a well-defined notion inputs and targets, such as when studying directed information flows in neural systems [ 9 ], or how multiple social identities synergistically inform on a single outcome [ 39 ], then a single-target PID may be the most appropriate. In contrast, if one is looking at higher-order generalizations of undirected functional connectivity [ 22 , 38 ], then a PED or GID could be more relevant.…”

Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions

“…While I have focused on the ΦID framework as a means of decomposing the excess entropy of ongoing, spontaneous neural dynamics in dissociated cultures, in principle the framework could apply to any data set with multiple, interacting predictor and predicted variables: the temporal dimension is not required. This opens up a wider range of applications of data analyses than is accessible to the classic PID—for example, Varley & Kaminski recently used the PID to asses how varying social identities (such as race and sex) jointly disclose information on single outcomes (such as income or health status) [ 61 ], however outcomes themselves are not independent and may contain interesting higher-order correlations within themselves. For example, how do the identities race and sex disclose information about income and health outcomes collectively?…”

Emergence of a synergistic scaffold in the brains of human infants

“…This is information that can only be learned by knowing the state of Region 1 and Region 2 and Region 3, and so on. For a more detailed discussion of redundancy, synergy, and logical implicature, see [96, 97]. Synergistic information requires a high degree of coordination between multiple regions, forming an integrated “whole” that is “greater than the sum of it’s parts” [19].…”