Markus Spiske on Unsplash
What can math tell us about unfairness? Bias, discrimination, and inequity are phenomena that are deeply complex, context sensitive, personal, and intersectional. The mathematical modelling of social scenarios, on the other hand, is a practice that necessitates simplification. Using models to understand what happens in our social realm means representing the complex with something much less complex in order to study it.
Nonetheless, when used carefully models can help us learn about deep patterns related to inequity, including those that govern how unfair norms and patterns of behaviour emerge in groups of people. They are especially useful in highlighting spaces of possibility. What might happen in the complex dynamics of the social realm? Once we answer this question, we can use experiments and experimental studies to learn more about these possibilities.
In recent work by fellow modellers and myself, we’ve looked in particular at what minority status means for patterns of unfair behaviour. Suppose you are part of a company where people work together, but are still figuring out some of the social rules for how this is done. Suppose further that men make up the majority of workers and women the minority. And lastly suppose, as argued by Cecilia L. Ridgeway in Framed by Gender, that like most humans your coworkers notice gender, and use it to shape their interactions with others in ways that can be subtle (and sometimes not-so-subtle). In a case like this, even without gender biases coming into play, women can end up disadvantaged.
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Labels:
evolutionary_biology, discrimination, social_modelling, anthropology, sociology,
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