Abstract

In The Origins of Unfairness, Cailin O’Connor develops a series of evolutionary game models to show that gender might have emerged to solve coordination problems in the division of labor. One assumption of those models is that agents engage in gendered social learning. This assumption puts the explanatory cart before the horse. How did early humans have a well-developed system of gendered social learning before the gendered division of labor? This paper develops a pair of models that show it is possible for the gendered division of labor to arise on more minimal assumptions.

Highlights

  • In The Origins of Unfairness, Cailin O’Connor outlines a novel theory of the origins of gender (O’Connor 2019)

  • Her models are illuminating but have a difficulty. She assumes that agents engage in gendered social learning as the mechanism by which successful strategies spread through a population. This seems to put the explanatory cart before the horse—how did early humans have a well-developed system of gendered social learning before the gendered division of labor? If we want to explain the origins of gender, one should not help themself to gender-like behavior

  • While the models assume this sort of learning in order to get gendered division of labor, perhaps that is putting the cart before the horse. (O’Connor 2019, 66) But she leaves it as a task for future research, “a full account of how groups manage to get all these features in place at once is beyond this book.” (67) The remainder of this paper develops an account of how it is possible for gendered strategic behavior and gendered social learning to co-evolve

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Summary

Gender as an Evolved Coordination Device

This section describes a game-theoretic model of the evolution of gender that has been developed in previous work (O’Connor 2019). This paper is not a broad defense or critique of O’Connor’s view. Motivating the theory makes it easier to understand this paper’s contribution

Gender and Social Convention
Evolving Gender in Models
The Circularity Problem
From Equations to Agents
An Agent-Based Model
The Inertial Mechanism
Implications
The Co-Evolution of Gendered Learning and Strategic Interaction
Core Results
Robustness by Mutation Rates
Growing Gender from Scratch
Conclusion
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