# On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach

@article{Komiyama2020OnSD, title={On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach}, author={Junpei Komiyama and Shunya Noda}, journal={ArXiv}, year={2020}, volume={abs/2010.01079} }

We analyze statistical discrimination using a multi-armed bandit model where myopic firms face candidate workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. In such an environment, laissez-faire may result in a highly unfair and inefficient outcome---myopic firms are reluctant to hire minority workers because the lack of data about minority workers prevents accurate… Expand

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