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Comparing hiring strategies in a committee with similarity biases

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Abstract

We present an organizational model that develops organizational expertise and socialization with a hiring process informed by the inherent biases of individuals. We present factors that we believe critically impact candidate selection, literature related to these factors, and our resulting equations. We discuss the model, and present two virtual experiments. The first virtual experiment was used to validate the new model by comparing the implementation with an existing reference implementation—we found similar patterns—which established relational equivalence. The second virtual experiment compared organizations with and without a stochastic selection process and with various selection strategies. Organizations that stressed socialization tended to need to review more (otherwise equally qualified) applicants than organizations that did not, and organizations that were able to deliberate more thoroughly found turnover less effective at maintaining organizational performance. Larger committees reduced the number of applicants that needed to be reviewed in firms that valued diversity, but offered no particular benefit to other organizations.

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Acknowledgements

We are grateful to the Office of Naval Research for funding this work (Contract #: N00014-08-1-1186). We also appreciate Jonathan H. Morgan’s comments and suggestions.

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Correspondence to Geoffrey P. Morgan.

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Previous Presentation of Work: A preliminary version of this work was submitted and published to the Proceedings of the Twentieth Conference on Behavior Representation in Modeling and Simulation (BRiMS) under the title “Exploring the Impact of a Stochastic Hiring Function in Dynamic Organizations”. This work is greatly expanded from the earlier work. This paper contains new work both in the form of multiple selection models (see Sect. 4.2) and a much extended virtual experiment, described in Sect. 6.

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Morgan, G.P., Carley, K.M. Comparing hiring strategies in a committee with similarity biases. Comput Math Organ Theory 20, 1–19 (2014). https://doi.org/10.1007/s10588-012-9130-1

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