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Modelling a Centralized Academic Labour Market: Efficiency and Fairness

Philippe Caillou 1, 2, 3 Michèle Sebag 1, 2, 3, 4
2 TANC - Algorithmic number theory for cryptology
Inria Saclay - Ile de France, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : A formalization of the centralized French Academic Labour Market in terms of Multi-Agent System is presented. Extensive simulations are used to investigate typical and possible regimes of the system, supporting a sensitivity analysis of the main parameters of the model, such as the applicative pressure and the preference bias towards local candidates. Based on the calibration of the parameters of the abstract model after the information disclosed from o±cial sources, some positive and normative results are presented. Three prototypical settings are distinguished: the equilibrium setting corresponds to the perfect market case, where each candidate interacts with each University; the Humanities and Sciences setting; and the Law and Management setting. The differences between the last two settings concern both the applicative pressure (number of candidate per academic position) and the scope of each candidate (interacting with few or many Universities). Counter-intuitive findings, related in particular to the role of the preference biases, are presented and discussed.
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Contributor : Philippe Caillou Connect in order to contact the contributor
Submitted on : Friday, September 5, 2008 - 4:04:31 PM
Last modification on : Thursday, July 8, 2021 - 3:48:20 AM
Long-term archiving on: : Monday, October 8, 2012 - 12:56:59 PM


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  • HAL Id : inria-00319072, version 1



Philippe Caillou, Michèle Sebag. Modelling a Centralized Academic Labour Market: Efficiency and Fairness. ECCS 2008, Sep 2008, Jerusalem, Israel. ⟨inria-00319072⟩



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