104 research outputs found
Is diversity good?
Prominent ethical and policy issues such as affirmative action and female
enrollment in science and engineering revolve around the idea that diversity is
good. However, even though diversity is an ambiguous concept, a precise
definition is seldom provided. We show that diversity may be construed as a
factual description, a craving for symmetry, an intrinsic good, an instrumental
good, a symptom, or a side effect. These acceptions differ vastly in their
nature and properties. The first one cannot lead to any action and the second
one is mistaken. Diversity as intrinsic good is a mere opinion, which cannot be
concretely applied; moreover, the most commonly invoked forms of diversity
(sexual and racial) are not intrinsically good. On the other hand, diversity as
instrumental good can be evaluated empirically and can give rise to policies,
but these may be very weak. Finally, symptoms and side effects are not actually
about diversity. We consider the example of female enrollment in science and
engineering, interpreting the various arguments found in the literature in
light of this polysemy.
Keywords: ethics, policy, higher education, female students, minority
students, affirmative actionComment: 7 page
Academic team formation as evolving hypergraphs
This paper quantitatively explores the social and socio-semantic patterns of
constitution of academic collaboration teams. To this end, we broadly underline
two critical features of social networks of knowledge-based collaboration:
first, they essentially consist of group-level interactions which call for
team-centered approaches. Formally, this induces the use of hypergraphs and
n-adic interactions, rather than traditional dyadic frameworks of interaction
such as graphs, binding only pairs of agents. Second, we advocate the joint
consideration of structural and semantic features, as collaborations are
allegedly constrained by both of them. Considering these provisions, we propose
a framework which principally enables us to empirically test a series of
hypotheses related to academic team formation patterns. In particular, we
exhibit and characterize the influence of an implicit group structure driving
recurrent team formation processes. On the whole, innovative production does
not appear to be correlated with more original teams, while a polarization
appears between groups composed of experts only or non-experts only, altogether
corresponding to collectives with a high rate of repeated interactions
The Calculus of Committee Composition
Modern institutions face the recurring dilemma of designing accurate evaluation procedures in settings as diverse as academic selection committees, social policies, elections, and figure skating competitions. In particular, it is essential to determine both the number of evaluators and the method for combining their judgments. Previous work has focused on the latter issue, uncovering paradoxes that underscore the inherent difficulties. Yet the number of judges is an important consideration that is intimately connected with the methodology and the success of the evaluation. We address the question of the number of judges through a cost analysis that incorporates the accuracy of the evaluation method, the cost per judge, and the cost of an error in decision. We associate the optimal number of judges with the lowest cost and determine the optimal number of judges in several different scenarios. Through analytical and numerical studies, we show how the optimal number depends on the evaluation rule, the accuracy of the judges, the (cost per judge)/(cost per error) ratio. Paradoxically, we find that for a panel of judges of equal accuracy, the optimal panel size may be greater for judges with higher accuracy than for judges with lower accuracy. The development of any evaluation procedure requires knowledge about the accuracy of evaluation methods, the costs of judges, and the costs of errors. By determining the optimal number of judges, we highlight important connections between these quantities and uncover a paradox that we show to be a general feature of evaluation procedures. Ultimately, our work provides policy-makers with a simple and novel method to optimize evaluation procedures
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