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Institution

Paris Dauphine University

EducationParis, France
About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Approximation algorithm. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.


Papers
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TL;DR: Why modular approaches might be preferable to the full model in misspecified settings is investigated and a principled criteria to choose between modular and full-model approaches is proposed.
Abstract: In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference, prediction, or decision problem. In such circumstances, it is convenient to use a graphical model to represent the statistical dependencies, via a set of connected "modules", each relating to a specific data modality, and drawing on specific domain expertise in their development. In principle, given data, the conventional statistical update then allows for coherent uncertainty quantification and information propagation through and across the modules. However, misspecification of any module can contaminate the estimate and update of others, often in unpredictable ways. In various settings, particularly when certain modules are trusted more than others, practitioners have preferred to avoid learning with the full model in favor of approaches that restrict the information propagation between modules, for example by restricting propagation to only particular directions along the edges of the graph. In this article, we investigate why these modular approaches might be preferable to the full model in misspecified settings. We propose principled criteria to choose between modular and full-model approaches. The question arises in many applied settings, including large stochastic dynamical systems, meta-analysis, epidemiological models, air pollution models, pharmacokinetics-pharmacodynamics, and causal inference with propensity scores.

70 citations

Posted Content
TL;DR: In this article, it was shown that the ex ante incentive compatible core of an exchange economy with private information is always non-empty, even if utility functions are quasi-linear.
Abstract: The ex ante incentive compatible core of an exchange economy with private information is the (standard) core of a socially designed characteristic function, which expresses the fact that coalitions allocate goods by means of random incentive compatible mechanisms. We first survey some results in the case of perfectly divisible goods. Examples then show that the ex ante incentive compatible core can be empty, even if utility functions are quasi-linear. If, in addition to quasi-linearity, further assumptions are made (like independent private values), the non-emptiness of the core follows nevertheless from d'Aspremont and Gerard-Varet's construction of incentive compatible, ex post efficient mechanisms. We also introduce a private information version of Shapley and Scarf's economies with indivisible goods, and prove that the ex ante incentive compatible core is always non-empty in this framework.

70 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyze the factors that drive the adoption of innovative resource efficiency strategies to reduce energy and material use, under different market conditions, and uncover the paradox of lower adoption of resource efficiency strategy in an economic downturn and identify the characteristics of firms that adopt these strategies.

70 citations

Journal ArticleDOI
TL;DR: In this paper, the authors introduce a minimal flow-like variational problem whose solution is characterized by a very degenerate elliptic PDE and prove regularity results for the degenerate EDE.

70 citations

Proceedings ArticleDOI
12 Jul 2011
TL;DR: Six judgment aggregation rules are studied; two of them, based on distances, have been previously defined; the other four are new, and all inspired both by voting theory and knowledge representation and reasoning, and address some of their social choice theoretic properties.
Abstract: Many voting rules are based on some minimization principle. Likewise, in the field of logic-based knowledge representation and reasoning, many belief change or inconsistency handling operators also make use of minimization. Surprisingly, minimization has not played a major role in the field of judgment aggregation, in spite of its proximity to voting theory and logic-based knowledge representation and reasoning. Here we make a step in this direction and study six judgment aggregation rules; two of them, based on distances, have been previously defined; the other four are new, and all inspired both by voting theory and knowledge representation and reasoning. We study the inclusion relationships between these rules and address some of their social choice theoretic properties.

70 citations


Authors

Showing all 1819 results

NameH-indexPapersCitations
Pierre-Louis Lions9828357043
Laurent D. Cohen9441742709
Chris Bowler8728835399
Christian P. Robert7553536864
Albert Cohen7136819874
Gabriel Peyré6530316403
Kerrie Mengersen6573720058
Nader Masmoudi6224510507
Roland Glowinski6139320599
Jean-Michel Morel5930229134
Nizar Touzi5722411018
Jérôme Lang5727711332
William L. Megginson5516918087
Alain Bensoussan5541722704
Yves Meyer5312814604
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202317
202291
2021371
2020408
2019415
2018392