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Fundação Getulio Vargas

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Representative Papers

Rationalizing Dynamic Choices

Mar 29, 2019Social Science Research Network

This paper studies how an observer determines whether a sequence of observable actions can be rationalized by a Bayesian agent with endogenous information acquisition and updating, under a known utility function. Method: The authors derive the first necessary and sufficient condition for dynamic rationalizability: behavior is irrationalizable if and only if a universally dominant deviation exists—a deviation that strictly improves expected utility across all possible information structures. This condition is information-structure-free, greatly enhancing testability. Contribution/Results: The framework extends to stochastic choice, enabling monotonic rationalization under risk aversion, empirical falsification of Bayesian models, feasibility characterization in dynamic information design, and partial identification of utility parameters. Its core innovation is a verifiable dominance-based rationalizability criterion, which reveals that stronger risk aversion weakens predictive power of behavior and permits preference identification without assuming any specific information structure.

13 citations2 influentialRead paper

Train-Free Segmentation in MRI with Cubical Persistent Homology

Jan 02, 2024arXiv.org

To address the scarcity of annotated data in MRI segmentation, this paper proposes a training-free, fully unsupervised topological segmentation framework. Methodologically, it leverages cubical persistent homology to extract topological features—such as connected components and voids—from MRI volumes; employs automated threshold selection and spatial localization of representative cycles; and integrates anatomical geometric priors (e.g., spheres, cylinders, circles) to achieve precise segmentation of target structures—including glioblastoma, myocardium, and fetal cortical plate. Its key innovation lies in the first use of spatial coordinates of representative cycles to directly guide segmentation, thereby ensuring interpretability, topological stability, and geometric adaptability. Evaluated across multiple clinical MRI tasks, the method matches state-of-the-art supervised approaches in performance while requiring no labeled data—significantly enhancing robustness and clinical trustworthiness.

2 citations1 influentialRead paper

Connected Incomplete Preferences

Aug 10, 2020

This paper investigates a novel class of incomplete preferences—“connected preferences”—whose maximal comparability domain is topologically connected. Method: For continuous preferences, we establish the first necessary and sufficient condition for connectedness and fully characterize the structure of the maximal comparability domain, proving it must be arc-connected. Our approach integrates order theory, topology (notably connectedness and arc-connectedness), and real analysis, leveraging preference representation techniques and continuity characterizations. Contribution/Results: We derive a continuity-based criterion for connected preferences, provide constructive sufficient conditions, deliver an exact structural characterization of the maximal comparability domain, and establish a mechanism ensuring its arc-connectedness. These results yield a new modeling framework for incomplete preferences that combines mathematical rigor with economic interpretability, systematically linking topological properties of the choice space to the structure of rational choice behavior.

1 citationsRead paper
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