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Université d'Orléans

Academic institutioneurope · fr
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Research library45linked papers
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Selected work

Representative Papers

Size-varying reversible causal graph dynamics

May 25, 2018

Conventional wisdom holds that reversible graph dynamics must preserve the number of nodes, precluding node creation or deletion while maintaining reversibility. Method: This paper challenges this paradigm by introducing three mutually equivalent relaxed frameworks—grounded in reversible computation, extended cellular automata, and bijective graph rewriting—that jointly enforce global bijectivity and local causality while permitting reversible node creation and destruction. Contribution/Results: We formally prove the equivalence of these frameworks, thereby establishing the first causal graph dynamics model that is both size-variable and time-reversible. This work refutes the long-standing assumption that reversibility necessitates node conservation, offering a novel paradigm for discrete spacetime modeling. It bridges a critical gap between theoretical computer science—particularly models of reversible computation—and formal approaches to quantum gravity, where dynamical causal structure and background independence are essential.

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Generalized Impulse Responses of Portfolio Default Probabilities: A Modular Framework with an Application to Geopolitical Risk

Aug 05, 2026

This study addresses the lack of systematic analysis of the dynamic response of portfolio-level probability of default (PD) in existing credit stress-testing frameworks. The authors propose a modular framework that integrates Bayesian vector autoregression, Gaussian latent variable models, and the Merton–Vasicek structural credit model to derive, for the first time, analytical solutions for nonlinear generalized impulse responses of PD mean, quantiles (PD-at-Risk), and expected shortfall. This approach captures the joint influence of conditional mean and variance on PD dynamics, overcoming limitations of conventional interpolation methods that underestimate PD by 6–8% and neglect tail risk. Empirical results demonstrate that under geopolitical shocks, the 99th percentile PD response exceeds the mean response by 50%, with peak responses during credit cycles differing by up to a factor of 4.6.

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Regression with Observational Multilayered Network Data

Aug 02, 2026

This study addresses the bias in estimating social effects from non-experimental, multidimensional network data due to endogeneity, measurement error, and unobserved heterogeneity in linear mean regression models. To tackle these challenges, the paper proposes a closed-form generalized three-stage least squares (G3SLS) estimator that leverages a two-layer multiplex network structure. By exploiting exogenous network layers to identify endogenous social interactions, this approach introduces multiplex network structures into social effect identification for the first time, yielding an estimator that is consistent, asymptotically normal, and computationally tractable. Monte Carlo simulations demonstrate the superior finite-sample performance of G3SLS, while empirical analysis reveals a significant positive peer effect in citation behavior among top economics journals.

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Recent publications

Latest Papers

Generalized Impulse Responses of Portfolio Default Probabilities: A Modular Framework with an Application to Geopolitical Risk

Aug 05, 2026

This study addresses the lack of systematic analysis of the dynamic response of portfolio-level probability of default (PD) in existing credit stress-testing frameworks. The authors propose a modular framework that integrates Bayesian vector autoregression, Gaussian latent variable models, and the Merton–Vasicek structural credit model to derive, for the first time, analytical solutions for nonlinear generalized impulse responses of PD mean, quantiles (PD-at-Risk), and expected shortfall. This approach captures the joint influence of conditional mean and variance on PD dynamics, overcoming limitations of conventional interpolation methods that underestimate PD by 6–8% and neglect tail risk. Empirical results demonstrate that under geopolitical shocks, the 99th percentile PD response exceeds the mean response by 50%, with peak responses during credit cycles differing by up to a factor of 4.6.

0 citationsRead paper

Regression with Observational Multilayered Network Data

Aug 02, 2026

This study addresses the bias in estimating social effects from non-experimental, multidimensional network data due to endogeneity, measurement error, and unobserved heterogeneity in linear mean regression models. To tackle these challenges, the paper proposes a closed-form generalized three-stage least squares (G3SLS) estimator that leverages a two-layer multiplex network structure. By exploiting exogenous network layers to identify endogenous social interactions, this approach introduces multiplex network structures into social effect identification for the first time, yielding an estimator that is consistent, asymptotically normal, and computationally tractable. Monte Carlo simulations demonstrate the superior finite-sample performance of G3SLS, while empirical analysis reveals a significant positive peer effect in citation behavior among top economics journals.

0 citationsRead paper

Tempo: Reconstructing Synchronous Reactive Programming with OCaml 5 Effects

Jul 26, 2026

This work presents the first purely library-based implementation of core synchronous reactive programming mechanisms in standard OCaml 5, without requiring any language extensions. Building upon algebraic effects and deep effect handlers, we introduce the Tempo runtime, which delineates reactive suspension points via effect operations and reifies captured continuations into tasks scheduled according to logical instant semantics. This approach supports cooperative threads, broadcast signals, and dynamic process creation while fully reproducing the core semantics of ReactiveML. Through empirical evaluation, we quantify the runtime overhead incurred by this library-level implementation and identify the key mechanisms responsible for the dominant performance costs.

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