Université Paris-Panthéon-Assas

« Dans la sphère économique, un acte, une habitude, une institution, une loi n’engendrent pas seulement un effet, mais une série d’effets. De ces effets, le premier seul est immédiat ; il se manifeste simultanément avec sa cause, on le voit. Les autres ne se déroulent que successivement, on ne les voit pas, heureux si on les prévoit. Entre un mauvais et un bon Economiste, voici toute la différence : l’un s’en tient à l’effet visible, l’autre tient compte et de l’effet qu’on voit et de ceux qu’il faut prévoir. » Frédéric Bastiat (1850), Ce qu’on voit, ce qu’on ne voit pas . Abstract : Outre l’économiste érudit, le spécialiste de l’école autrichienne et de la théorie du Public Choice, Bertrand Lemennicier se passionnait pour la prévision en général et la prévision politique en particulier. Dans cet article en forme d’hommage, nous proposons de faire un point d’étape sur les méthodes de prévision électorale, en allant des méthodes non quantitatives aux méthodes quantitatives au sein desquelles les modèles de vote politico-économiques occupent une place éminente. En l’espèce, nous évoquerons la contribution de Bertrand Lemennicier à « l’art » de la prévision électorale.

This paper derives a feasible GLS estimator for a two-way error component model with serial correlation on both the time effects as well as the remainder disturbances. This estimator is based on two methods, one proposed by De Porres and Krishnaku mar(2013) for deriving the spectral decomposition of a general error component structure and the other based on an inversion trick for the variance-covariance matrix of this model suggested by Skoglund and Karlsson (2001). While the last paper used maximum likelihood methods under the normality assumption, we use method of moments estimators following Baltagi and Li (1991) for the one-way error component model with serially correlated remainder disturbances and its extension by Brou et al. (2011) for the two-way model with serially correlated time effects as well as remainder disturbances. Monte Carlo simulations are performed to compare the performance of these two estimators as well as a bias correction version based on Nobach (2023). Our results find that the method based on the (Skoglund and Karlsson 2001) inverse that is bias corrected a la (Nobach 2023) performs the best in root mean square error (RMSE) as well as mean absolute percentage error (MAPE) and is recommended.

Aluminum alloys are light and corrosion-resistant materials, which is why they are widely used in structures in many industrial fields (construction, automotive, electric cables). The article deals with the aluminum busduct structure. Therefore, the mechanical and especially electrical properties of busduct welds are the basic criteria for assessing the quality of welds. The aim of the work was to present the advantages of a process combining metal inert gas welding with immediate microjet cooling (MJC). The parameters of aluminum welding using the micro-jet method were estimated in order to obtain products with the desired strength, mechanical and electrical parameters. Information regarding the influence of various microjet parameters on the metallographic structure was also recorded. Then, the metallographic properties and some physical properties of the welding structures (mechanical resistance, electrical conductivity) were examined. In addition, computer simulations of the welding process with micro-jet cooling were performed. The heat affected zone in the welded material was determined. The proposed numerical method will allow the assessment of the parameters of the welding process with micro-jet cooling depending on the parameters of the materials undergoing the welding process. The numerical approach will significantly reduce costly and time-consuming in situ work. Planning the welding of large structures (such as busducts) will be more economical using the results of computer simulations.

Automated decision-making is increasingly prevalent, prompting discussions about AI replacing judges in court. This paper explores how machine-made sentencing decisions are perceived through an experimental study using a public good game with punishment. The study examines preferences for human versus automated punishers and the perceived fairness of penalties. Results indicate that rule violators prefer algorithmic punishment when penalty severity is uncertain and violations are significant. While human judges are typically reluctant to delegate, they are more likely to do this when they do not have discretion over the sanction level. Fairness perceptions are similar for both humans and algorithms, except when human judges choose a less severe penalty, which enhances perceived fairness.

This article investigates the finite-time (FT) boundedness problem for the time delay (TD) Takagi–Sugeno fuzzy model (TSFM) with conformable derivative (CD) and in the presence of certain actuator faults. Through the reconstruction of an appropriate Lyapunov–Krasovskii functional, some sufficient conditions expressed by the linear matrix inequalities (LMIs) are given to ensure the FT boundedness of the proposed model not only during regular operation but also when encountering certain actuator faults. Finally, a numerical example and an inverted pendulum system are presented to illustrate our theoretical results.

This paper focuses on solving the challenge of observer-based exponential control (O-BC) regarding conformable fuzzy polynomial models with time delay. In this work, polynomial matrices with unmeasurable states are considered to enhance the practicality of the model in the design problem. The proposed approach guarantees the existence of the polynomial controller and observer gains by satisfying sufficient conditions based on the sum-of-squares (S-O-S) approach. The stability conditions of the addressed system are solved using MATLAB SOSTOOLS, and the effectiveness of the proposed control scheme is evaluated through the presentation of simulation results from a numerical example and a mass-spring-damper system.

With around 6000 species and 200 genera worldwide, hoverflies (Syrphidae, Diptera) are important and a diverse group of pollinators, second to wild bees (Hymenoptera). Here, we studied the diversity of Syrphidae visiting flowers in low shrubland maquis environments of three compensation areas in the Ajaccio region (Corsica, France). A total of 138 hoverflies visiting flowers were sampled representing 27 species from 16 genera. The subfamily Syrphinae was the most diverse in comparison to Milesinae or Eristalinae. The syrphid communities were dominated at 67% by seven species ( Eumerus barbarus , Sphaerophoria scripta , Chrysotoxum intermedium , Episyrphus balteatus , Syritta pipiens , Melanostoma mellinum and Melanostoma scalare ). Most of data reported here are new for the Ajaccio region. Loretto stands out from the other two sites with both a greater species diversity and specimen abundance of hoverflies recorded visiting flowers. With regard to the daily activity, flower visits by syprhids occurred mainly during the morning at the three studied sites, and flowers of Asteraceae were the most visited. Finally, hoverflies showed a marked seasonality since most records of flower visits occurred in autumn (from September to November) when other floral visitors are rarer or absent.

During the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help addressing some of these limitations. This survey provides a detailed overview of the different blockchain AI-based optimization and improvement approaches, tools and methodologies proposed to meet the needs of existing systems and applications with their benefits and drawbacks. Afterwards, the focus is on suggesting AI-based directions where to address some of the fundamental limitations of blockchain.

The classic framework of Anscombe and Aumann (Ann Math Stat 34:199–205, 1963) for decision-making under uncertainty postulates both a primary source of uncertainty (the “horse race”) and an auxiliary randomization device (the “roulette wheel”). It also imposes a specific timing of resolution of uncertainty as the horse race takes place before the roulette is played. While this timing is without loss of generality for Subjective Expected Utility, it forbids plausible choice patterns of ambiguity aversion. In this paper, we reverse this timing by assuming that the roulette is played prior to the horse race and we obtain an axiomatic characterization of Choquet Expected Utility that is dual to that of Schmeidler (Econometrica 57(3):571–587, 1989). In this representation, ambiguity aversion is characterized by an aversion to conditioning roulette acts on horse events which, as we argue, is more plausible. Moreover, it can be larger than in Schmeidler’s model. Finally, our reversed timing yields incentive compatibility of the random incentive mechanisms, frequently used in experiments for eliciting ambiguity attitudes.

Conceptual clustering is a well-studied research area in the field of unsupervised machine learning. It aims to identify disjoint clusters, where each cluster represents a collection of similar transactions described by a common pattern. The first phase of earlier conceptual clustering methods relies on the enumeration of closed patterns. Nevertheless, the extraction of such patterns can be challenging, primarily due to their rigorous nature. Indeed, closed patterns can be not frequent or fail to cover all the transactions within a cluster. To overcome this issue, this paper presents a novel approach based on the relaxation of frequent patterns called k-relaxed frequent patterns. Then, we introduce a propositional satisfiability method for enumerating such patterns. Afterwards, we employ an integer linear programming approach to compute the set of disjoint clusters. Finally, we demonstrate the efficiency of our approach through an extensive experiments conducted on several popular real-life datasets.

Fifty-one years ago, in April 2023, the first Chipko protest saw Adivasis, or indigenous Indian women, embracing trees to prevent the logging of the green Himalayan forests. The Chipko Movement got its name from the Hindi word ‘chipko’ meaning ‘to hug’ or ‘to cling to’ when the indigenous communities of India exhibited their close ties of existence with nature. This grassroots environmental non-violent movement supports the preservation of human rights enumerated in the UDHR (1948) like the rights to freedom, social justice, economic equity, and access to social and international order, as well as the right to a clean and sustainable environment as in the UN Human Rights Council Resolution 48/13 (October 2021). Social worker Mahasweta Devi (1926–2016) lived with these Adivasi communities and documented their oral narratives. This paper unravels the call for the human right to eco-justice in her stories and novellas and show how Devi creates an imaginative community that can embrace characters, authors, and readers in their quest for the human right to a sustainable environment. Devi’s experiences with the Indigenous folks speaks to the dire need to reframe environmental justice. This paper explores how her fiction is not just an ethno-historical-fictional account but is one propelled by the narrative sleight of hand of a postcolonial social worker, writer, and human rights activist. In colonial times, these communities were perceived as ‘uncivilized’ with the colonial masters striving to bring ‘order’ over ‘chaos’. Their existence remains precarious with the present-day postcolonial government implementing dam-building projects in the name of greater common good. This paper considers how Devi’s eco-critical writing underscored the need to reframe environmental justice in postcolonial times by referring to David Schlosberg’s paradigm ‘distribution, recognition, and participation’, where all stakeholders should partake in the distribution of environmental harm, recognize and annihilate injury to voiceless actors who depend on the produce of the Earth for their daily sustenance, and participate in preserving and cherishing the Earth.

In this paper, we address an essential problem related to object detection and image processing: detecting objects potentially nested in other ones. This problem exists particularly in the beekeeping sector: detecting varroa parasites on bees. Indeed, beekeepers must ensure the level of infestation of their apiaries by the varroa parasite which settles on the backs of bees. As far as we know, there is no yet a published approach to deal with nested object detection using only one neural network trained on two different datasets. We propose an approach that fills this gap. Therefore, we improve the accuracy and the efficiency of bee and varroa detection task. Our work is based on deep learning, more precisely Mask R-CNN neural network. Instead of segmenting detected objects (bees), we segment internal objects (varroas). We add a branch to Faster R-CNN to segment internal objects. We extract relevant features for internal object segmentation and suggest efficient method for training the neural network on two different datasets. Our experiments are based on a set of images of bee frames, containing annotated bees and varroa mites. Due to differences in occurrence rates, two different sets were created. After carrying out experiments, we ended up with a single neural network capable of detecting two nested objects without decreasing accuracy compared to two separate neural networks. Our approach, compared to traditional separate neural networks, improves varroa detection accuracy by 1.9%, reduces infestation level prediction error by 0.22%, and reduces execution time by 28% and model memory by 23%. In our approach, we extract Res4 (a layer of the ResNet neural network) features for varroa segmentation, which improves detection accuracy by 11% compared to standard FPN extraction. Thus, we suggest a new approach that detects nested objects more accurately than two separate network approaches.

It is often argued that inequality may worsen coordination failures as it exacerbates conflicts of interests, making it difficult to achieve an efficient outcome. This paper shows that this needs not to be always the case. In a context in which two interacting populations have conflicting interests, we introduce ex-ante inequality, by making one population stronger than the other. This reduces the cost of miscoordination for the weakest population, and at the same time it makes some equilibria more equitable than others, thus more focal and attractive for inequality-averse players. Hence, both social preferences and strategic risk considerations may ease coordination. We provide experimental support for this hypothesis, by considering an extended two-population Hawk–Dove game, where ex-ante inequality, number of pure-strategy equilibria, and cost of coordination vary across treatments. We find that subjects coordinate more often on the efficient outcomes in the treatment with ex-ante inequality.

In this paper we present PARSEME-AR, the first openly available Arabic corpus manually annotated for Verbal Multiword Expressions (VMWEs). The annotation process is carried out based on guidelines put forward by PARSEME, a multilingual project for more than 26 languages. The corpus contains 4749 VMWEs in about 7500 sentences taken from the Prague Arabic Dependency Treebank. The results notably show a high degree of discontinuity in Arabic VMWEs in comparison to other languages in the PARSEME suite. We also propose analyses of interesting and challenging phenomena encountered during the annotation process. Moreover, we offer the first benchmark for the VMWE identification task in Arabic, by training two state-of-the-art systems, on our Arabic data.