This paper introduces innovative enhancements to the K-means and K-nearest neighbors (KNN) algorithms based on the concept of Gini prametric spaces. Unlike traditional distance metrics, Gini-based measures incorporate both value-based and rank-based information, improving robustness to noise and outliers. The main contributions of this work include: proposing a Gini-based measure that captures both rank information and value distances; presenting a Gini K-means algorithm that is proven to converge and demonstrates resilience to noisy data; and introducing a Gini KNN method that performs competitively with state-of-the-art approaches such as Hassanat’s distance in noisy environments. Experimental evaluations on 14 datasets from the UCI repository demonstrate the superior performance and efficiency of Gini-based algorithms in clustering and classification tasks. This work opens new avenues for leveraging rank-based measures in machine learning and statistical analysis.
Collusion in market pricing is a concept associated with human actions to raise market prices through artificially limited supply. Recently, the idea of algorithmic collusion was put forward, where the human action in the pricing process is replaced by automated agents. Although experiments have shown that collusive market equilibria can be reached through such techniques, without the need for human intervention, many of the techniques developed remain susceptible to exploitation by other players, making them difficult to implement in practice. In this article, we explore a situation where an agent has a multi-objective strategy, and not only learns to unilaterally exploit market dynamics originating from other algorithmic agents, but also learns to model the behaviour of other agents directly. Our results show how common critiques about the viability of algorithmic collusion in real-life settings can be overcome through the usage of slightly more complex algorithms.
Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate setting, these methods are often opaque and difficult to interpret. To address this, alternative methodologies have been proposed, using causal graphs combined with iterative quantile regressions (Plečko and Meinshausen (2020)) or sequential transport Fernandes Machado et al. (2025)) to examine fairness at the individual level, often referred to as “counterfactual fairness.” Despite these advancements, transporting categorical variables remains a significant challenge in practical applications with real datasets.
In this paper, we propose a novel approach to address this issue. Our method involves (1) converting categorical variables into compositional data and (2) transporting these compositions within the probabilistic simplex of \mathbb{R}^d. We demonstrate the applicability and effectiveness of this approach through an illustration on real-world data, and discuss limitations.
C’était le titre initial de l’article qu’on avait écrit avec Laurence Barry (co-titulaire de la chaire de recherche PARI – programme de recherche sur l’appréhension des risques et des incertitudes – placée sous l’égide de l’Institut Louis Bachelier en partenariat avec l’ENSAE/CREST et Sciences Po), la semaine dernière et qui a été publié sur le site du quotidien Le Monde,
The digital revolution has profoundly transformed market dynamics, particularly within the insurance sector. This transformation encompasses the infrastructure and technologies that facilitate information exchange, the emergence of new business practices, a deluge of data, and the rise of innovative players capitalizing on these changes to deliver unique value propositions to customers. Traditional insurance companies face significant challenges and opportunities as they navigate competition from established Big Tech firms and agile insurtech startups. This study examines the disruptive nature of digital advancements, compelling historical players to confront the innovator’s dilemma (Christensen, 1997): whether to adapt and develop established practices or invest in new strategies to leverage digital opportunities. In doing so, they also come up against smaller, more agile start-ups. We highlight the necessity for insurance actors to rethink their roles in light of new market entrants and the evolving landscape shaped by Big Tech’s data monetization strategies. To analyze these dynamics, we propose an original framework in the form of a triangle of possibilities, which positions various market players and elucidates their strategic movements, innovations, and possible partnerships. This framework also aids in identifying competitive advantages and development trajectories, ultimately offering scenarios for the evolution of traditional insurance players in a digital and data-driven era.
In many jurisdictions, insurance companies are prohibited from discriminating based on certain policyholder characteristics. Exclusion of prohibited variables from models prevents direct discrimination, but fails to address proxy discrimination, a phenomenon especially prevalent when powerful predictive algorithms are fed with an abundance of acceptable covariates. The lack of formal definition for key fairness concepts, in particular indirect discrimination, hinders effective fairness assessment. We review causal inference notions and introduce a causal graph tailored for fairness in insurance. Exploiting these, we discuss potential sources of bias, formally define direct and indirect discrimination, and study the theoretical properties of fairness methodologies. A novel categorization of fair methodologies into five families (best-estimate, unaware, aware, hyperaware, and corrective) is constructed based on their expected fairness properties. A comprehensive pedagogical example illustrates the implications of our findings: the interplay between our fair score families, group fairness criteria, and discrimination.
Tout nouveau tout chaud, le dernier numéro de l’Actuariel est paru, avec en particulier un article Open finance : Big bang annoncé dans l’assurance pour lequel j’avais eu long entretien. Au delà de quelques idées qu’on retrouve ici ou là, je peux mentionner une petite phrase, qui semble avoir retenu l’attention…
« Le débat est posé de manière sournoise en faisant croire aux citoyens qu’ils bénéficieront de produits plus personnalisés, sans rappeler que l’assurance est bien souvent un jeu à somme nulle et que si certains paient moins cher, cela signifie que d’autres paient plus » indique Arthur Charpentier, professeur à l’Université du Québec à Montréal et actuaire agrégé.
"sendo l'intento mio scrivere cosa utile a chi la intende…"