Our joint paper, with Lariosse Kouakou, Matthias Löwe, Philipp Ratz and Franck Vermet, entitled “Collaborative Insurance Sustainability and Network Structure” is now available on Arxiv,
The peer-to-peer (P2P) economy has been growing with the advent of the internet, with well known brands such as Uber or Airbnb being examples thereof. In the insurance sector the approach is still in its infancy, but some companies have started to explore P2P-based collaborative insurance products (eg. Lemonade in the U.S. or Inspeer in France). The actuarial literature only recently started to consider those risk sharing mechanisms, as in Denuit and Robert (2020) or Feng et al. (2021). In this paper, describe and analyse such a P2P product, with some reciprocal risk sharing contracts. Here, we consider the case where policyholders still have an insurance contract, but the first self-insurance layer, below the deductible, can be shared with friends. We study the impact of the shape of the network (through the distribution of degrees) on the risk reduction. We consider also some optimal setting of the reciprocal commitments, and discuss the introduction of contracts with friends of friends to mitigate some possible drawbacks of having people without enough connections to exchange risks.
La seconde partie de mon intervention sur la science des données, dans le cadre du cours INF7100 portera sur les statistiques, univariées et multivarirées. Le plan sera le suivant
- 201: De la Statistique aux Sciences de Données pdf video (14:24)
- 211: Fonctions Usuelles en Statistique (fonction de réparition, densité, histogramme) pdf video (28:37)
- 221: Indicateurs Statistique: Valeur Centrale (moyenne) pdf video (32:56)
- 222: Indicateurs Statistique: Dispersion (variance, inégalités) pdf video (22:21)
- 223: Indicateurs Statistique: Approximations (approximation normale) pdf video (18:42)
- 224: Indicateurs Statistique: Quantiles pdf video (24:54)
- 231: Inférence (statistique bayésienne) pdf video (39:33)
- 241: Tests Statistiques (1) (tests, significativité, p-value) pdf video (43:41)
- 242: Tests Statistiques (2) (erreurs) pdf video (16:51)
- 261: Statistiques Bivariées pdf video (25:16)
- 271: Statistiques Multivariées: Projections pdf video (29:06)
- 272: Statistiques Multivariées: Clusters pdf video (32:21)
- 281: Réseaux et Graphs pdf video (32:40)
- 291: Données Chronologiques pdf video (29:01)
With Alfred Galichon and Lucas Vernet, we recently uploaded a paper entitled optimal transport on large networks on arxiv.
This article presents a set of tools for the modeling of a spatial allocation problem in a large geographic market and gives examples of applications. In our settings, the market is described by a network that maps the cost of travel between each pair of adjacent locations. Two types of agents are located at the nodes of this network. The buyers choose the most competitive sellers depending on their prices and the cost to reach them. Their utility is assumed additive in both these quantities. Each seller, taking as given other sellers prices, sets her own price to have a demand equal to the one we observed. We give a linear programming formulation for the equilibrium conditions. After formally introducing our model we apply it on two examples: prices offered by petrol stations and quality of services provided by maternity wards (only the later is described here for privacy issues). These examples illustrate the applicability of our model to aggregate demand, rank prices and estimate cost structure over the network. We insist on the possibility of applications to large scale data sets using modern linear programming solvers such as Gurobi.
Demand for gas in gas stations in Britanny, and demand for maternity in France (with border correction)
In addition to this paper we released a R toolbox to implement our results and an online tutorial, optimalnetwork.github.io.
With Emmanuel Flachaire, we recently uploaded a short article, Extended Scale-Free Networks, on arxiv.
Recently, Broido & Clauset (2019) mentioned that (strict) Scale-Free networks were rare, in real life. This might be related to the statement of Stumpf, Wiuf & May (2005), that sub-networks of scale-free networks are not scale-free. In the later, those sub-networks are asymptotically scale-free, but one should not forget about second-order deviation (possibly also third order actually). In this article, we introduce a concept of extended scale-free network, inspired by the extended Pareto distribution, that actually is maybe more realistic to describe real network than the strict scale free property. This property is consistent with Stumpf, Wiuf & May (2005): sub-network of scale-free larger networks are not strictly scale-free, but extended scale-free.