Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters

The main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network – a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.Natural disaster losses, Insurability, Uncertainty, Multilayer perceptron neural network, Prediction.

Entrevue avec Sylvain Barthélémy et Laurent Bougrain

Les questions de cette interview sont posées à Sylvain Barthélémy et Laurent Bougrain par les organisateurs du colloque international « Les applications des réseaux de neurones en économie, finance, management et environnement », Chtourou Nouri, Feki Rochdi et Bazin Damien. Ce colloque, organisé par l’Ecole Supérieure de Commerce de Sfax, s’est déroulé en juin 2011 aux Iles Kerkennah, en Tunisie

La survenue des catastrophes naturelles : classification des variables explicatives par les réseaux de neurones

Durant les dernières décennies, l’occurrence des catastrophes naturelles a été fortement à la hausse. En effet, les catastrophes naturelles sont devenues de plus en plus fréquentes. En fait, ces risques dévastateurs ont touché durant les années précédentes différents pays dans des zones très diversifiées et continueront très probablement à être de réelles menaces dans le monde. Puisqu’aucun pays n’est à l’abri des catastrophes naturelles, il s’avère alors utile d’étudier les facteurs déterminants de leur survenue notamment avec la restriction de leurs périodes de retour et donc l’augmentation de leurs chances d’occurrence. Il nous a donc semblé opportun de tester les facteurs sous-jacents de la survenue des catastrophes naturelles. Notre travail se base sur l’application d’un réseau neuronal de type perceptron multicouche pour prédire le nombre des catastrophes naturelles à partir des variables les plus connues théoriquement. Ainsi, nous allons utiliser ce modèle neuronal pour effectuer l’analyse de sensitivité. Cette dernière permet de classer les variables explicatives selon l’importance de leur contribution dans la détermination du nombre de catastrophes naturelles comptabilisées durant la période d’étude. Les résultats obtenus ont montré que le réseau retenu peut prédire le nombre des catastrophes naturelles. De même, les différentes variables possèdent un effet considérable sur la sortie du réseau neuronal mais selon différents ordres d’importance. De ce fait, toutes ces variables contribuent à l’explication d’un problème aussi complexe comme la survenue des catastrophes naturelles

La place de l’Etat dans la représentation économique des systèmes concurrentiels

International audienc

Original scientific paper Rim Jemli Research Unit in Development Economy,

Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters Summary: The main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network- a technique rooted in artificial intelligence- insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country

Energy Analysis and Potentials of Biodiesel Production from Jatropha Curcas in Tunisia

International audienceOleaginous plants such as Jatropha Curcas Linnaeus (JCL), not intended for human consumption but used for biodiesel production, could contribute to beneficial outcomes. This plant grows on poor land (arid and marginal land) and is drought resistant. Jatropha Curcas is native to South America and widely grown in South and Central America, Africa, and Asia (Achten et al., 2007; Kumar et al., 2011). Some areas in Tunisia may be suitable for JCL, although not for food production. The purpose of this paper is to evaluate the energy performance and potential of Jatropha Curcas for biodiesel production in Tunisia. The evaluation will be completed through the elaboration of an energy balance using life cycle assessment (LCA), which will facilitate the decision making process. Therefore, the cultivation of JCL production in Tunisia will be an experiment with uncertain results. The energy assessment reveals a negative energy return (EE = 0.42). Because of high investment costs and low yield per hectare, Jatropha is not a sound economic choice

Le développement de l’assurance des catastrophes naturelles : facteur de développement économique

Dans le cadre de la société contemporaine ou encore « la société du risque », comme a été baptisée par Beck dans les années quatre-vingt, même la nature des risques a évolué. Certains risques revêtent un aspect dynamique et dont les conséquences sont à grande échelle. De ce fait, ces risques, dits majeurs, remettent en cause les conditions de l’assurabilité telles que définies par Berliner (1982). En effet, l’incertitude croissante qui nous entoure empreint l’assurance par de nouvelles lacunes : l’ignorance de l’occurrence des risques majeurs ainsi que la difficulté de l’évaluation de leurs gravités. Dans ce contexte, la gestion des catastrophes naturelles revêt une grande importance aussi bien pour le bien-être social que pour l’épanouissement du tissu économique et la croissance en général.De ce fait, cet article vise à tester la relation entre une bonne assurance des catastrophes naturelles et le développement économique de vingt-quatre pays de l’OCDE. La technique utilisée est la méthode de données de panel. On propose ainsi une analyse d’intégration-cointégration sur panel, en utilisant la méthode des moindres carrés ordinaires dynamiques (DOLS). Les résultats trouvés approuvent bien l’existence d’une relation positive entre le développement de l’assurance des catastrophes naturelles (mesuré par le taux de pénétration de l’assurance) et l’amélioration du PIB/tête des pays étudiés.In the contemporary society or the “society of risk” as was baptized by Beck in the eighties, the nature of risk is in perpetual evolution. Certain risks dress a dynamic aspect and the consequences of which are of large-scales. Therefore, these major risks differ significantly from Baruch Berliner’s earlier insurance conditions of 1982. Indeed, the increasing uncertainty which surrounds us imprints the insurance by new gaps: the ignorance of the occurrence of the major risks as well as the difficulty of the evaluation of theirs gravities. In this context, the risk management of natural disasters takes on a big importance as well for the social well-being as for the economic blooming and the growth generally.Therefore, this article aims at testing the relation between the improvement of the natural disasters insurance and economic development of twenty four countries of the OECD. The used technique is the method of data of panel. We propose an analysis of integration-cointegration on panel, by using the method of Dynamic Ordinary Least Squares (DOLS). The found results approve well the existence of a positive relation between the prosperity of the natural disasters insurance (measured by the rate of insurance’s penetration) and the development growth of our sample (measured by the GDP per person)

Risk management and policy implications for concentrating solar power technology investments in Tunisia

International audienceConcentrating solar power (CSP) is a promising technology in Tunisia. However, its diffusion is facing many barriers which deter investments. Through the analysis of a CSP plant in Southern Tunisia by using the Global Risk Analysis (GRA) method, we try to analyze the main risks faced by investors. The main objective of this research is to identify and analyze the risks faced by CSP investors in Tunisia and develop strategies that should be adopted to accelerate the process of diffusion of this technology. This analysis allows us to conclude that the CSP project is very exposed to political, financial, physical-chemical, legal, and strategic hazards. Moreover, we show that among the four phases of the project, the preparation phase is the most vulnerable to hazards. In fact, the GRA method makes it possible to determine the list of the major risks, such as the risk of not obtaining permission to build a CSP plant, the risk of non compliance with the deadline, the risk of failure to achieve the expected performance, the risk of insufficient access to capital, and the risk of conflicts with local residents. In order to de-risk CSP technology in Tunisia, we propose some strategies, such as strengthening the public-private partnerships, using participatory approaches, creating local employment, etc

Risk management and policy implications for concentrating solar power technology investments in Tunisia

International audienceConcentrating solar power (CSP) is a promising technology in Tunisia. However, its diffusion is facing many barriers which deter investments. Through the analysis of a CSP plant in Southern Tunisia by using the Global Risk Analysis (GRA) method, we try to analyze the main risks faced by investors. The main objective of this research is to identify and analyze the risks faced by CSP investors in Tunisia and develop strategies that should be adopted to accelerate the process of diffusion of this technology. This analysis allows us to conclude that the CSP project is very exposed to political, financial, physical-chemical, legal, and strategic hazards. Moreover, we show that among the four phases of the project, the preparation phase is the most vulnerable to hazards. In fact, the GRA method makes it possible to determine the list of the major risks, such as the risk of not obtaining permission to build a CSP plant, the risk of non compliance with the deadline, the risk of failure to achieve the expected performance, the risk of insufficient access to capital, and the risk of conflicts with local residents. In order to de-risk CSP technology in Tunisia, we propose some strategies, such as strengthening the public-private partnerships, using participatory approaches, creating local employment, etc