Akcije

Panoeconomicus
kako citirati ovaj članak
podeli ovaj članak

Metrika

  • citati u SCIndeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[]
  • posete u poslednjih 30 dana:1
  • preuzimanja u poslednjih 30 dana:0

Sadržaj

članak: 3 od 10  
Back povratak na rezultate
2010, vol. 57, br. 1, str. 43-60
Insurability challenges under uncertainty: An attempt to use the artificial neural network for the prediction of losses from natural disasters
(naslov ne postoji na srpskom)
aResearch Unit in Development Economy, Faculty of Economics Sciences and Management, University of Sfax, Tunisia
bHigher School of Commerce, University of Sfax, Tunisia

e-adresarim_jemli@yahoo.fr, nouri.chtourou@fseg.rnu.tn, Rochdi.Feki@escs.rnu.tn
Ključne reči: natural disaster losses; insurability; uncertainty; multilayer perceptron neural network; prediction
Sažetak
(ne postoji na srpskom)
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.
Reference
Aitkenhead, M.J., Parivash, L., Miller, D.R. (2007) Remote Sensing-based Neural Network Mapping of Tsunami Damage in Aceh, Indonesia. Disasters, 31(3), str. 217-226
Akerlof, G.A. (1970) The market for 'lemons': Quality uncertainty and the market mechanism. Q J Econ, vol. 84, br. 3, str. 488-500
Arrow, K.J. (1953) Le role des valeurs boursieres pour la repartition la meilleure des risques. Econometrie, Colloques Internationaux du Centre National de la Recherche Scientifique, Paris, Vol. XI, 41-7
Berliner, B. (1982) Limits of Insurability of risks. Englewood Cliffs: Prentice Hall Professional Technical Reference
Brockett, P.L., Cooper, W.W., Golden, L.L., Pitaktong, U. (1994) A neural network method for obtaining an early warning of insurer insolvency. Journal of Risk and Insurance, 61(3), 402
Chemarin, S., Claude, H. (2005) Vers une Theorie Economique de lAssurabilite en Incertitude. Paris: Chaire Developpement Durable, Cahier n° 2005- 005
Cummins, J.D., Doherty, N.A., Anita, Lo. (1999) Can insurers pay for the big one? Measuring capacity of an insurance market to respond to catastrophic losses. University of Pennsylvania, Wharton School Center for Financial Institutions, 98-11, Working Paper
Cybenko, G.V. (1989) Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2(4): 303-314
Dreyfus, G. (1997) Les Reseaux de Neurones? Une Technique Operationnelle pour le Traitement des Données Industrielles, Economiques et Financieres. Mesures, 699, nov
Guha-Sapir, D., Tschoegl, L., Below, R. (2006) An analytical review of selected data sets on natural disasters and impacts. u: UNDP/CRED Workshop on improving compilation of reliable data on disaster occurrence and impact, 2-4 April 2006, Bangkok, Thailand
Guha-Sapir, D. (2007) International workshop on information platforms for disaster reduction. u: IPDR Workshop, Asian science and technology forum Tsukuba, October 2007, Tsukuba, Japan, str. 3-4
Insurance Service Office (1994) The impact of catastrophes on property insurance. http://www.iso.com/Research-and-Analyses/Studies-and-Whitepapers/The-Impact-of- Catastrophes-on-Property-Insurance.html
Keynes, J.M. (1921) A treatise on probability. London - New York: Macmillan Publishing
Knight, F.H. (1921) Risk, uncertainty and profit. Boston: Houghton Mifflin
Kunreuther, H.C. (1996) Mitigating losses and providing protection against catastrophic risks: The role of insurance and other policy instruments. u: Paper presented at the European Society for Risk Analysis, Guilford, England
Kunreuther, H.C., Erwann, M.K.O. (2007) Climate change, insurability of large-scale disasters and the emerging liability challenge. National Bureau of Economic Research, Working Paper 12821
le Bret, C. (1997) Connaissez-vous le Data Mining. Science Tribune, Oct., www.tribunes.com/ tribune/art97/lebf.htm
Makki, S.S., Agapi, S. (2001) Farmers' participation in crop Insurance markets: Creating the right incentives. American Journal of Agricultural Economics, 83(3): 662-667
Minsky, M., Seymour, P. (1969) Perceptron: An introduction to computational geometry. Cambridge: MIT Press
Rosenblatt, F. (1962) Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington: Spartan Books
Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986) Learning representation by back-propagating errors. Nature, vol. 323, 533-536
Swiss, Re. (2005) Innover pour assurer iInassurable. u: Sigma n° 4/2005, Sep. 1st, Zurich: Swiss Company of Reinsurance, Economic Research & Consulting, 1-44
Viscusi, K.W., Born, P.W. (2006) The catastrophic effects of natural disasters on insurance markets. National Bureau of Economic, Research Working Paper 12348
 

O članku

jezik rada: engleski
vrsta rada: izvorni naučni članak
DOI: 10.2298/PAN1001043J
objavljen u SCIndeksu: 07.04.2010.