השוואת שיטות

	Probable Maximum Loss Estimation	Exposure Modeling (Disaster Risk)
תחום	Disaster Studies	Disaster Studies
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2005	2017
הוגה השיטה	Patricia Grossi & Howard Kunreuther; Kirsten Mitchell-Wallace et al.	Catalina Yepes-Estrada & Vitor Silva (GEM); GEM Foundation global exposure program
סוג	Tail (return-period) loss metric read from a loss exceedance distribution	Spatial-inventory construction pipeline for elements at risk
מקור מכונן	Grossi, P., & Kunreuther, H. (Eds.) (2005). Catastrophe Modeling: A New Approach to Managing Risk. Springer. ISBN: 9780387241050	Yepes-Estrada, C., Silva, V., Valcárcel, J., Acevedo, A. B., Tarque, N., Hube, M. A., Coronel, G., & Santa María, H. (2017). Modeling the Residential Building Inventory in South America for Seismic Risk Assessment. Earthquake Spectra, 33(1), 299-322.
כינויים	Probable Maximum Loss (PML), Return-Period Loss, Tail Loss Estimation, Catastrophe Value-at-Risk	Exposure Database Development, Asset Inventory Modeling, Building Exposure Model, Elements at Risk Mapping
קשורות	4	4
תקציר	Probable maximum loss (PML) estimation reads a tail loss, the loss associated with a chosen rare return period or exceedance probability, from the loss exceedance curve produced by a probabilistic risk or catastrophe model. Where average annual loss summarizes the mean of the loss distribution, PML characterizes its extreme: a 1-in-250-year PML is the loss level exceeded with one percent probability in a year (a 0.4 percent probability for 1-in-250). Patricia Grossi and Howard Kunreuther's 2005 volume sets out PML and the exceedance-probability curve as core catastrophe-model outputs, and Kirsten Mitchell-Wallace and colleagues' 2017 practitioner's guide details how the industry computes and uses PML, including the crucial distinction between occurrence and aggregate exceedance. PML is the metric that drives solvency capital, reinsurance purchase, risk appetite, and regulatory stress tests, because catastrophe risk is about surviving the rare bad year, not the average one. It is a percentile (value-at-risk) of the loss distribution and therefore inherits both the power and the fragility of tail estimation. Defining it precisely, return period, occurrence versus aggregate, and uncertainty, is essential to using it responsibly.	Exposure modeling builds the geolocated inventory of assets, people, and values that are at risk from a hazard, the elements-at-risk layer that, together with hazard and vulnerability, determines disaster loss. It answers what is where and worth how much: how many buildings of each construction type sit in each location, their replacement value, and the population that occupies them at different times of day. Catalina Yepes-Estrada, Vitor Silva, and colleagues' 2017 South America residential exposure model and Vitor Silva and colleagues' 2020 global seismic risk model exemplify the modern approach of synthesizing census statistics, building characteristics, and expert mapping into open, georeferenced databases. Because loss equals hazard acting on exposure through vulnerability, exposure accuracy often dominates the realism of a risk estimate. Exposure models feed catastrophe models, HAZUS-style loss estimation, and probabilistic risk metrics like average annual loss. Constructing them well, with consistent taxonomy, credible values, and validated counts, is foundational to all downstream disaster risk analysis.