Suchergebnisse

Projecting losses associated with hurricanes is a complex and difficult undertaking that is wrought with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real‐time estimates grew from $2 to $3 billion in losses late on August 12 to a peak of $50 billion for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm hit the resort areas of Charlotte Harbor near Punta Gorda and then went on to Orlando in the central part of the state, with early poststorm estimates converging on a damage estimate in the $28 to $31 billion range. Comparable damage to central Florida had not been seen since Hurricane Donna in 1960. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has recognized the role of computer models in projecting losses from hurricanes. The FCHLPM established a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a Holland B parameter hurricane wind field model. Uncertainty analysis quantifies the expected percentage reduction in the uncertainty of wind speed and loss that is attributable to each of the input variables.

This book evaluates the Simulated International Processer (SIPER) model of the international political system, which was derived from the Inter-Nation Simulation model created at Northwestern University. Stuart Bremer makes a significant advance by programming all aspects of the basic theory so that it more completely and accurately reflects the complexity of international relations in the real world. After describing the SIPER model, the author sets forth the values of the variables and parameters used for the twenty-four runs, including those that remained the same throughout and those that were varied. He then undertakes to validate the model by determining the extent to which the simulated nations behave like real world nations and simulated international systems like real international systems. In conclusion, he assesses the model's strengths and weaknesses and points to possible future improvements.Originally published in 1977.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These paperback editions preserve the original texts of these important books while presenting them in durable paperback editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905

This book evaluates the Simulated International Processer (SIPER) model of the international political system, which was derived from the Inter-Nation Simulation model created at Northwestern University. Stuart Bremer makes a significant advance by programming all aspects of the basic theory so that it more completely and accurately reflects the complexity of international relations in the real world. After describing the SIPER model, the author sets forth the values of the variables and parameters used for the twenty-four runs, including those that remained the same throughout and those that were varied. He then undertakes to validate the model by determining the extent to which the simulated nations behave like real world nations and simulated international systems like real international systems. In conclusion, he assesses the model's strengths and weaknesses and points to possible future improvements. Originally published in 1977. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Many different techniques have been proposed for performing uncertainty and sensitivity analyses on computer models for complex processes. The objective of the present study is to investigate the applicability of three widely used techniques to three computer models having large uncertainties and varying degrees of complexity in order to highlight some of the problem areas that must be addressed in actual applications. The following approaches to uncertainty and sensitivity analysis are considered: (1) response surface methodology based on input determined from a fractional factorial design; (2) Latin hypercube sampling with and without regression analysis; and (3) differential analysis. These techniques are investigated with respect to (1) ease of implementation, (2) flexibility, (3) estimation of the cumulative distribution function of the output, and (4) adaptability to different methods of sensitivity analysis. With respect to these criteria, the technique using Latin hypercube sampling and regression analysis had the best overall performance. The models used in the investigation are well documented, thus making it possible for researchers to make comparisons of other techniques with the results in this study.

This paper explores the optimal number of unique jobs for an industrial enterprise by using computer simulations to reduce overall costs of creating and operating these jobs through staff remuneration optimization. The study utilizes the author's mathematical models of workers' behavior in a stimulating environment and optimization of the number of similar jobs used in industrial enterprises. The paper employs computational mathematics and computer modeling to analyze potential solutions for organizing unique workplaces. The study demonstrates that the estimated number of unique jobs can be reduced by increasing work shifts, hiring qualified workers, and offering incentive wage rates. The paper presents the results of model computational experiments on the optimal number of jobs for performing a given amount of work in two shifts under three scenarios: attracting workers with average market qualifications, attracting more qualified workers with wages under average market conditions, and attracting more qualified workers with wages at increased rates.