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Robust Simulation for Mega-Risks = The Path from Single-Solution to Competitive, Multi-Solution Methods for Mega-Risk Management /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Robust Simulation for Mega-Risks/ by Craig E. Taylor.
Reminder of title:	The Path from Single-Solution to Competitive, Multi-Solution Methods for Mega-Risk Management /
Author:	Taylor, Craig E.
Description:	XXI, 164 p. 21 illus., 9 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Natural disasters. -
Online resource:	https://doi.org/10.1007/978-3-319-19413-4
ISBN:	9783319194134

Robust Simulation for Mega-Risks = The Path from Single-Solution to Competitive, Multi-Solution Methods for Mega-Risk Management /
Taylor, Craig E.

Robust Simulation for Mega-RisksThe Path from Single-Solution to Competitive, Multi-Solution Methods for Mega-Risk Management /[electronic resource] :by Craig E. Taylor. - 1st ed. 2015. - XXI, 164 p. 21 illus., 9 illus. in color.online resource.

Introduction: Initial Queries Going Forward -- The Deductivist Theory of Probability and Statistics -- The Frequency Theory of Probability -- Probability and Randomness as Beliefs: Bayesian Theory -- More Challenges to Tradition: Extreme Value Diagnostics, Power Laws, and the Wobble -- Mathematization of Statistics: Flexibility and Convergence -- Robust Simulation and Non-linear Reasoning: Quantitative and Qualitative Examples -- Managing Expectations: Qualitative Considerations And Quantitative Decision Procedures -- Conclusions and Queries.

This book introduces a new way of analyzing, measuring and thinking about mega-risks, a “paradigm shift” that moves from single-solutions to multiple competitive solutions and strategies. “Robust simulation” is a statistical approach that demonstrates future risk through simulation of a suite of possible answers. To arrive at this point, the book systematically walks through the historical statistical methods for evaluating risks. The first chapters deal with three theories of probability and statistics that have been dominant in the 20th century, along with key mathematical issues and dilemmas. The book then introduces “robust simulation” which solves the problem of measuring the stability of simulated losses, incorporates outliers, and simulates future risk through a suite of possible answers and stochastic modeling of unknown variables. This book discusses various analytical methods for utilizing divergent solutions in making pragmatic financial and risk-mitigation decisions. The book emphasizes the importance of flexibility and attempts to demonstrate that alternative credible approaches are helpful and required in understanding a great many phenomena. .

ISBN: 9783319194134

Standard No.: 10.1007/978-3-319-19413-4doiSubjects--Topical Terms:

555029
Natural disasters.

LC Class. No.: GB5000-5030

Dewey Class. No.: 551

Robust Simulation for Mega-Risks = The Path from Single-Solution to Competitive, Multi-Solution Methods for Mega-Risk Management /
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505 0 $a Introduction: Initial Queries Going Forward -- The Deductivist Theory of Probability and Statistics -- The Frequency Theory of Probability -- Probability and Randomness as Beliefs: Bayesian Theory -- More Challenges to Tradition: Extreme Value Diagnostics, Power Laws, and the Wobble -- Mathematization of Statistics: Flexibility and Convergence -- Robust Simulation and Non-linear Reasoning: Quantitative and Qualitative Examples -- Managing Expectations: Qualitative Considerations And Quantitative Decision Procedures -- Conclusions and Queries.
520 $a This book introduces a new way of analyzing, measuring and thinking about mega-risks, a “paradigm shift” that moves from single-solutions to multiple competitive solutions and strategies. “Robust simulation” is a statistical approach that demonstrates future risk through simulation of a suite of possible answers. To arrive at this point, the book systematically walks through the historical statistical methods for evaluating risks. The first chapters deal with three theories of probability and statistics that have been dominant in the 20th century, along with key mathematical issues and dilemmas. The book then introduces “robust simulation” which solves the problem of measuring the stability of simulated losses, incorporates outliers, and simulates future risk through a suite of possible answers and stochastic modeling of unknown variables. This book discusses various analytical methods for utilizing divergent solutions in making pragmatic financial and risk-mitigation decisions. The book emphasizes the importance of flexibility and attempts to demonstrate that alternative credible approaches are helpful and required in understanding a great many phenomena. .
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