Robust Simulation For Mega-risks: The Path From Single-solution To Competitive, Multi-solution Methods For Mega-risk Management
by Craig E. Taylor /
2015 / English / PDF
3.2 MB Download
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.
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.