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This book explains and illustrates recent developments and advances
in decision-making and risk analysis. It demonstrates how
artificial intelligence (AI) and machine learning (ML) have not
only benefitted from classical decision analysis concepts such as
expected utility maximization but have also contributed to making
normative decision theory more useful by forcing it to confront
realistic complexities. These include skill acquisition, uncertain
and time-consuming implementation of intended actions, open-world
uncertainties about what might happen next and what consequences
actions can have, and learning to cope effectively with uncertain
and changing environments. The result is a more robust and
implementable technology for AI/ML-assisted decision-making. The
book is intended to inform a wide audience in related applied areas
and to provide a fun and stimulating resource for students,
researchers, and academics in data science and AI-ML, decision
analysis, and other closely linked academic fields. It will also
appeal to managers, analysts, decision-makers, and policymakers in
financial, health and safety, environmental, business, engineering,
and security risk management.
Causal analytics methods can revolutionize the use of data to make
effective decisions by revealing how different choices affect
probabilities of various outcomes. This book presents and
illustrates models, algorithms, principles, and software for
deriving causal models from data and for using them to optimize
decisions with uncertain outcomes. It discusses how to describe and
summarize situations; detect changes; evaluate effects of policies
or interventions; learn what works best under different conditions;
predict values of as-yet unobserved quantities from available data;
and identify the most likely explanations for observed outcomes,
including surprises and anomalies. The book resents practical
techniques for causal modeling and analytics that practitioners can
apply to improve understanding of how choices affect probabilities
of consequences and, based on this understanding, to recommend
choices that are more likely to accomplish their intended
objectives.The book begins with a survey of modern analytics
methods, focusing mainly on techniques useful for decision, risk,
and policy analysis. Chapter 2 introduces free in-browser software,
including the Causal Analytics Toolkit (CAT) software, to enable
readers to perform the analyses described and to apply modern
analytics methods easily to their own data sets. Chapters 3 through
11 show how to apply causal analytics and risk analytics to
practical risk analysis challenges, mainly related to public and
occupational health risks from pathogens in food or from pollutants
in air. Chapters 12 through 15 turn to broader questions of how to
improve risk management decision-making by individuals, groups,
organizations, institutions, and multi-generation societies with
different cultures and norms for cooperation. These chapters
examine organizational learning, community resilience, societal
risk management, and intergenerational collaboration and justice in
managing risks.
Causal analytics methods can revolutionize the use of data to make
effective decisions by revealing how different choices affect
probabilities of various outcomes. This book presents and
illustrates models, algorithms, principles, and software for
deriving causal models from data and for using them to optimize
decisions with uncertain outcomes. It discusses how to describe and
summarize situations; detect changes; evaluate effects of policies
or interventions; learn what works best under different conditions;
predict values of as-yet unobserved quantities from available data;
and identify the most likely explanations for observed outcomes,
including surprises and anomalies. The book resents practical
techniques for causal modeling and analytics that practitioners can
apply to improve understanding of how choices affect probabilities
of consequences and, based on this understanding, to recommend
choices that are more likely to accomplish their intended
objectives.The book begins with a survey of modern analytics
methods, focusing mainly on techniques useful for decision, risk,
and policy analysis. Chapter 2 introduces free in-browser software,
including the Causal Analytics Toolkit (CAT) software, to enable
readers to perform the analyses described and to apply modern
analytics methods easily to their own data sets. Chapters 3 through
11 show how to apply causal analytics and risk analytics to
practical risk analysis challenges, mainly related to public and
occupational health risks from pathogens in food or from pollutants
in air. Chapters 12 through 15 turn to broader questions of how to
improve risk management decision-making by individuals, groups,
organizations, institutions, and multi-generation societies with
different cultures and norms for cooperation. These chapters
examine organizational learning, community resilience, societal
risk management, and intergenerational collaboration and justice in
managing risks.
Improving Risk Analysis shows how to better assess and manage
uncertain risks when the consequences of alternative actions are in
doubt. The constructive methods of causal analysis and risk
modeling presented in this monograph will enable to better
understand uncertain risks and decide how to manage them. The book
is divided into three parts. Parts 1 shows how high-quality risk
analysis can improve the clarity and effectiveness of individual,
community, and enterprise decisions when the consequences of
different choices are uncertain. Part 2 discusses social decisions.
Part 3 illustrates these methods and models, showing how to apply
them to health effects of particulate air pollution. "Tony Cox's
new book addresses what risk analysts and policy makers most need
to know: How to find out what causes what, and how to quantify the
practical differences that changes in risk management practices
would make. The constructive methods in Improving Risk Analysis
will be invaluable in helping practitioners to deliver more useful
insights to inform high-stakes decisions and policy,in areas
ranging from disaster planning to counter-terrorism investments to
enterprise risk management to air pollution abatement policies.
Better risk management is possible and practicable; Improving Risk
Analysis explains how." Elisabeth Pate-Cornell, Stanford University
"Improving Risk Analysis offers crucial advice for moving
policy-relevant risk analyses towards more defensible,
causally-based methods. Tony Cox draws on his extensive experience
to offer sound advice and insights that will be invaluable to both
policy makers and analysts in strengthening the foundations for
important risk analyses. This much-needed book should be required
reading for policy makers and policy analysts confronting uncertain
risks and seeking more trustworthy risk analyses." Seth Guikema,
Johns Hopkins University "Tony Cox has been a trail blazer in
quantitative risk analysis, and his new book gives readers the
knowledge and tools needed to cut through the complexity and
advocacy inherent in risk analysis. Cox's careful exposition is
detailed and thorough, yet accessible to non-technical readers
interested in understanding uncertain risks and the outcomes
associated with different mitigation actions. Improving Risk
Analysis should be required reading for public officials
responsible for making policy decisions about how best to protect
public health and safety in an uncertain world." Susan E. Dudley,
George Washington University
Improving Risk Analysis shows how to better assess and manage
uncertain risks when the consequences of alternative actions are in
doubt. The constructive methods of causal analysis and risk
modeling presented in this monograph will enable to better
understand uncertain risks and decide how to manage them. The book
is divided into three parts. Parts 1 shows how high-quality risk
analysis can improve the clarity and effectiveness of individual,
community, and enterprise decisions when the consequences of
different choices are uncertain. Part 2 discusses social decisions.
Part 3 illustrates these methods and models, showing how to apply
them to health effects of particulate air pollution. "Tony Cox's
new book addresses what risk analysts and policy makers most need
to know: How to find out what causes what, and how to quantify the
practical differences that changes in risk management practices
would make. The constructive methods in Improving Risk Analysis
will be invaluable in helping practitioners to deliver more useful
insights to inform high-stakes decisions and policy,in areas
ranging from disaster planning to counter-terrorism investments to
enterprise risk management to air pollution abatement policies.
Better risk management is possible and practicable; Improving Risk
Analysis explains how." Elisabeth Pate-Cornell, Stanford University
"Improving Risk Analysis offers crucial advice for moving
policy-relevant risk analyses towards more defensible,
causally-based methods. Tony Cox draws on his extensive experience
to offer sound advice and insights that will be invaluable to both
policy makers and analysts in strengthening the foundations for
important risk analyses. This much-needed book should be required
reading for policy makers and policy analysts confronting uncertain
risks and seeking more trustworthy risk analyses." Seth Guikema,
Johns Hopkins University "Tony Cox has been a trail blazer in
quantitative risk analysis, and his new book gives readers the
knowledge and tools needed to cut through the complexity and
advocacy inherent in risk analysis. Cox's careful exposition is
detailed and thorough, yet accessible to non-technical readers
interested in understanding uncertain risks and the outcomes
associated with different mitigation actions. Improving Risk
Analysis should be required reading for public officials
responsible for making policy decisions about how best to protect
public health and safety in an uncertain world." Susan E. Dudley,
George Washington University
Risk Analysis: Foundations, Models, and Methods fully addresses the
questions of "What is health risk analysis?" and "How can its
potentialities be developed to be most valuable to public health
decision-makers and other health risk managers?" Risk analysis
provides methods and principles for answering these questions. It
is divided into methods for assessing, communicating, and managing
health risks. Risk assessment quantitatively estimates the health
risks to individuals and to groups from hazardous exposures and
from the decisions or activities that create them. It applies
specialized models and methods to quantify likely exposures and
their resulting health risks. Its goal is to produce information to
improve decisions. It does this by relating alternative decisions
to their probable consequences and by identifying those decisions
that make preferred outcomes more likely. Health risk assessment
draws on explicit engineering, biomathematical, and statistical
consequence models to describe or simulate the causal relations
between actions and their probable effects on health. Risk
communication characterizes and presents information about health
risks and uncertainties to decision-makers and stakeholders. Risk
management applies principles for choosing among alternative
decision alternatives or actions that affect exposure, health
risks, or their consequences.
In Risk Analysis of Complex and Uncertain Systems acknowledged
risk authority Tony Cox shows all risk practitioners how
Quantitative Risk Assessment (QRA) can be used to improve risk
management decisions and policies. It develops and illustrates QRA
methods for complex and uncertain biological, engineering, and
social systems - systems that have behaviors that are just too
complex to be modeled accurately in detail with high confidence -
and shows how they can be applied to applications including
assessing and managing risks from chemical carcinogens, antibiotic
resistance, mad cow disease, terrorist attacks, and accidental or
deliberate failures in telecommunications network infrastructure.
This book was written for a broad range of practitioners, including
decision risk analysts, operations researchers and management
scientists, quantitative policy analysts, economists, health and
safety risk assessors, engineers, and modelers.
This book grew out of an effort to salvage a potentially useful
idea for greatly simplifying traditional quantitative risk
assessments of the human health consequences of using antibiotics
in food animals. In 2001, the United States FDA's Center for
Veterinary Medicine (CVM) (FDA-CVM, 2001) published a risk
assessment model for potential adverse human health consequences of
using a certain class of antibiotics, fluoroquinolones, to treat
flocks of chickens with fatal respiratory disease caused by
infectious bacteria. CVM's concern was that fluoroquinolones are
also used in human medicine, raising the possibility that
fluoroquinolone-resistant strains of bacteria selected by use of
fluoroquinolones in chickens might infect humans and then prove
resistant to treatment with human medicines in the same class of
antibiotics, such as ciprofloxacin. As a foundation for its risk
assessment model, CVM proposed a dramatically simple approach that
skipped many of the steps in traditional risk assessment. The basic
idea was to assume that human health risks were directly
proportional to some suitably defined exposure metric. In symbols:
Risk = K x Exposure, where "Exposure" would be defined in terms of
a metric such as total production of chicken contaminated with
fluoroquinolone-resistant bacteria that might cause human
illnesses, and "Risk" would describe the expected number of cases
per year of human illness due to fluoroquinolone-resistant
bacterial infections caused by chicken and treated with
fluoroquinolones."
In Risk Analysis of Complex and Uncertain Systems acknowledged
risk authority Tony Cox shows all risk practitioners how
Quantitative Risk Assessment (QRA) can be used to improve risk
management decisions and policies. It develops and illustrates QRA
methods for complex and uncertain biological, engineering, and
social systems - systems that have behaviors that are just too
complex to be modeled accurately in detail with high confidence -
and shows how they can be applied to applications including
assessing and managing risks from chemical carcinogens, antibiotic
resistance, mad cow disease, terrorist attacks, and accidental or
deliberate failures in telecommunications network infrastructure.
This book was written for a broad range of practitioners, including
decision risk analysts, operations researchers and management
scientists, quantitative policy analysts, economists, health and
safety risk assessors, engineers, and modelers.
Risk Analysis: Foundations, Models, and Methods fully addresses the
questions of "What is health risk analysis?" and "How can its
potentialities be developed to be most valuable to public health
decision-makers and other health risk managers?" Risk analysis
provides methods and principles for answering these questions. It
is divided into methods for assessing, communicating, and managing
health risks. Risk assessment quantitatively estimates the health
risks to individuals and to groups from hazardous exposures and
from the decisions or activities that create them. It applies
specialized models and methods to quantify likely exposures and
their resulting health risks. Its goal is to produce information to
improve decisions. It does this by relating alternative decisions
to their probable consequences and by identifying those decisions
that make preferred outcomes more likely. Health risk assessment
draws on explicit engineering, biomathematical, and statistical
consequence models to describe or simulate the causal relations
between actions and their probable effects on health. Risk
communication characterizes and presents information about health
risks and uncertainties to decision-makers and stakeholders. Risk
management applies principles for choosing among alternative
decision alternatives or actions that affect exposure, health
risks, or their consequences.
This book highlights quantitative risk assessment and modeling
methods for assessing health risks caused by air pollution, as well
as characterizing and communicating remaining uncertainties. It
shows how to apply modern data science, artificial intelligence and
machine learning, causal analytics, mathematical modeling, and risk
analysis to better quantify human health risks caused by
environmental and occupational exposures to air pollutants. The
adverse health effects that are caused by air pollution, and
preventable by reducing it, instead of merely being statistically
associated with exposure to air pollution (and with other many
conditions, from cold weather to low income) have proved to be
difficult to quantify with high precision and confidence, largely
because correlation is not causation. This book shows how to use
recent advances in causal analytics and risk analysis to determine
more accurately how reducing exposures affects human health risks.
Quantitative Risk Analysis of Air Pollution Health Effects is
divided into three parts. Part I focuses mainly on quantitative
simulation modelling of biological responses to exposures and
resulting health risks. It considers occupational risks from
asbestos and crystalline silica as examples, showing how dynamic
simulation models can provide insights into more effective policies
for protecting worker health. Part II examines limitations of
regression models and the potential to instead apply machine
learning, causal analysis, and Bayesian network learning methods
for more accurate quantitative risk assessment, with applications
to occupational risks from inhalation exposures. Finally, Part III
examines applications to public health risks from air pollution,
especially fine particulate matter (PM2.5) air pollution. The book
applies freely available browser analytics software and data sets
that allow readers to download data and carry out many of the
analyses described, in addition to applying the techniques
discussed to their own data. http://cox-associates.com:8899/
This book highlights quantitative risk assessment and modeling
methods for assessing health risks caused by air pollution, as well
as characterizing and communicating remaining uncertainties. It
shows how to apply modern data science, artificial intelligence and
machine learning, causal analytics, mathematical modeling, and risk
analysis to better quantify human health risks caused by
environmental and occupational exposures to air pollutants. The
adverse health effects that are caused by air pollution, and
preventable by reducing it, instead of merely being statistically
associated with exposure to air pollution (and with other many
conditions, from cold weather to low income) have proved to be
difficult to quantify with high precision and confidence, largely
because correlation is not causation. This book shows how to use
recent advances in causal analytics and risk analysis to determine
more accurately how reducing exposures affects human health risks.
Quantitative Risk Analysis of Air Pollution Health Effects is
divided into three parts. Part I focuses mainly on quantitative
simulation modelling of biological responses to exposures and
resulting health risks. It considers occupational risks from
asbestos and crystalline silica as examples, showing how dynamic
simulation models can provide insights into more effective policies
for protecting worker health. Part II examines limitations of
regression models and the potential to instead apply machine
learning, causal analysis, and Bayesian network learning methods
for more accurate quantitative risk assessment, with applications
to occupational risks from inhalation exposures. Finally, Part III
examines applications to public health risks from air pollution,
especially fine particulate matter (PM2.5) air pollution. The book
applies freely available browser analytics software and data sets
that allow readers to download data and carry out many of the
analyses described, in addition to applying the techniques
discussed to their own data. http://cox-associates.com:8899/
This book grew out of an effort to salvage a potentially useful
idea for greatly simplifying traditional quantitative risk
assessments of the human health consequences of using antibiotics
in food animals. In 2001, the United States FDA's Center for
Veterinary Medicine (CVM) (FDA-CVM, 2001) published a risk
assessment model for potential adverse human health consequences of
using a certain class of antibiotics, fluoroquinolones, to treat
flocks of chickens with fatal respiratory disease caused by
infectious bacteria. CVM's concern was that fluoroquinolones are
also used in human medicine, raising the possibility that
fluoroquinolone-resistant strains of bacteria selected by use of
fluoroquinolones in chickens might infect humans and then prove
resistant to treatment with human medicines in the same class of
antibiotics, such as ciprofloxacin. As a foundation for its risk
assessment model, CVM proposed a dramatically simple approach that
skipped many of the steps in traditional risk assessment. The basic
idea was to assume that human health risks were directly
proportional to some suitably defined exposure metric. In symbols:
Risk = K x Exposure, where "Exposure" would be defined in terms of
a metric such as total production of chicken contaminated with
fluoroquinolone-resistant bacteria that might cause human
illnesses, and "Risk" would describe the expected number of cases
per year of human illness due to fluoroquinolone-resistant
bacterial infections caused by chicken and treated with
fluoroquinolones."
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