Causal Analytics For Applied Risk Analysis
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Causal Analytics for Applied Risk Analysis
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.
AI-ML for Decision and Risk Analysis
Author: Louis Anthony Cox Jr.
language: en
Publisher: Springer Nature
Release Date: 2023-07-05
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.
The Science of Risk Analysis
This book provides a comprehensive demonstration of risk analysis as a distinct science covering risk understanding, assessment, perception, communication, management, governance and policy. It presents and discusses the key pillars of this science, and provides guidance on how to conduct high-quality risk analysis. The Science of Risk Analysis seeks to strengthen risk analysis as a field and science by summarizing and extending current work on the topic. It presents the foundation for a distinct risk field and science based on recent research, and explains the difference between applied risk analysis (to provide risk knowledge and tackle risk problems in relation to for example medicine, engineering, business or climate change) and generic risk analysis (on concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterise, communicate, manage and govern risk). The book clarifies and describes key risk science concepts, and builds on recent foundational work conducted by the Society for Risk Analysis in order to provide new perspectives on science and risk analysis. The topics covered are accompanied by cases and examples relating to current issues throughout. This book is essential reading for risk analysis professionals, scientists, students and practitioners, and will also be of interest to scientists and practitioners from other fields who apply risk analysis in their work.