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Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated. In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem sets facilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.
List of contents
- Dedication
- Acknowledgements
- Chapter 1 - Introduction
- Chapter 2 - Rational Inference: A Constrained Optimization Framework
- Chapter 3 - The Metrics of Info-Metrics
- Chapter 4 - Entropy Maximization
- Chapter 5 - Inference in The Real World
- Chapter 6 - Advanced Inference in The Real World
- Chapter 7: Efficiency, Sufficiency, and Optimality
- Chapter 8 - Prior Information
- Chapter 9 - A Complete Info-Metrics Framework
- Chapter 10 - Modeling and Theories
- Chapter 11 - Causal Inference via Constraint Satisfaction
- Chapter 12 - Info-Metrics and Statistical Inference: Discrete Problems
- Chapter 13 - Info-Metrics and Statistical Inference: Continuous Problems
- Chapter 14 - New Applications Across Disciplines
- Epilogue
- Appendices
- List of Symbols
- References
- Index
About the author
Amos Golan is a professor of economics and directs the Info-Metrics Institute at American University. He is also an External Professor at the Santa Fe Institute and a Senior Associate at Pembroke College, Oxford. His research is primarily in the interdisciplinary field of info-metrics - the science and practice of information processing, modeling, inference, and problem solving with insufficient information. He has published in economics, econometrics, statistics, mathematics, physics and philosophy journals. His books include Maximum Entropy Econometrics: Robust Estimation with Limited Data (coauthored with Judge and Miller) and Information and Entropy Econometrics - A Review and Synthesis.
Summary
Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure.
Additional text
Each chapter comes with a set of carefully designed exercises and problems. I am also delighted to see a dedicated info-metrics website that provides codes and data for all examples in this book, interactive examples, and additional materials. I have no doubt that this book should be of interest to researchers in many fields of social and natural science and is an ideal choice for an introductory course in information theory.
