Advances in Info-Metrics: Information and Information Processing across Disciplines

ISBN : 9780190636685

Min Chen; J. Michael Dunn; Amos Golan; Aman Ullah
552 ページ
171 x 248 mm

Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In Advances in Info-Metrics, Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah bring together a group of thirty experts to expand the study of info-metrics across the sciences and demonstrate how to solve problems using this interdisciplinary framework. Building on the theoretical underpinnings of info-metrics, the volume sheds new light on statistical inference, information, and general problem solving. The book explores the basis of information-theoretic inference and its mathematical and philosophical foundations. It emphasizes the interrelationship between information and inference and includes explanations of model building, theory creation, estimation, prediction, and decision making. Each of the nineteen chapters provides the necessary tools for using the info-metrics framework to solve a problem. The collection covers recent developments in the field, as well as many new cross-disciplinary case studies and examples. Designed to be accessible for researchers, graduate students, and practitioners across disciplines, this book provides a clear, hands-on experience for readers interested in solving problems when presented with incomplete and imperfect information.


Part I. Information, Meaning and Value
1. Information and its Value J. Michael Dunn and Amos Golan
2. A Computational Theory of Meaning Pieter Adriaans
Part II. Information Theory and Behavior
3. Inferring the Logic of Collective Information Processors Bryan C. Daniels
4. Information Theoretic Perspective on Human Ability Hwan-sik Choi
5. Information Recovery Related to Adaptive Economic Behavior and Choice George Judge
Part III. Info-metrics and Theory Construction
6. Maximum Entropy: A Foundation for a Unified Theory of Ecology John Harte
7. Entropic Dynamics: Mechanics without Mechanism Ariel Caticha
Part IV. Info-metrics in Action I: Prediction and Forecasts
8. Towards Deciphering of Cancer Imbalances: Using Information Theoretic Surprisal Analysis for Understanding of Cancer Systems Nataly Kravchenko-Balasha
9. Forecasting Socio Economic Distributions on Small Area Spatial Domains for Count Data
Rosa Bernardini Papalia and Esteban Fernandez-Vazquez
10. Performance and Risk Aversion of Funds with Benchmarks: A Large Deviations Approach F. Douglas Foster and Michael Stutzer
11. Estimating Macroeconomic Uncertainty and Discord Using Info-Metrics Kajal Lahiri and Wuwei Wang
12. Reduced perplexity: A simplified perspective on assessing probabilistic forecasts Kenric P. Nelson
Part V. Info-metrics in Action II: Statistical and Econometrics Inference
13. Info-metric Methods for the Estimation of Models with Group-Specifc Moment Conditions Martyn Andrews, Alastair R. Hall, Rabeya Khatoony, and James Lincoln
14. Generalized Empirical Likelihood Based Kernel Estimation of Spatially Similar Densities Kuangyu Wen and Ximing Wu
15. Renyi Divergence and Monte Carlo Integration John Geweke and Garland Durham
Part VI. Info-metrics, Data Intelligence and Visualization
16. Cost-Benefit Analysis of Data Intelligence - Its Broader Interpretations Min Chen
17. The Role of Information Channel in Visual Computing Miquel Feixas and Mateu Sbert
Part VII. Info-metrics and Nonparametric Inference
18. Entropy-based Model Averaging Estimation of Nonparametric Models Yundong Tu
19. Information Theoretic Estimation of Econometric Functions Millie Yi Mao and Aman Ullah


Min Chen is the Professor of Scientific Visualization at Oxford University and a fellow of Pembroke College. He has co-authored over 200 publications, including his recent contributions in areas such as theory of visualization, video visualization, visual analytics, and perception and cognition in visualization. ; J. Michael Dunn is Oscar Ewing Professor Emeritus of Philosophy, Professor Emeritus of Informatics and Computer Science, at Indiana University, where he spent most of his career and was founding dean of the School of Informatics. He is an affiliate member of the Info-Metrics Institute at the American University. His research has focused on information based logics. ; Amos Golan is Professor of Economics and Director of 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. A leader in info-metrics, he is the author of Foundations of Info-Metrics: Information, Inference, and Incomplete Information. ; Aman Ullah is Distinguished Professor of Economics at the University of California, Riverside. The author of 10 books and more than 160 published articles, Professor Ullah has helped shape the field of econometrics and has pioneered the development and application of non-parametric and semi-parametric methods.