Douglas Miller

George G. Judge

Amos Golan

This monograph examines the problem of recovering and processing information when the underlying data are limited or partial, and the corresponding models that form the basis for estimation and inference are ill-posed or undermined

Robust Estimation with Limited Data

Maximum Entropy Econometrics

William Mooney

Danielle Wilson

Tinatin Mumladze

Elissa Cohen

Troy McGuinness

Jisung Moon

The possibility of reoccurring waves of the novel coronavirus that triggered the 2020 pandemic makes it critical to identify underlying policy-relevant factors that could be leveraged to decrease future COVID-19 death rates. We examined variation in a number of underlying, policy-relevant, country-level factors and COVID-19 death rates across countries. We found three such factors that significantly impact the survival probability of patients infected with COVID-19. In order of impact, these are universal TB (BCG) vaccination, air pollution deaths and a health-related expenditure. We quantify each probability change by age and sex. To deal with small sample size and high correlations, we use an information-theoretic inferential method that also allows us to introduce priors constructed from independent SARS data.

Effect of Universal TB Vaccination and Other Policy-Relevant Factors on the Probability of Patient Death from COVID-19

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.

Modeling, Inference, and Imperfect Information

Foundations of Info-Metrics

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 informeddecisions 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, andprogresses 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 informationaggregation, 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 setsfacilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.

Modeling and Inference with Imperfect Information

Jeffrey M. Perloff

Larry S. Karp

This book presents, compares, and develops various techniques for estimating market power - the ability to set price profitably above marginal cost - and strategies - the game-theoretic plans used by firms to compete with rivals. The authors start by examining static model approaches to estimating market power. They extend the analysis to dynamic models. Finally, they develop methods to estimate firms' strategies directly and examine how these strategies determine market power. A detailed technical appendix reviews the relevant information-theoretic and other econometric models that are used throughout. Questions and detailed answers for students and researchers are provided in the book for easy use.

Estimating Market Power and Strategies

Min Chen

J. Michael Dunn

Aman Ullah

"Info-metrics is a framework for rational inference on the basis of limited, or insufficient, information. It is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. Info-metrics has its roots in information theory (Shannon, 1948), Bernoulli's and Laplace's principle of insufficient reason (Bernoulli, 1713) and its offspring the principle of maximum entropy (Jaynes, 1957). It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. Within a constrained optimization setup, info-metrics provides a simple way for modeling and understanding all types of systems and problems. It is a framework for processing the available information with minimal reliance on assumptions and information that cannot be validated. Quite often a model cannot be validated with finite data. Examples include biological, social and behavioral models, as well as models of cognition and knowledge. The info-metrics framework extends naturally for tackling these types of common problems"--

Amos Golan

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