The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted? Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics. Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.

All living things are remarkably complex, yet their DNA is unstable, undergoing countless random mutations over generations. Despite this instability, most animals do not grow two heads or die, plants continue to thrive, and bacteria continue to divide. Robustness and Evolvability in Living Systems tackles this perplexing paradox. The book explores why genetic changes do not cause organisms to fail catastrophically and how evolution shapes organisms' robustness. Andreas Wagner looks at this problem from the ground up, starting with the alphabet of DNA, the genetic code, RNA, and protein molecules, moving on to genetic networks and embryonic development, and working his way up to whole organisms. He then develops an evolutionary explanation for robustness. Wagner shows how evolution by natural selection preferentially finds and favors robust solutions to the problems organisms face in surviving and reproducing. Such robustness, he argues, also enhances the potential for future evolutionary innovation. Wagner also argues that robustness has less to do with organisms having plenty of spare parts (the redundancy theory that has been popular) and more to do with the reality that mutations can change organisms in ways that do not substantively affect their fitness. Unparalleled in its field, this book offers the most detailed analysis available of all facets of robustness within organisms. It will appeal not only to biologists but also to engineers interested in the design of robust systems and to social scientists concerned with robustness in human communities and populations.

This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.

This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.

The uncertainty that researchers face in specifying their estimation model threatens the validity of their inferences. In regression analyses of observational data, the 'true model' remains unknown, and researchers face a choice between plausible alternative specifications. Robustness testing allows researchers to explore the stability of their main estimates to plausible variations in model specifications. This highly accessible book presents the logic of robustness testing, provides an operational definition of robustness that can be applied in all quantitative research, and introduces readers to diverse types of robustness tests. Focusing on each dimension of model uncertainty in separate chapters, the authors provide a systematic overview of existing tests and develop many new ones. Whether it be uncertainty about the population or sample, measurement, the set of explanatory variables and their functional form, causal or temporal heterogeneity, or effect dynamics or spatial dependence, this book provides guidance and offers tests that researchers from across the social sciences can employ in their own research.

Mature sciences have been long been characterized in terms of the “successfulness”, “reliability” or “trustworthiness” of their theoretical, experimental or technical accomplishments. Today many philosophers of science talk of “robustness”, often without specifying in a precise way the meaning of this term. This lack of clarity is the cause of frequent misunderstandings, since all these notions, and that of robustness in particular, are connected to fundamental issues, which concern nothing less than the very nature of science and its specificity with respect to other human practices, the nature of rationality and of scientific progress; and science’s claim to be a truth-conducive activity. This book offers for the first time a comprehensive analysis of the problem of robustness, and in general, that of the reliability of science, based on several detailed case studies and on philosophical essays inspired by the so-called practical turn in philosophy of science.