This book is a product of applying info-gap decision theory to policy formulation and evaluation in monetary economics and related domains. Info-gap theory has been applied to planning and decision problems in many areas, including engineering, biological conservation, project management, economics, medicine, homeland security, and more.

Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. Info-Gap Decision Theory is written for decision analysts. The term "decision analyst" covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts, usually without calling themselves by this name. In addition to engineers, decision analysts work in planning offices for public agencies, in project management consultancies, they are engaged in manufacturing process planning and control, in financial planning and economic analysis, in decision support for medical or technological diagnosis, and so on and on. Decision analysts provide quantitative support for the decision-making process in all areas where systematic decisions are made. This second edition entails changes of several sorts. First, info-gap theory has found application in several new areas - especially biological conservation, economic policy formulation, preparedness against terrorism, and medical decision-making. Pertinent new examples have been included. Second, the combination of info-gap analysis with probabilistic decision algorithms has found wide application. Consequently "hybrid" models of uncertainty, which were treated exclusively in a separate chapter in the previous edition, now appear throughout the book as well as in a separate chapter. Finally, info-gap explanations of robust-satisficing behavior, and especially the Ellsberg and Allais "paradoxes", are discussed in a new chapter together with a theorem indicating when robust-satisficing will have greater probability of success than direct optimizing with uncertain models. - New theory developed systematically - Many examples from diverse disciplines - Realistic representation of severe uncertainty - Multi-faceted approach to risk - Quantitative model-based decision theory

Why solving the information problem should be at the core of our pandemic response: essential reading about the long-term implications of our current crisis. COVID-19 is caused by a virus. The COVID-19 pandemic is caused by a lack of good information. A pandemic is essentially an information problem: this is the enlightening and provocative idea at the heart of this book. If we solve the information problem, argues economist Joshua Gans, we can defeat the virus. For example, when we don't know who is infected, we have to act as if everyone is infected. If we actively manage the information problem--if we know who is infected and with whom they had contact--we can suppress the virus or buy time for vaccine development. This is an expanded version of an eBook originally published as Economics in the Age of COVID-19.

The Common Agricultural Policy (CAP) has been supporting the incomes of the European Union's agricultural community for half a century. Despite this, there is still no official system in place to track the economic wellbeing of farmers and their families. This book examines the evidence on the overall wealth of farming households, and concludes that in nearly all member states, they are not generally a poor sector of society, with disposable incomes that are similar to, or exceed, the national average.

An analysis of divergent online news preferences of journalists and consumers and what this means for media and democracy in the digital age. The websites of major media organizations—CNN, USA Today, the Guardian, and others—provide the public with much of the online news they consume. But although a large proportion of the top stories these sites disseminate cover politics, international relations, and economics, users of these sites show a preference (as evidenced by the most viewed stories) for news about sports, crime, entertainment, and weather. In this book, Pablo Boczkowski and Eugenia Mitchelstein examine the divergence in preferences and consider its implications for the media industry and democratic life in the digital age. Drawing on analyses of more than 50,000 stories posted on twenty news sites in seven countries in North and South America and Western Europe, Boczkowski and Mitchelstein find that the gap in news preferences exists regardless of ideological orientation or national media culture, and that it is not affected by innovations in forms of storytelling, such as blogs and user-generated content on mainstream news sites. Drawing upon these findings, they explore the news gap's troubling consequences for the matrix that connects communication, technology, and politics in the digital age.

A guide to the pandemic economy: essential reading about the long-term implications of our current crisis. The COVID-19 pandemic has unleashed a firehose of information (much of it wrong) and an avalanche of opinions (many of them ill-founded). Most of us are so distracted by the everyday awfulness that we don't see the broader issues in play. In this book, economist Joshua Gans steps back from the short-term chaos to take a clear and systematic look at how economic choices are being made in response to COVID-19. He shows that containing the virus and pausing the economy—without letting businesses fail and people lose their jobs—are the necessary first steps.

This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.