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V. 1. An introduction to dynamical systems and market mechanisms -- v. 2. An introduction to macroeconomics dynamics.
The beauty of science may be pure and eternal, but the practice of science costs money. And scientists, being human, respond to incentives and costs, in money and glory. Choosing a research topic, deciding what papers to write and where to publish them, sticking with a familiar area or going into something new—the payoff may be tenure or a job at a highly ranked university or a prestigious award or a bump in salary. The risk may be not getting any of that. At a time when science is seen as an engine of economic growth, Paula Stephan brings a keen understanding of the ongoing cost-benefit calculations made by individuals and institutions as they compete for resources and reputation. She shows how universities offload risks by increasing the percentage of non-tenure-track faculty, requiring tenured faculty to pay salaries from outside grants, and staffing labs with foreign workers on temporary visas. With funding tight, investigators pursue safe projects rather than less fundable ones with uncertain but potentially path-breaking outcomes. Career prospects in science are increasingly dismal for the young because of ever-lengthening apprenticeships, scarcity of permanent academic positions, and the difficulty of getting funded. Vivid, thorough, and bold, How Economics Shapes Science highlights the growing gap between the haves and have-nots—especially the vast imbalance between the biomedical sciences and physics/engineering—and offers a persuasive vision of a more productive, more creative research system that would lead and benefit the world.
This book presents introductory economics material using standard mathematical tools, including calculus. It is designed for a relatively sophisticated undergraduate who has not taken a basic university course in economics. The book can easily serve as an intermediate microeconomics text. The focus of this book is on the conceptual tools. Contents: 1) What is Economics? 2) Supply and Demand. 3) The US Economy. 4) Producer Theory. 5) Consumer Theory. 6) Market Imperfections. 7) Strategic Behavior.
The essays in this volume were a challenge to me to write. I am an economist to the core, inclined to evaluate most observed behavior and public policies with conventional neoclassical theory. The essays represent my attempt to come to grips with the meaning and importance of what I try to do as a professional economist. They reflect my attempt to acquire a new and improved understanding of the usefulness and limitations of the writings of professional economists, especially my own. In this regard, although I hope others will find the thoughts useful, the volume represents a personal statement of how one economist views his and others' work. For that reason the discussion is often openly normative, tinged with the conviction that social discourse is more than costs and benefits and that economics cannot be fully evaluated by the methods - economic methods - that are the subject of the evaluation. These essays could not have been written without considerable encouragement and help from colleagues and friends. The following people are recognized for having read one or more chapters and for having contributed critical, substantive comments: Diana Bailey, Wilfred Beckerman, Geoffrey Brennan, William Briet, James Buchanan, Delores Martin, David Maxwell, Mary Ann McKenzie, Warren Samuels, Robert Staaf, Richard Wagner, Karen Vaughn, and Bruce Yandle. I am very much in their debt. However, they should not be held accountable for any of the positions taken and any errors that may remain.
This is a new release of the original 1962 edition.
The Nature and Method of Economic Sciences: Evidence, Causality, and Ends argues that economic phenomena can be examined from five analytical levels: a statistical descriptive approach, a causal explanatory approach, a teleological explicative approach, a normative approach and, finally, the level of application. The above viewpoints are undertaken by different but related economic sciences, including statistics and economic history, positive economics, normative economics, and the 'art of political economy'. Typically, positive economics has analysed economic phenomena using the second approach, causally explaining and often trying to predict the future evolution of the economy. It has not been concerned with the ends selected by the individual or society, taking them as given. However, various new economic currents have emerged during the last 40 years, and some of these do assign a fundamental role to ends within economics. This book argues that the field of positive economics should adapt to deal with the issues that arise from this. The text attempts to discern the nature of economic phenomena, introducing the different approaches and corresponding economic sciences. It goes on to analyse the epistemological characteristics of these in the subsequent chapters, as well as their disciplinary interrelations. This book is a valuable resource for students and scholars of the social sciences, philosophy, and the philosophy of economics. It will also be of interest to those researching political economy and the development of economic thought.
There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. "Economics of Digitization "identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. "Economics of Digitization" will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research.
Ever since the inception of economics over two hundred years ago, the tools at the discipline’s disposal have grown more and more more sophisticated. This book provides a historical introduction to the methodology of economics through the eyes of economists. The story begins with John Stuart Mill's seminal essay from 1836 on the definition and method of political economy, which is then followed by an examination of how the actual practices of economists changed over time to such an extent that they not only altered their methods of enquiry, but also their self-perception as economists. Beginning as intellectuals and journalists operating to a large extent in the public sphere, they then transformed into experts who developed their tools of research increasingly behind the scenes. No longer did they try to influence policy agendas through public discourse; rather they targeted policymakers directly and with instruments that showed them as independent and objective policy advisors, the tools of the trade changing all the while. In order to shed light on this evolution of economic methodology, this book takes carefully selected snapshots from the discipline’s history. It tracks the process of development through the nineteenth and twentieth centuries, analysing the growth of empirical and mathematical modelling. It also looks at the emergence of the experiment in economics, in addition to the similarities and differences between modelling and experimentation. This book will be relevant reading for students and academics in the fields of economic methodology, history of economics, and history and philosophy of the social sciences.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.