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A path-breaking journey into the brain, showing how perception, thought, and action are products of "maps" etched into your gray matter--and how technology can use them to read your mind.
"A cultural and structural analysis of the NEA's dance funding from its inception through the early 2000s. Wilbur studies how people in power engineer and translate institutional norms of arts recognition within dance, performance, and arts policy disclosure"--
Posing a major challenge to economic orthodoxy, Imperfect Knowledge Economics asserts that exact models of purposeful human behavior are beyond the reach of economic analysis. Roman Frydman and Michael Goldberg argue that the longstanding empirical failures of conventional economic models stem from their futile efforts to make exact predictions about the consequences of rational, self-interested behavior. Such predictions, based on mechanistic models of human behavior, disregard the importance of individual creativity and unforeseeable sociopolitical change. Scientific though these explanations may appear, they usually fail to predict how markets behave. And, the authors contend, recent behavioral models of the market are no less mechanistic than their conventional counterparts: they aim to generate exact predictions of "irrational" human behavior. Frydman and Goldberg offer a long-overdue response to the shortcomings of conventional economic models. Drawing attention to the inherent limits of economists' knowledge, they introduce a new approach to economic analysis: Imperfect Knowledge Economics (IKE). IKE rejects exact quantitative predictions of individual decisions and market outcomes in favor of mathematical models that generate only qualitative predictions of economic change. Using the foreign exchange market as a testing ground for IKE, this book sheds new light on exchange-rate and risk-premium movements, which have confounded conventional models for decades. Offering a fresh way to think about markets and representing a potential turning point in economics, Imperfect Knowledge Economics will be essential reading for economists, policymakers, and professional investors.
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.
Originally published in 1976, this work attempted to establish the legitimacy of understanding economic behaviour in psychological terms. This revised edition stresses the fact that economic abundance does not necessarily lead to satisfaction, and includes new material on contemporary applications.
An authoritative guide to the new economics of our crisis-filled century. Published in collaboration with the Institute for New Economic Thinking. The 2008 financial crisis was a seismic event that laid bare how financial institutions’ instabilities can have devastating effects on societies and economies. COVID-19 brought similar financial devastation at the beginning of 2020 and once more massive interventions by central banks were needed to heed off the collapse of the financial system. All of which begs the question: why is our financial system so fragile and vulnerable that it needs government support so often? For a generation of economists who have risen to prominence since 2008, these events have defined not only how they view financial instability, but financial markets more broadly. Leveraged brings together these voices to take stock of what we have learned about the costs and causes of financial fragility and to offer a new canonical framework for understanding it. Their message: the origins of financial instability in modern economies run deeper than the technical debates around banking regulation, countercyclical capital buffers, or living wills for financial institutions. Leveraged offers a fundamentally new picture of how financial institutions and societies coexist, for better or worse. The essays here mark a new starting point for research in financial economics. As we muddle through the effects of a second financial crisis in this young century, Leveraged provides a road map and a research agenda for the future.
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.
The increasing creation, support, use and consumption of digital representation of information touches a wide breadth of economic activities. This digitization has transformed social interactions, facilitated entirely new industries and undermined others and reshaped the ability of people - consumers, job seekers, managers, government officials and citizens - to access and leverage information. This important book includes seminal papers addressing topics such as the causes and consequences of digitization, factors shaping the structure of products and services and creating an enormous range of new applications and how market participants make their choices over strategic organization, market conduct, and public policies. This authoritative collection, with an original introduction by the editors, will be an invaluable source of reference for students, academics and practitioners with an interest in the economics of digitisation and the digital economy.