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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.
The papers of Jacob Marschak which follow in these volumes are an extraordinary combination of original and fruitful departures in economic and social thought, superb clarity of exposition, and sensitivity to the values of earlier work and even competing traditions. They make us marvel alike at their variety, their quantity, and their quality. But they do not, even so, fully reflect Marschak's contributions to the development of social science. He has had an unusual influence as one who exercises leadership. In a formal, organizational sense, this role has been manifest in his capacity as Director of the Cowles Commission for Research in Economics, then at the University of Chicago, in that organization's most productive and influential period, and later in his central role in the Western Management Science Institute, at the University of California at Los Angeles. I can speak from first-hand knowledge about the first. His special capacities are, first, the recognition of promising new concepts and of promising young scholars, and, second, getting his colleagues to join him in developing the ideas and involving them fully in the necessary tasks. There was an unusual combination of strength and humility in his methods; a display of force in pushing the work along but a willingness, almost an insistence, on treating even the most junior associate as a fully equal colleague in intellectual develop ment, whose criticism of himself was to be encouraged. His leadership has been exercised in the absence of formal positions.
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.
To make the best decisions, you need the best information. However, because most issues in game theory are grey, nearly all recent research has been carried out using a simplified method that considers grey systems as white ones. This often results in a forecasting function that is far from satisfactory when applied to many real situations. Grey Ga
A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike
The papers of Jacob Marschak which follow in these volumes are an extraordinary combination of original and fruitful departures in economic and social thought, superb clarity of exposition, and sensitivity to the values of earlier work and even competing traditions. They make us marvel alike at their variety, their quantity, and their quality. But they do not, even so, fully reflect Marschak's contributions to the development of social science. He has had an unusual influence as one who exercises leadership. In a formal, organizational sense, this role has been manifest in his capacity as Director of the Cowles Commission for Research in Economics, then at the University of Chicago, in that organization's most productive and influential period, and later in his central role in the Western Management Science Institute, at the University of California at Los Angeles. I can speak from first-hand knowledge about the first. His special capacities are, first, the recognition of promising new concepts and of promising young scholars, and, second, getting his colleagues to join him in developing the ideas and involving them fully in the necessary tasks. There was an unusual combination of strength and humility in his methods; a display of force in pushing the work along but a willingness, almost an insistence, on treating even the most junior associate as a fully equal colleague in intellectual develop ment, whose criticism of himself was to be encouraged. His leadership has been exercised in the absence of formal positions.
This SPR Departmental Paper will provide policymakers with a framework for studying changes to national data policy frameworks.
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.
This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.