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1. |
Record Nr. |
UNINA9910696456803321 |
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Autore |
Stanton Mark W |
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Titolo |
Reducing costs in the health care system [[electronic resource] ] : learning from what has been done / / author, Mark W. Stanton |
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Pubbl/distr/stampa |
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Rockville, Md. : , : Agency for Healthcare Research and Quality, , [2002] |
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Descrizione fisica |
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11 pages : digital, PDF file |
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Collana |
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Research in action ; ; issue #9 |
AHRQ pub. ; ; no. 02-0046 |
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Soggetti |
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Medical care, Cost of - United States |
Health insurance - United States - Costs |
Health Care Costs |
Health Expenditures |
Cost Control - methods |
Health Care Reform - economics |
Insurance, Health |
Managed Care Programs - economics |
United States |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from PDF caption (viewed Mar. 6, 2008). |
"September 2002." |
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Nota di bibliografia |
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Includes bibliographical references. |
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Sommario/riassunto |
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This report examines strategies intended to affect health care costs and investigates whether or not they save money. AHRQ funded research has found that some approaches (specific employer contribution methods, competition among health maintenance organizations (HMOs), and behavioral managed care) save money, and others (cost sharing, flexible spending accounts, and hospital mergers) have mixed results. A unique contribution of AHRQ's research is its focus on the dynamic or interactive effects of cost containment strategies; i.e., estimating the likely effects of efforts in one sector on the rest of the system. The report also reviews some of the new research efforts underway. |
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2. |
Record Nr. |
UNINA9910672436903321 |
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Titolo |
Artificial Intelligence, Learning and Computation in Economics and Finance / / edited by Ragupathy Venkatachalam |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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9783031152948 |
9783031152931 |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (331 pages) |
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Collana |
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Understanding Complex Systems, , 1860-0840 |
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Disciplina |
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Soggetti |
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Economics |
Mathematical physics |
Computer science |
Dynamics |
Nonlinear theories |
Computer engineering |
Computer networks |
Social sciences - Mathematics |
Theoretical, Mathematical and Computational Physics |
Computer Science |
Applied Dynamical Systems |
Computer Engineering and Networks |
Mathematics in Business, Economics and Finance |
Economia |
Processament de dades |
Intel·ligència artificial |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Perspectives from the Development of Agent-based Modelling in Economics and Finance -- Towards a General Model of Financial |
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Markets -- The U-Mart Futures Exchange Experiment and Her Institutional Design Historically Inherited -- A Bottom-Up Framework for Data-Driven Agent-Based Simulations -- Can News Networks and Topics Influence Assets Return and Volatility? -- Causal Inference and Agent-Based Models -- Finding the Human in Their Stories: Some Thoughts on Digital Humanities Tools -- Interdependence Overcomes the Limitations of Rational Theories of Collective Behavior: The Productivity of Patents by Nations -- Sand Castles and Financial Systems.-Estimation of Agent-Based Models via Approximate Bayesian Computation -- Unravelling Aspects of Decision Making Under Uncertainty -- Logic and Epistemology in Behavioral Economics -- Aggregate Investor Attention and Bitcoin Return: The Machine Learning Approach -- Information and Market Power: An Experimental Investigation into the Hayek Hypothesis -- Algorithmically Learning, Creatively and Intelligently to Play Games -- A Simonian Formalistic Perspective on Collaborative, Distributed Invention -- Modified Sraffan Schemes and Algorithmic Rational Agents. |
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Sommario/riassunto |
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This book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded. Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools. The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis. |
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