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140122 ||| eng |
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|a 9783642877209
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| 100 |
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|a Sengupta, J.K.
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| 245 |
0 |
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|a Optimal Decisions under Uncertainty
|h Elektronische Ressource
|c by J.K. Sengupta
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| 250 |
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|a 1st ed. 1981
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 1981, 1981
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| 300 |
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|a VII, 161 p
|b online resource
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| 505 |
0 |
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|a 1. Optimal Decisions: Theory and Practice -- 2. Linear Programming Under Uncertainty -- 2.1 Introduction -- 2.2 Mixed Strategy Solutions -- 2.3 Informational Efficiency -- 2.4 Econometric Tests -- 3. Risk Aversion in Decision Models -- 3.1 Introduction -- 3.2 Risk Aversion in Economic Models -- 3.3 Applications in Other Models -- 3.4 Selected Empirical Applications -- 4 Linear Allocation Rules Under Uncertainty -- 4.1 Introduction -- 4.2 Comparative Analysis of Allocation Rules -- 4.3 Estimation and Regulation by Allocation -- 4.4 Team Decisions as Games -- 4.5 Allocation Under Imperfect Competition -- 5. Economic Planning Under Uncertainty -- 5.1 Introduction -- 5.2 Input-Output Model Under Risk Aversion -- 5.3 Output Planning Under Imperfect Competition -- 5.4 Stabilization Policy Under Constraints -- 6. Stochastic Programs as Nonzero Sum Games -- 6.1 Introduction -- 6.2 Games with Unknown Parameters -- 6.3 Constrained Statistical Games -- 7. Research Trends and Problems
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| 653 |
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|a Operations research
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| 653 |
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|a Quantitative Economics
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| 653 |
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|a Econometrics
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| 653 |
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|a Operations Research and Decision Theory
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
0 |
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|a Lecture Notes in Economics and Mathematical Systems
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| 028 |
5 |
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|a 10.1007/978-3-642-87720-9
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-642-87720-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 658.403
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| 520 |
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|a The theory of optimal decisions in a stochastic environment has seen many new developments in recent years. The implications of such theory for empirical and policy applications are several. This book attempts to analyze some of the impor tant applied aspects of this theory and its recent developments. The stochastic environment is considered here in specific form, e.g., (a) linear programs (LP) with parameters subject to a probabilistic mechanism, (b) decision models with risk aversion, (c) resource allocation in a team, and (d) national economic planning. The book attempts to provide new research insights into several areas, e.g., (a) mixed strategy solutions and econometric tests of hypotheses of LP models, (b) the dual problems of efficient estimation and optimal regulation, (c) input-output planning under imperfect competition, and (d) linear programs viewed as constrained statistical games. Methods of optimal decision rules developed here for quadratic and linear decision problems are applicable in three broad areas: (a) applied economic models in resource allocation, planning and team decision, (b) operations research models in management decisions involving portfolio analysis and stochastic programming, and (c) systems science models in stochastic control and adaptive behavior. Some results reported here have been published in professional journals be-. fore, and I would like to thank the following journals in particular: Inter national Journal of Systems Science, Journal of Optimization Theory and Applica tions and Journal of Mathematical Analysis and Applications
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