Book Review:Management Decision Making Under Uncertainty: An Introduction to Probability and Statistical Decision Theory. T. R. Dyckman, S. Smidt, A. K. McAdams

1971 ◽  
Vol 44 (2) ◽  
pp. 232
Author(s):  
Robert L. Winkler
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Statistical Decision Theory ◽  
Decision Making Under Uncertainty ◽  
Statistical Decision

scholarly journals Decision making, movement planning and statistical decision theory

2008 ◽  
Vol 12 (8) ◽  
pp. 291-297 ◽  
Author(s):  
Julia Trommershäuser ◽  
Laurence T. Maloney ◽  
Michael S. Landy
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Movement Planning ◽  
Statistical Decision Theory ◽  
Statistical Decision

FROM LOCAL ASSESSMENTS TO GLOBAL RATIONALITY

1996 ◽  
Vol 05 (02n03) ◽  
pp. 315-331 ◽  
Author(s):  
LOVE EKENBERG ◽  
MATS DANIELSON ◽  
MAGNUS BOMAN
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Autonomous Agents ◽  
The Other ◽  
Statistical Decision Theory ◽  
Statistical Decision ◽  
Imprecise Information ◽  
Finite Set ◽  
Admissible Strategies ◽  
Range Of Values

We present a theory and a tool for the treatment of problems arising when a decision making agent faces a situation involving a choice between a finite set of strategies, having access to a finite set of autonomous agents reporting their opinions. Each of these agents may itself be a decision making agent, and the theory is independent of whether there is a specific coordinating agent or not. Any decision making agent is allowed to assign different credibilities to the statements made by the other autonomous agents. The theory admits the representation of vague and numerically imprecise information, and the evaluation results in a set of admissible strategies by using criteria conforming to classical statistical decision theory. The admissible strategies can be further investigated with respect to strength and also with respect to the range of values that makes them admissible.

scholarly journals Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

Econometrica ◽  
2021 ◽  
Vol 89 (6) ◽  
pp. 2827-2853
Author(s):  
Charles F. Manski
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Model Space ◽  
Point Estimation ◽  
Statistical Decision Theory ◽  
Point Estimate ◽  
Statistical Decision ◽  
Probabilistic Structure ◽  
States Of Nature ◽  
As If

Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of as‐if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as‐if optimization or any other model‐based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald.

scholarly journals Bayesian natural selection and the evolution of perceptual systems

2002 ◽  
Vol 357 (1420) ◽  
pp. 419-448 ◽  
Author(s):  
Wilson S. Geisler ◽  
Randy L. Diehl
Keyword(s):  
Natural Selection ◽  
Decision Theory ◽  
Ideal Observer ◽  
Statistical Decision Theory ◽  
Statistical Decision ◽  
Natural Stimuli ◽  
Ideal Observers ◽  
Formal Framework ◽  
Bayesian Statistical Decision Theory ◽  
Perceptual Systems

In recent years, there has been much interest in characterizing statistical properties of natural stimuli in order to better understand the design of perceptual systems. A fruitful approach has been to compare the processing of natural stimuli in real perceptual systems with that of ideal observers derived within the framework of Bayesian statistical decision theory. While this form of optimization theory has provided a deeper understanding of the information contained in natural stimuli as well as of the computational principles employed in perceptual systems, it does not directly consider the process of natural selection, which is ultimately responsible for design. Here we propose a formal framework for analysing how the statistics of natural stimuli and the process of natural selection interact to determine the design of perceptual systems. The framework consists of two complementary components. The first is a maximum fitness ideal observer, a standard Bayesian ideal observer with a utility function appropriate for natural selection. The second component is a formal version of natural selection based upon Bayesian statistical decision theory. Maximum fitness ideal observers and Bayesian natural selection are demonstrated in several examples. We suggest that the Bayesian approach is appropriate not only for the study of perceptual systems but also for the study of many other systems in biology.

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