Management Decision Making Under Uncertainty: An Introduction to Probability and Statistical Decision Theory.

1971 ◽  
Vol 66 (335) ◽  
pp. 658
Author(s):  
Richard P. Bland ◽  
T. R. Dyckman ◽  
S. Smidt ◽  
A. K. McAdams
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Statistical Decision Theory ◽  
Management Decision ◽  
Decision Making Under Uncertainty ◽  
Statistical Decision ◽  
Management Decision Making

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

scholarly journals Simulation as Implementation of Digital Approach to Decision-Making under Uncertainty and Risk

2019 ◽  
Vol 62 ◽  
pp. 11004
Author(s):  
O.V. Tiutyk ◽  
M.E. Butakova
Keyword(s):  
Decision Making ◽  
Process Model ◽  
Relevant Literature ◽  
Information Support ◽  
Practical Significance ◽  
Primary Data ◽  
Management Decision ◽  
Decision Making Under Uncertainty ◽  
Modeling Tools ◽  
Management Decision Making

The paper articulates the problem of modeling management decision-making process in new digital reality: decision-making under uncertainty, volatile environment, huge amount of data to be accounted, objective analytical risk attitude. The proposed solution includes critical selection of risk and uncertainty management tools aimed at improvement quality of management decision-making information support in the sustainable development context (on the example of construction projects). The aim of research is the development of the new “digital” approach to the process of reducing uncertainty when decision making in highly risk projects, including the process model and toolkit. Methodology is based on logical analysis and synthesis, decomposition, qualitative analysis of the relevant literature and primary data (top management informal interviews, targeted sample), comparative and regression analysis, time series analysis, mathematical statistics and simulation modelling based on nine sets of design estimation paperwork and turnover-balance sheets. The contribution into the existing knowledge includes substantiating the correlation of the terms of the digital decision-making and simulation modeling tools in high-risk projects management under insufficient statistical data and their mutual interaction. Also, advisability of formalization of the decision-making method based on unprocessed design estimates is justified, and appropriate methods for sustainable decision-making information support are selected. The approbation demonstrated practical significance and economic effectiveness of developed approach; experiment was carried out on the base of «RSU – 6» LLC’s projects (Tchaikovsky, Perm region).

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.

Using Fire in Forest Management: Decision Making Under Uncertainty

1984 ◽  
Vol 14 (5) ◽  
pp. 8-19 ◽  
Author(s):  
David Cohan ◽  
Stephen M. Haas ◽  
David L. Radloff ◽  
Richard F. Yancik
Keyword(s):  
Decision Making ◽  
Forest Management ◽  
Management Decision ◽  
Decision Making Under Uncertainty ◽  
Management Decision Making
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