Bayesian Analysis of Overprediction of Insanity

1974 ◽  
Vol 34 (1) ◽  
pp. 207-214
Author(s):  
R. E. Peterson ◽  
K. K. Seo
Keyword(s):  
Decision Making ◽  
Bayesian Analysis ◽  
Loss Function ◽  
Violent Behavior ◽  
Decision Making Under Uncertainty ◽  
Natural Consequence ◽  
Rational Person ◽  
Personal Risk ◽  
States Of Nature ◽  
As If

Decision-making under uncertainty is visualized as a two-action game against nature. The psychiatrist is the player and has two actions from which to choose: predict violent behavior or predict sanity. The two states of nature are (i) the accused is in fact guilty and (ii) the accused is in fan innocent. The psychiatrist acts as if he evaluates a loss function which is such that overprediction of violent behavior is the natural consequence of a rational person who wishes to minimize his personal risk. Society's loss function, however, differs from the psychiatrist's loss function to such an extent that a rational society would want to underpredict violent behavior in order to minimize the risk of false confinements. It is suggested that the player of this game (the psychiatrist) has been ill-advisedly chosen.

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 STATES OF NATURE AND THE NATURE OF STATES

2016 ◽  
Vol 33 (1) ◽  
pp. 73-90 ◽  
Author(s):  
Edi Karni
Keyword(s):  
Decision Making ◽  
State Space ◽  
The State ◽  
Decision Making Under Uncertainty ◽  
Choice Sets ◽  
Standard Models ◽  
Definition Of ◽  
States Of Nature ◽  
Conceptual Difficulties

Abstract:This paper discusses the definition of the state space and corresponding choice sets that figure in the theory of decision making under uncertainty. It elucidates an approach that overcomes some conceptual difficulties with the standard models and accommodates a procedure for expanding the state space in the wake of growing awareness.

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