Applied Statistical Decision Theory.

  title={Applied Statistical Decision Theory.},
  author={Paul D. Minton and Howard Raiffa and Robert Schlaifer},
  journal={American Mathematical Monthly},
In general, this book is concerned with capital budgeting and decision theory. Specifically, it centers around a singular uncertainty decision the decision by oil and gas operators to drill, or not to drill, a well. Actually, a sequence of decisions must precede this final action, but each one of these preliminary decisions is principally directed toward the final payoff question: Should we invest money in this well? If so, how much should we risk, and how much of the risk should we share with… 
A Model for Decision Making Under Uncertainty
Decision theory usually is partitioned according to whether the decision is made under conditions of (a) certainty, (b) risk, or (c) uncertainty. These areas are defined as follows: (a) Certainty if
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Decision making with adaptive utility provides a generalisation to classical Bayesian decision theory, allowing the creation of a normative theory for decision selection when preferences are
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Applied statistical decision theory has wide applications in decision-making fields of studies, such as economic, business management and industrial managements. In this work, following Pratt et
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An overview is presented of an economic decision model a) that incorporates linear programming and a Bayesian decision model and b) can be utilized by a cattle feeder in making decisions under
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Through a simple numerical example, various (but certainly not all) ways to do decision making with imprecise probabilities, and the differences between these ways, are demonstrated. Introduction
Uncertainty, imprecision, and the precautionary principle in climate change assessment.
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Six of the more useful classes of probability measures are described and it is demonstrated how each may be used to represent climate change uncertainties and the need for care when selecting an appropriate class is emphasized.
Value of Information in Portfolio Decision Analysis
This paper defines and compares different analytic strategies in terms of the resulting value added for a range of simulated portfolios and compares them against a strawman alternative of random funding decisions.
Information Avoidance and Overvaluation in Sequential Decision Making under Epistemic Constraints
This paper illustrates how to assess VoI in sequential decision making under epistemic constraints (as those imposed by societal regulations), by modeling a Partially Observable Markov Decision Processes (POMDP) and evaluating non optimal policies via Finite State Controllers (FSCs).
How to elicit a cost function? Lessons of hope and disappointment from a diced bacon case-study
Statistical decision theory provides an attractive framework to help choose decisions under uncertainty. Unfortunately, it does not seem to be often implemented for specific applications. In this