Optimal Decision Rules in Uncertain Dichotomous Choice Situations

1982 ◽  
Vol 23 (2) ◽  
pp. 289 ◽  
Author(s):  
Shmuel Nitzan ◽  
Jacob Paroush
Keyword(s):  
Decision Rules ◽  
Optimal Decision ◽  
Dichotomous Choice ◽  
Uncertain Dichotomous Choice

Bank's Portfolio Management under Uncertainty

1992 ◽  
Vol 36 (2) ◽  
pp. 30-38
Author(s):  
Subarna K. Samanta ◽  
Ali H. Mohamad-Zadeh
Keyword(s):  
Inventory Management ◽  
Commercial Bank ◽  
Expected Utility ◽  
Portfolio Management ◽  
Decision Rules ◽  
Optimal Decision ◽  
Net Income ◽  
Major Objective ◽  
Excess Reserves

The major objective of this paper is to derive a set of optimal decision rules (for asset or inventory management) for a commercial bank operating under uncertain circumstances (subject to stochastic deposit loss). The bank is assumed to be maximizing the expected utility derived from it's net income. This objective is realized by the marginal conditions of the model. It shows how and under what conditions, the banker should expand loans at the expense of securities and/or excess reserves and how he adjusts to de-regulations and how the change in uncertainty about the deposit loss affects him.

Classifiers Based on Optimal Decision Rules

2013 ◽  
Vol 127 (1-4) ◽  
pp. 151-160 ◽  
Author(s):  
Talha Amin ◽  
Igor Chikalov ◽  
Mikhail Moshkov ◽  
Beata Zielosko
Keyword(s):  
Decision Rules ◽  
Optimal Decision

Has Dynamic Programming Improved Decision Making?

2019 ◽  
Vol 11 (1) ◽  
pp. 833-858 ◽  
Author(s):  
John Rust
Keyword(s):  
Decision Making ◽  
Dynamic Programming ◽  
Real World ◽  
Operations Research ◽  
Decision Rules ◽  
Decision Problems ◽  
Optimal Decision ◽  
Sequential Decision ◽  
Success Stories ◽  
Real World Applications

Dynamic programming (DP) is a powerful tool for solving a wide class of sequential decision-making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision in any situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence with the comparative paucity of its real-world applications to improve the decision making of individuals and firms. The fuzziness of many real-world decision problems and the difficulty in mathematically modeling them are key obstacles to a wider application of DP in real-world settings. Nevertheless, I discuss several success stories, and I conclude that DP offers substantial promise for improving decision making if we let go of the empirically untenable assumption of unbounded rationality and confront the challenging decision problems faced every day by individuals and firms.

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