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Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions
Stefan Rass1, Sandra König2, Stefan Schauer2
1Universität Klagenfurt, Institute of Applied Informatics, Klagenfurt, Austria.
Plos One
|December 29, 2016
Summary
This study introduces loss-distributions, a novel method for ordering random variables to aid decision-making under uncertainty. This approach enables better decisions when consequences are unpredictable.
Area of Science:
- Decision Analysis
- Probability Theory
- Risk Management
Background:
- Decision-making often involves imprecise information and unpredictable consequences.
- Existing decision theory struggles with ordering all possible probability distributions.
Purpose of the Study:
- To introduce a novel theoretical framework for decision-making under uncertainty.
- To develop a method for ordering random loss variables.
Main Methods:
- Defining and utilizing a restricted subset of probability distributions termed 'loss-distributions'.
- Establishing a total ordering on random loss variables based on these distributions.
- Employing simulation data to validate the approach.
Main Results:
- A theoretical framework for decision-making under uncertainty was established.
- A method for creating a total ordering on random loss variables was developed.
- The practical applicability of the loss-distribution approach was demonstrated through simulations.
Conclusions:
- Loss-distributions offer a practical way to compare random variables for decision-making.
- This method enables informed decisions even with uncertain consequences.
- The approach provides a valuable tool for decision theory and risk management.
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