Difference between revisions of "Optimizing rules"
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'''Optimizing rules''' are rules that are used to prioritise between several possible decision options. In decision analysis, this is done by estimating the outcomes of all options using some outcome measures or utilities, and then comparing the outcomes using the rules as the criteria. There are broad categories of optimizing rules: | '''Optimizing rules''' are rules that are used to prioritise between several possible decision options. In decision analysis, this is done by estimating the outcomes of all options using some outcome measures or utilities, and then comparing the outcomes using the rules as the criteria. There are broad categories of optimizing rules: | ||
#Utilitarian optimizing: maximize the average utility over the whole population | #Utilitarian optimizing: maximize the average utility over the whole population | ||
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#Risk averse: minimize the possible loss (one euro for sure is better than ten euros with probability 0.1) | #Risk averse: minimize the possible loss (one euro for sure is better than ten euros with probability 0.1) | ||
#Risk prone: maximize the possible benefit (one euro for sure is worse than ten euros with probability 0.1) | #Risk prone: maximize the possible benefit (one euro for sure is worse than ten euros with probability 0.1) | ||
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Latest revision as of 11:06, 3 October 2008
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Optimizing rules are rules that are used to prioritise between several possible decision options. In decision analysis, this is done by estimating the outcomes of all options using some outcome measures or utilities, and then comparing the outcomes using the rules as the criteria. There are broad categories of optimizing rules:
- Utilitarian optimizing: maximize the average utility over the whole population
- Egalitarian optimizing: maximize the utility for those who are worst off
- Elitist optimizing: maximize the utility for those who are best off
In addition, several attitudes towards risk can be used:
- Risk neutral: maximize the expected outcome (one euro for sure is equal to ten euros with probability 0.1)
- Risk averse: minimize the possible loss (one euro for sure is better than ten euros with probability 0.1)
- Risk prone: maximize the possible benefit (one euro for sure is worse than ten euros with probability 0.1)
