conceptual thinking about the unknown uncertainty: expected value, sensitivity analysis, and the...
Post on 20-Dec-2015
218 Views
Preview:
TRANSCRIPT
Conceptual thinking about the unknown
Uncertainty: expected value, sensitivity analysis, and the value of information
Review 1Review 1
• Your problem is not as unique as you think it is
• You have more data than you think you have
• You need less data than you think you need
• There is a useful measure that is much simpler than you think it is
• Your problem is not as unique as you think it is
• You have more data than you think you have
• You need less data than you think you need
• There is a useful measure that is much simpler than you think it is
Review 2Review 2
• If it matters, it is detectable/observable
• If it can be detected, it can be detected as an amount (or range of possible amounts)
• If it can be detected as a range of possible amounts, it can be measured
• If it matters, it is detectable/observable
• If it can be detected, it can be detected as an amount (or range of possible amounts)
• If it can be detected as a range of possible amounts, it can be measured
Review 3Review 3
• Write down a number
• Break it down into pieces (decomposition)
• Try different decompositions
• Average
• Write down a number
• Break it down into pieces (decomposition)
• Try different decompositions
• Average
Clarifying the measurement problemClarifying the measurement problem
• What is the decision this is supposed to support?
• What really is the thing being measured?
• Why does this thing matter to the decision?
• What do you know about it now?• What is the value of knowing more?
• What is the decision this is supposed to support?
• What really is the thing being measured?
• Why does this thing matter to the decision?
• What do you know about it now?• What is the value of knowing more?
CalibrationCalibration
There are two extremes of subjective confidence:
Over confidence
Under confidence
There are two extremes of subjective confidence:
Over confidence
Under confidence
Uncertainty and RiskUncertainty and Risk
• Uncertainty = The lack of complete certainty. That is, more than one outcome is possible, so that the true outcome/SON/result/value is not known
• Risk = uncertainty involving hazard. That is, some outcomes are bad, involve a loss, or where they are all bad, some are catastrophic
• Uncertainty = The lack of complete certainty. That is, more than one outcome is possible, so that the true outcome/SON/result/value is not known
• Risk = uncertainty involving hazard. That is, some outcomes are bad, involve a loss, or where they are all bad, some are catastrophic
EXPECTED VALUE ANALYSISEXPECTED VALUE ANALYSIS
• Consists of modeling uncertainty as a set of contingencies that are exhaustive and mutually exclusive with specific probabilities of occurrence.
• In practice, this means the analyst identifies representative contingencies and assigns probabilities to each of them so that they sum to one.
• The probabilities can be based on historically observed frequencies, subjective assessments, or experts (based on information, theory, or both).
• Consists of modeling uncertainty as a set of contingencies that are exhaustive and mutually exclusive with specific probabilities of occurrence.
• In practice, this means the analyst identifies representative contingencies and assigns probabilities to each of them so that they sum to one.
• The probabilities can be based on historically observed frequencies, subjective assessments, or experts (based on information, theory, or both).
Calculating the expected value of net benefitsCalculating the expected value of net benefits
• Calculate the net benefits of each contingency
• Multiply by that contingency's probability of occurrence.
• Sum the weighted benefits
E(NB) = Pi (Bi - Ci)
• Calculate the net benefits of each contingency
• Multiply by that contingency's probability of occurrence.
• Sum the weighted benefits
E(NB) = Pi (Bi - Ci)
Representativeness of contingenciesRepresentativeness of contingencies
Specification of contingenciesSpecification of contingencies
Annualized Annualized Annualized Annualizedcrop value with
irrigationcrop value
without irrigation
cost of dam &
distribution system
net benefit EX(P) V|P EX(P) V|P
$4,500,000 $0 $200,000 $4,300,000 0.10 $430,000 0.05 2150004,500,000 2,800,000 200,000 $1,500,000 0.12 $180,0004,500,000 3,700,000 200,000 $600,000 0.80 $480,000 0.66 $396,0004,000,000 3,600,000 200,000 $200,000 0.12 $24,0003,000,000 2,800,000 200,000 $0 0.10 $0 0.05 $0
$910,000 1.00 $815,000
Decision trees and expected NBDecision trees and expected NB
Decision analysis has two stages. - First, one specifies the logical structure of the
decision problem in terms of sequences of decisions and realizations of contingencies using a diagram (called a decision tree) that links an initial decision to final outcomes.
- Second, one works backwards from final outcomes to the initial decision, calculating expected values of net benefits across contingencies and pruning dominated branches (ones with lower expected values of net benefits).
Decision analysis has two stages. - First, one specifies the logical structure of the
decision problem in terms of sequences of decisions and realizations of contingencies using a diagram (called a decision tree) that links an initial decision to final outcomes.
- Second, one works backwards from final outcomes to the initial decision, calculating expected values of net benefits across contingencies and pruning dominated branches (ones with lower expected values of net benefits).
Vaccine exampleVaccine example
• Present value of expected net benefits of the vaccination program is simply E(CNV) - E(CV) (i.e., the expected value of the costs when not implementing the program minus the expected costs when implementing the program).
• Present value of expected net benefits of the vaccination program is simply E(CNV) - E(CV) (i.e., the expected value of the costs when not implementing the program minus the expected costs when implementing the program).
Decision tree for vaccination program analysisDecision tree for vaccination program analysis
What’s up for grabs?What’s up for grabs?
• Population at risk- Total Population (round to
10K)
- Fraction High Risk ?
• Infection Rate ?• Mortality Rate ?
- Value of Life ?
• Time lost to Flu ?- Opportunity Cost of Time ?
• Chance of Epidemic- First Year ?
- Second Year ?
• Population at risk- Total Population (round to
10K)
- Fraction High Risk ?
• Infection Rate ?• Mortality Rate ?
- Value of Life ?
• Time lost to Flu ?- Opportunity Cost of Time ?
• Chance of Epidemic- First Year ?
- Second Year ?
• Total number of people vaccinated
• Vaccination Rate • Administrative Costs
- Overheads (Fixed)- Dose Price (Variable)
• Adverse Reaction Rate ?• Herd Immunity Rate ?• Vaccine Effectiveness
Rate?• Discount Rate ?
• Total number of people vaccinated
• Vaccination Rate • Administrative Costs
- Overheads (Fixed)- Dose Price (Variable)
• Adverse Reaction Rate ?• Herd Immunity Rate ?• Vaccine Effectiveness
Rate?• Discount Rate ?
Expected net benefits of vaccinationsExpected net benefits of vaccinations
Expected net benefits of vaccinationsExpected net benefits of vaccinations
Worst; best case analysisWorst; best case analysis
• What are our maximum downside risks if we take no action?
• What if we do?
• What are our maximum downside risks if we take no action?
• What if we do?
Histogram of realized net benefitsHistogram of realized net benefits
The cost and value of informationThe cost and value of information
• Perfect information?• Imperfect information?• Quasi-option value
• Perfect information?• Imperfect information?• Quasi-option value
Exogenous learningExogenous learning
Endogenous learningEndogenous learning
top related