EVPI
Expected Value of Perfect
Information (EVPI)
In decision-making under risk , each state of nature is associated with probability of its occurrence ;
If the decision-maker can acquire perfect (complete) information about the occurrence of various states of nature , he will be able to select a strategy that yields the desired pay-off for whatever state of nature that actually occurs
EMV /EOL criterion helps the decision-maker select a strategy that optimises the expected pay-off without complete information
EVPI =Expected profit with perfect information-expected profit without complete information
=Expected loss without complete information-Expected loss with perfect information.
Decision Tree
Is a technique for handling multi-stage decision problem, where consequence of one decision affects future decisions
This analysis involves construction of a diagram showing all the strategies , states of nature and probabilities associated with the states of nature
A decision tree consists of nodes , branches
,probability estimates and pay-offs.
Nodes are of two types : decision nodes and chance nodes Decision Tree
A decision node is usually represented by a square , where a decision-maker must make a decision
Each branch leading away from a decision node represents one of the strategies available to the DM
A chance node is usually represented by a circle and indicates a point where the DM will discover the response to his decision, i.e., different possible outcomes from a chosen course of action
Decision Tree
Branches emanate from and connects various nodes
( decision/ states of nature)
Two types of Branches : decision branches & chance branches Decision Branch: Each branch leading away from the decision node represents a strategy that can be chosen at a decision point
Chance Branch : A branch leading away from the chance node represents the states of nature of a set of chance factors .Associated probabilities are indicated
Information (EVPI)
In decision-making under risk , each state of nature is associated with probability of its occurrence ;
If the decision-maker can acquire perfect (complete) information about the occurrence of various states of nature , he will be able to select a strategy that yields the desired pay-off for whatever state of nature that actually occurs
EMV /EOL criterion helps the decision-maker select a strategy that optimises the expected pay-off without complete information
EVPI =Expected profit with perfect information-expected profit without complete information
=Expected loss without complete information-Expected loss with perfect information.
Decision Tree
Is a technique for handling multi-stage decision problem, where consequence of one decision affects future decisions
This analysis involves construction of a diagram showing all the strategies , states of nature and probabilities associated with the states of nature
A decision tree consists of nodes , branches
,probability estimates and pay-offs.
Nodes are of two types : decision nodes and chance nodes Decision Tree
A decision node is usually represented by a square , where a decision-maker must make a decision
Each branch leading away from a decision node represents one of the strategies available to the DM
A chance node is usually represented by a circle and indicates a point where the DM will discover the response to his decision, i.e., different possible outcomes from a chosen course of action
Decision Tree
Branches emanate from and connects various nodes
( decision/ states of nature)
Two types of Branches : decision branches & chance branches Decision Branch: Each branch leading away from the decision node represents a strategy that can be chosen at a decision point
Chance Branch : A branch leading away from the chance node represents the states of nature of a set of chance factors .Associated probabilities are indicated