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Incorporating Uncertainty In Investment Appraisal Notes

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Lecture 4: Incorporating Uncertainty In Investment Appraisal Summary

* Risk and uncertainty

* Revision of probabilities

* Decision trees Risk and Uncertainty

* Risk = Outcome of a certain decision not certain
* Probabilistic models of decision making

* Uncertainty = outcomes are unknown, probabilities cannot be specified
* Non-probabilistic models of decision making

* Objective probabilities
* Known historical frequencies of occurrence of certain events/outcomes of a certain decision

* Subjective probabilities
* Subjective evaluation of the likelihood of the outcomes of a certain decision
* If historical frequencies are not available, as is the case of highly specific/unique investment projects
* Mathematical treatment analogous to that of objective probabilities
* Allows to transform uncertainty into risk

The basic decision model
* Set of alternative courses of action (or projects) - a
* Set of possible monetary outcomes (or payoffs) for each action - x
* Set of possible states of the world (or events) which influence outcomes - s
* Set of probabilities of each outcome - p(x)
* Choice criterion

* EPPI = Average payoff that could be generated if, for each alternative, states of the world could be perfectly predicted
* EPPI = EMV +EOL

* EOL = Expected opportunity loss

* EVPI = Difference between the best we expect to do with perfect information (average payoff) and the best we expect to do without the information (best average payoff)
* Expected opportunity loss of the optimal alternative

* Expected Monetary Value = EMV = E(X)

* EVSI = Difference between the best we expect to do with additional information (may be imperfect) and the best we expect to do without the information
* Represents the maximum the decision maker would be willing to pay for that additional information
* Imperfect information does not perfectly predict which state of the world is going to occur
? But it may change the subjective probabilities assigned to states of the world by the decision maker Example of a basic decision (Q.4 exam paper 2002)

Course Notes Page 17

Key Points

* Objective vs. subjective probabilities

* The basic decision model

* Choice criteria

* Bayes' rule

* Signals

* Joint and conditional probabilities

* Decision trees Definitions

* Decision trees = Method for capturing the flexibility of investment projects

* EOL = Expected opportunity loss

* EPPI = Average payoff that could be generated if, for each alternative, states of the world could be perfectly predicted

* EVPI = Difference between the best we expect to do with perfect information (average payoff) and the best we expect to do without the information (best average payoff)

* EVSI = Difference between the best we expect to do with additional information (may be imperfect) and the best we expect to do without the information

* Risk = Outcome of a certain decision not certain

* Uncertainty = outcomes are unknown, probabilities cannot be specified Formulae

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*

*

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* EPPI = EMV +EOL

* Expected Monetary Value = EMV = E(X)

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