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Incorporating Uncertainty In Investment Appraisal Notes

This is an extract of our Incorporating Uncertainty In Investment Appraisal document, which we sell as part of our Managerial Accounting Notes collection written by the top tier of LSE students.

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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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*

*

*

*

*

*

* EPPI = EMV +EOL

* Expected Monetary Value = EMV = E(X)

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