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Chap 12: Risk & Uncertainty - Coggle Diagram
Chap 12: Risk & Uncertainty
When there is a strong element of risk or uncertainty in a decision, the decision that is taken may be affected by the extent of the risk or uncertainty.
Making decisions now affects future outcomes. This means that managers preparing and implementing budgets will need to estimate figures upon which to make decisions and set set targets.
Risk exists where a decision-maker has knowledge that several different future outcomes are possible, usually due to past experience.
This past experience enables a decision-maker to estimate the probability of the like occurrence of each potential future outcome.
Risk Seeker
Risk neutral
Risk averse
Allowing for uncertainty:
obtain more information about the effects of the decision via Market Research & Focus Groups
-> reliable information can reduce uncertainty.
Probabilities and expected values (EV)
When final outcome of a decision is unknown and a range of possible future outcomes have been quantified, probabilities can be assigned to these outcomes and a weighted average of those outcomes calculated.
Highest EV should be chosen
Pay off tables: identify and record all possible outcomes (or pay-offs) in situations where there are several decision options and the outcome from each decision depends on the eventual circumstances that arise.
Limitations of EV:
EV is a long-term average, so will not be reached in the short term and is therefore not suitable for one off decisions.
The results are dependent on the accuracy of the probability estimates
The EV itself may not represent a single possible outcome
It ignores the range of possible outcomes
Other Decisions
Maximin decision rule:
-> is that a decision-maker should select the alternative that offers the least unattractive worst outcome. This would mean choosing the alternative that maximises the minimum profits.
Maximise the minimum return of each decision
Apply to a risk averse decision maker
Does not consider the probability of each outcome occurring
Is conservative (do not try maximising profit)
Maximax decision rule:
-> The maximax criterion looks at the best possible results. Maximax means 'maximise the maximum profit'. The decision with this rule is to choose the option that could provide the maximum possible profit.
Aim for best possible return
Apply to risk decision-maker
Does not consider the probability of each outcome occurring
is overly optimistic
Minimax regret decision rule:
-> Aims to minimise the regret from making the wrong decision.
Regret is the opportunity lost through making the wrong decision.
Decision Trees:
is a pictorial method of showing a sequence of interrelated decisions and their expected outcomes.
They can incorporate both the probabilities and value of expected outcomes and are used in decision-making.
Most useful when there are several decisions and ranges of outcome.
Evaluate the tree from right to left.
Benefit:
Clearly shows all the decisions and uncertain events and how they are interrelated.
Problems:
Based on EV, so suffers from same disadvantages as all EV techniques.
Heavily dependent on the probabilities used.
Oversimplification of reality.
Value of Information:
Information about uncertain variables may be available, from market research.
If this information is guaranteed to predict the future with certainty, it is defined as perfect information.
Perfect Information is information that predicts with 100% accuracy what the outcome situation will be.
Having PI removes all doubt and uncertainty from a decision, and enables managers to make decisions with complete confidence that they have selected the best decision option.
Value of PI (VOPI) = EV with PI - EV without PI
Limitations of using PI:
In practice useful information is never perfect, market research findings can be reasonably accurate, but could still be wrong an did is not until after the decision has been made that this would be known.
This is known as imperfect information, which while not as valuable as PI, is still better than nothing.
Techniques for dealing with certainty:
Sensitivity analysis is a method of analysing the uncertainty in a situation or decision.
-> It measures the effect of changes in the estimated value of an item o the future outcome.
-> It can therefore be used to assess the sensitivity of the expected outcome to variations or changes in the value of the item.
Easy to understand
Highlights key variables which are crucial to the success of the project, once identified these can be closely monitored
Assumes that all changes are independent; in reality, multiple variables are likely to change simultaneously
Only identifies the amount of change required in one variable, it does not assess the probability of that change occurring.
Does not offer a clear decision rule; appropriate management judgment is still required.
Simulation:
Models created using computers and random numbers.