Uncertainty refers to a situation in which a decision is expected to yield more than one outcome and the probability of none of the possible outcomes is known. Therefore, decisions taken under uncertainty are necessarily subjective. However, analysis have devised some decision rules to impart some objectively to the subjective decisions, provided decision-makers are able to identify the possible ‘states of nature’ and can estimate the outcome of each strategy. Some such important decision rules are discussed below:
1. Wald’s maximum decision criterion
Wald’s maximum decision criterion says that the decision-makers should first specify the worst possible outcome of each strategy and accept a strategy that gives best out of the worst outcomes. It gives a conservative decision rule for risk avoidance. However, this decision rule can be applied by those investors who fall in the category of risk averters. This investment rule can also be applied by firms whose very survival depends on avoiding losses.
2. Minimax regret criterion
| Minimax regret criterion is another decision rule under uncertainty. This criterion suggests that the decision-makers should select a strategy that minimizes the maximum regret of a wrong decision. What is regret? “Regret is measured by the difference between the pay-off of a given strategy and the pay-off of the best strategy under the same state of nature. Thus, regret is the opportunity cost of a decision. |
3. Hurwicz decision criterion
Hurwicz has suggested another criterion for investment decision under uncertainty. In his opinion, full realization of optimistic pay-off or full realization of most pessimistic pay-off is a rare phenomenon. The actual pay-off of a strategy lies somewhere between the two extreme situations. According to Hurwicz criterion, therefore, the decision-makers need to construct a decision index of most optimistic and most pessimistic pay-offs of each alternative strategy. The decision index is, in fact, a weighted average of maximum possible and minimum possible pay-offs, weight being their subjective probability such that sum of probabilities of maximum (Max) and minimum (Min) pay-offs equals one.
4. Laplace decision criterion
The Laplace criterion uses the Bayesian rule to calculate the expected value of each strategy. As mentioned earlier, Bayesian rule says that where meaningful estimate of probabilities is not available, the outcome of each strategy under each state of nature must be assigned the same probability and that the sum of probabilities of outcome of each strategy must add up to one. For this reason, the Laplace criterion is also called the ‘Bayesian criterion’. By assuming equal probability for all events, the environment of ‘uncertainty’ is converted into an environment of ‘risk’.
Once this decision rule is accepted, then decision-makers can apply the decision criteria that are applied under the condition of risk. The most common method used for the purpose is to calculate the ‘expected value’ as defined in the case of pay-off matrix in section. Once expected value of each strategy is worked out, then the strategy with the highest expected value is selected.
This decision rule avoids the problem that arises due to subjectivity in assuming a probability of pay-off. This criterion is, therefore, regarded as the criterion of rationality because it is free from a decision-makers attitude towards risk.
To sum up, uncertainty is an important factor in investment decisions but there is no unique method of dealing with uncertainty. There are several ways of making investment decisions under the condition of uncertainty. None of the methods as described above lead to a flawless decision. However, they do add some degree of certainty to decision-making. The choice of method depends on the availability of necessary data and reliability of a method under different conditions.
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