DecisionMaking under Uncertainty

Decision-making under uncertainty Decision making under uncertainty involves looking for additional information to make adecision, checking on the attitudes of the manager towards risk, and making a choice among risky alternatives for the course of action. Risk is the probability of threat, damage, loss, liability, or any negative consequence resulting from internal and external vulnerabilities and can be avoided through taking precautionary measures. Uncertainty is a situation where there more than one possible outcome in a given situation (Samuelson and Stephen, 500). Risk and uncertainty differ in that in risk, the outcome is unknown but the distribution of the outcome is not known however in uncertainty, both the outcome and the distribution of the outcome are not known (Samuelson and Stephen, 501).
Probability of an outcome is the odds that the outcome will occur (Samuelson and Stephen, 501). The three types of probability discussed in the book are relative frequency, subjective probability, and theoretical probability. The difference in these methods in generating probability is that the theoretical probability assigns equal likelihood to all possible outcomes of an experiment, therefore, the chances of an event occurring is 1/n for each possible outcome. Relative frequency probability type gives the probability of an event through finding the ratio of the times the event occurs and the number of trials undertaken. Subjective probability determines the probability of an outcome “represents the decision maker’s degree o belief that the outcome will occur” (Samuelson and Stephen, 501).
Statistical inference involves the use of statistical techniques for generating conclusions from a set amount of data through observation or sampling. Statistical inference using historical data involves the generation of conclusions from data that was collected a long time ago. Statistical bias using historical data creates bias in rapidly changing markets because of a change in the conditions of the market when the information was collected and the current market situation. The basis of the data collected could have changed and the current conditions reflect a difference. hence, the conclusions made reflect on the past and not the market situation currently as there are frequent changes in the market conditions resulting in statistical bias. The other reason for the bias in statistical inference of historical data is that statistical inference is affected by the random variation of the data over time and this variation results in the change of the inference made at a point in time. The expected value and its standard deviation aim at dealing with risk by deciding on a certain value that one expects from a given risky situation. In expected value and its standard deviation, the decision maker chooses the option that maximizes his/her expected value.
Risk neutrality in decision-making results in lack of consideration for future direction in the value of the asset if it will increase or decrease. Despite the fact that this decision-making method has the advantage of speed where many option are easily calculated in a short time, there are limitations associated with risk neutrality in decision making including inability to calculate price option over time, since it decides the price at the expiration date only. The inability of the decision maker to consider early exercise before the expiration of the duration owing to the consideration of the expiry date of the asset is the second limitation of risk neutrality decision-making model. Early exercising does not give the decision maker any advantage in risk neutral decision-making. therefore, it is affected by changing economic times that could have resulted in better returns from the asset.
Work Cited
Samuelson, Williams &amp. Stephen, Marks. Managerial Economics. Hoboken, NJ: John Wiley and Sons, 2012. Print.