Decision Making Under Uncertainty
Every day, managers make decisions that affect the profitability, effectiveness, and viability of the organization. Sometimes the factors affecting the predictability of events can be determined. However, not all variables affecting outcomes are neatly predictable. Decisions made under uncertainty are decisions for which there is no meaningful probability distribution underlying the various outcomes. In these situations, the decision maker simply does not know what will happen for the various decision alternatives. There are several approaches to decision making under conditions of uncertainty, including application of the Bayes' Decision Rule, Markov processes, and gaming. In the end, however, virtually every decision requires judgment. Knowledge of stochastic processes alone are insufficient to guide decision making.
Keywords Bayes' Decision Rule; Decision Analysis; Decision Theory; Gaming; Markov Chain; Model; Probability; Stochastic
Statistics: Decision Making Under Uncertainty
Every day, managers make decisions that affect the profitability, effectiveness, and viability of the organization. Although in some of these cases the parameters are known (e.g., if Harvey gives a raise to the production workers, there will not be enough money left over to buy parts to make widgets), in other cases they are not known (e.g., if Harvey does not give the production workers a raise, he does not know whether or not they will stay and continue to make widgets). Similarly, many of the decisions facing managers are complex (e.g., Harvey can ask the workers to postpone getting a raise and continue to make widgets while the company tries a new marketing strategy; if the workers do not continue to make widgets, the company cannot afford the new marketing campaign. However, there is no way to predict with 100 percent accuracy whether or not the campaign will be successful enough to bring in the added revenue to enable the company to give the workers a raise).
Factors Affecting the Predictability of Events
Trends, Business Cycles
Sometimes the factors affecting the predictability of events can be determined. These deterministic variables are those for which there are specific causes or determiners and include trends, business cycles, and seasonal fluctuations. Trends are persistent, underlying directions in which a factor or characteristic is moving in either the short, intermediate, or long term. In most cases, trends are linear rather than cyclic; growing or shrinking steadily over a period of years. For example, the increasing tendency for business to outsource and offshore technical support and customer service in many high tech companies over the past few years is a trend. However, not all trends are linear. Trends in new industries tend to be curvilinear as the demand for the new product or service grows after its introduction and then declines after the product or service becomes integrated into the economy. Another type of deterministic factor is business cycles. These are continually recurring variations in total economic activity. Business cycles usually occur across most sectors of the economy at the same time. For example, it has been noted that several years of a boom economy with expansion of economic activity (e.g., more jobs, higher sales) are often followed by slower growth or even contraction of economic activity. Business cycles may occur across one industry, a business sector, or even the economy in general. A third type of deterministic factor is seasonal fluctuations. These changes in economic activity occur in a fairly regular annual pattern and are related to seasons of the year, the calendar, or holidays. For example, office supply stores typically experience an upsurge in business in August as children receive their school supply lists for the coming year. Similarly, the demand for heating oil is typically greater during the cool months than it is in the warm months.
However, not all variables affecting outcomes are so neatly predictable. Stochastic variables are caused by randomness or include an element of chance or probability. These include both irregular and random fluctuations in the economy that occur due to unpredictable factors. For example, a natural disaster such as an earthquake or flood, political disturbance such as war or flu epidemic that causes high absenteeism is often unpredictable and can affect a business' profitability. In conditions of uncertainty, there is no meaningful probability distribution for the various outcomes. In these situations, the decision maker does not know what will happen for the various decision alternatives.
Conflicting Interests in Decision Making
Another factor complicating real world decision making processes is the fact that there is often more than one party to the decision and the parties may have conflicting interests. In fact, systems theory posits that the organization comprises multiple subsystems and that the functioning of each affects both the functioning of the others and the organization as a whole. So, for example, in the illustration above concerning giving raises to the workers during a time of flux, there are at least two major parties to the decision. From the workers' point-of-view, getting a raise now is better than maybe getting a raise later. Their raise or lack thereof, in turn, affects other parties not directly in the discussion such as their families (e.g., if there is no raise, the family cannot pay for Johnny's tuition) and their creditors (e.g., if there is no raise, the family cannot meet the increased payment on their adjustable rate mortgage). Management, of course, has a different point-of-view. If they give the workers a raise now, they will not have sufficient funds available for the new marketing campaign that will bring in more revenue. Without the additional revenue, they will have to lay off some of the workers, which means that they will not be able to meet an increased demand for widgets even if they do launch the new marketing campaign. They could take money to pay the production workers from the monies set aside for raises for new product development, but then they would not be able to gain a competitive edge over the companies offering similar items in the marketplace. In addition, management needs to report to its stockholders, and increased wages may mean decreased profits.
Categories of Decisions to be Made
The decisions facing managers in the business world can be classified into several categories: decisions made under certainty or uncertainty, under risk, or under conflict. A decision made under certainty occurs when all the facts of the situation are known and the model provides the decision maker with the exact consequences of choosing each alternative. This knowledge, however, does not mean that the decision is either obvious or trivial. There may be many possible courses of actions that can be taken, each with different consequences, and the decision maker needs to consider the advantages and disadvantages of each and weigh them against each other. Decisions made under uncertainty, on the other hand, are decisions for which there is no meaningful probability distribution underlying the various outcomes. In these situations, the decision maker simply does not know what will happen for the various decision alternatives.
Multiple Criteria Decision Making
Multiple criteria decision making is a discipline that deals with the problem of making decisions in complex situations where there are conflicting objectives. Multiple criteria decision making is founded on two interrelated, key concepts. Satisficing is the attempt to find solutions that satisfy all the constraints rather than optimizing them. For example, the workers may be given a raise, but only in six months after the new marketing campaign goes into effect. This would still give them a raise, but would also give the company an opportunity to get back on its feet. From the workers' point-of-view, the optimal situation would be to get raise now. From management's point-of-view, the optimal situation would be to keep wages low so that there are more profits. Neither one of these situations is optimized in this solution, but the constraints of both are satisfied. The second key concept of multiple criteria decision making is bounded rationality. This process involves setting the constraints of the situation and then attempting to find solutions that satisfy the constraints. This is an iterative process in which the constraints are adjusted as necessary and the search for solutions is continued until a satisfactory solution is found. For example, the workers at Widget Corporation may be willing to postpone getting a raise only if the raise that they get in six months is greater than the one that they would accept today.
Methods for Solving Multiple Criteria Decision Making Problems
As shown in Table 1, there are a number of methods available for solving multiple criteria decision making problems. Deterministic decision analysis is used to find the most preferred alternative in the decision space using value functions. Stochastic decision analysis does the same thing, but uses utility functions and stochastic outcomes. In the stochastic approach, both the utility function and the probability of the various outcomes are estimated by the decision maker. The multi-objective mathematical...
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