The techniques presented in Chapters 2 through 10 are typically thought of as deterministic ; that is, they are not subject to uncertainty or variation. With deterministic techniques, the assumption is that conditions of complete certainty and perfect knowledge of the future exist. In the linear programming models presented in previous chapters, the various parameters of the models and the model results were assumed to be known with certainty . In the model constraints, we did not say that a bowl would require 4 pounds of clay "70% of the time." We specifically stated that each bowl would require exactly 4 pounds of clay (i.e., there was no uncertainty in our problem statement). Similarly, the solutions we derived for the linear programming models contained no variation or uncertainty. It was assumed that the results of the model would occur in the future, without any degree of doubt or chance.
Deterministic techniques assume that no uncertainty exists in model parameters .
In contrast, many of the techniques in management science do reflect uncertain information and result in uncertain solutions. These techniques are said to be probabilistic . This means that there can be more than one outcome or result to a model and that there is some doubt about which outcome will occur. The solutions generated by these techniques have a probability of occurrence. They may be in the form of averages ; the actual values that occur will vary over time.
Probabilistic techniques include uncertainty and assume that there can be more than one model solution .
Many of the upcoming chapters in this text present probabilistic techniques. The presentation of these techniques requires that the reader have a fundamental understanding of probability. Thus, the purpose of this chapter is to provide an overview of the fundamentals, properties, and terminology of probability and statistics.