Decision making is hard. Decision making is easy when we know what to do. When we don't know what to do there are conflicting choices that must be balanced in the presence of uncertainty for each of those choices. The bigger issue is that important choices are usually ones where we know the least about the outcomes and the cost and schedule to achieve those outcomes.
Decision science evolved to cope with decision making in the presence of uncertainty. This approach goes back to Bernoulli in the early 1700s, but remained an academic subject into the 20th century, because there was no satisfactory way to deal with the complexity of real life. Just after World War II, the fields of systems analysis and operations research began to develop. With the help of computers, it became possible to analyze problems of great complexity in the presence of uncertainty.
In 1938, Chester Barnard, authored of The Functions of the Executive, and coined the term “decision making” from the lexicon of public administration into the business world. This term replaced narrower descriptions such as “resource allocation” and “policy making.”
Decision analysis functions at four different levels
- Philosophy - uncertainty is a consequence of our incomplete knowledge of the world. In some cases, uncertainty can be partially or completely resolved before decisions are made and resources committed. In many important cases, complete information is not available or is too expensive (in time, money, or other resources) to obtain.
- Decision framework - decision analysis provides concepts and language to help the decision-maker. The decision maker is aware of the adequacy or inadequacy of the decision basis:
- Decision-making process - provides a step-by-step procedure that has proved practical in tackling even the most complex problems in an efficient and orderly way.
- Decision making methodology - provides a number of specific tools that are sometimes indispensable in analyzing a decision problem. These tools include procedures for eliciting and constructing influence diagrams, probability trees, and decision trees; procedures for encoding probability functions and utility curves; and a methodology for evaluating these trees and obtaining information useful to further refine the analysis.
Each level focuses on different aspects of the problem of making decisions. And it is decision making that we're after. The purpose of the analysis is not to obtain a set of numbers describing decision alternatives. It is to provide the decision-maker the insight needed to choose between alternatives. These insights typically have three elements:
- What is important to making the decision?
- Why is it important?
- How important is it?
Now To The Problem at Hand
It has been conjectured ...
The key here and the critical unanswered question is how can a decision about an outcome in the future, in the presence of that uncertain future, be made in the absence of estimating the attributes going into that decision?
That is, if we have less than acceptable knowledge about a future outcome, how can we make a decision about the choices involved in that outcome?
Dealing with Uncertainty
All project work operates in the presence of uncertainty. The underlying statistical processes create probabilistic outcomes for future activities. These activities may be probabilistic events, or the naturally occurring variances of the processes that make up the project.
Clarity of discussion through the language of probability is one of the basis of decision analysis. The reality of uncertainty must be confronted and described, and the mathematics of probability is the natural language to describe uncertainty.
When we don't have the clarity of language, when redefining mathematical terms, misusing mathematical terms, enters the conversation, agreeing on the ways - and there are many ways - of making decisions in the presence of an uncertain future - becomes bogged down in approaches that can't be tested in any credible manner. What remains is personal opinion, small sample anecdotes, and attempts to solve complex problems with simple and simple minded approaches.
For every complex problem there is an answer that is clear, simple, and wrong. H. L. Mencken