Decision theory or analysis models deal primarily with decisions involving risk under institutional constraints. This risk under constraint also involves the important variable of limited information; that is, there are some things the decision maker does not know. In general, such models are used by those involved with investment decisions and market analysis, as well as risk analysis in a business, and not so much by lower-level employees.
Elements in Decision
In any business decision is an agent, an individual person in charge of the decision and responsible for it. The agent can be an owner or a major investor. There is also a finite number of possible outcomes. In addition, there are many alternatives to the present course of action. Only one can be chosen. Finally, there are a number of possible payoffs — with corresponding risks — for each possible course of action. All of these variables, to be properly analyzed, must be laid out in detail. Those whose job it is to measure risk and to forecast future market trends use these methods daily.
Investing with Limited Information
One of the most important elements of decision making analysis concerns the constant necessity of making important decisions under limited information. If a firm wants to buy a supplier so as to integrate vertically, the information concerning this will be inherently limited. Costs of raw materials and labor, just to use two examples, serve to show how problematic such as decision is.
Decision Theory under Uncertainty
Another important element in decision analysis is uncertainty. This is evident in the stock or bond markets. If a firm wants to decide whether to use debt or equity financing, the two most important variables are the current and future states of the markets. This, in turn, might depend on an analysis of present and future interest rates; if rates will fall in the future, then the money market is the way to go. This is a decision under uncertainty. Such important decisions often lie in the higher parts of management, using data supplied by risk analysts and accounting numbers.
Michael Oakeshott's Critique
The late Professor Michael Oakeshott of the London School of Economics rejected this rationalist approach to decision analysis. For him, decision theory cannot be based on quantitative figures. Anyone can crunch numbers, but only experience matters in the long run. Data mean nothing unless data can be contextualized within the history and tradition of a certain economic sector. The point is that only the most experienced parts of a firm should make important decisions. “Wisdom” is accumulated experience and should be based on a long-term immersion into an economic or political tradition. It should be passed down through apprenticeship. Data has its place but only within a context of long-term experience that manifests itself in non-quantifiable intuition.