Decision Analysis: Applied Decision Theory

Abstract

quoted by \cite{soohoo2000_howMuch} Howard Raiffa and Robert Schlaifer, Applied Statistical Decision Theory (Boston: Harvard University, 1961). \cite{raiffa1961_decisionTheory} Ronald A. Howard, �Decision Analysis: Applied Decision Theory,� Proceedings of the Fourth International Conference on Operational Research, David B. Hertz and Jacques Melese, editors (New York: Wiley- Interscience, 1966), pp. 55�71. The application of statistical decision theory to management problems traces its roots to the seminal work of Raiffa and Schlaifer in 1961 with considerable refinement by Howard in 1966. The term �decision analysis� was coined by Howard to refer specifically to the formal procedure for analyzing decision problems outlined in his article and subsequent research. At its core, decision analysis is a reductionist modeling approach that dissects decision problems into constituent parts: decisions to be made, uncertainties that make decisions difficult, and preferences used to value outcomes. Decision analysis offers several key advantages that recommend it well to the problem of computer security risk management. First, as its name implies, it is necessarily a decision-driven modeling technique. Second, its incorporation of probability theory provides it tools to capture, clarify, and convey uncertainty and the implications of uncertainty. Third, and probably most important, decision analysis utilizes influence diagrams as a common graphical language for encapsulating and communicating the collective knowledge of an organization, thus facilitating consensus-building.