Next week I plan to talk about how I’ve started putting what I learned in Decision Analysis to use in my work life…but before I do that…I would like to give you some background information on my love-hate relationship with DA.
Term #1 was dominated by this amazing class that some days I loved and other days I hated. That class was Decision Analysis. It’s a love – hate relationship because the material in the class at times can be a pain to learn / understand…but once you get it (once that click happens, or that light bulb goes off in your head)…you love it because all of these problems that in the past you thought were unsolvable suddenly become solvable. Hence the love – hate relationship. Below you will find an introduction to what DA is all about…it is from the class syllabus…next week I’ll post how I’ve actually started using some of the concepts that I’ve learned in DA to improve a friends response to a request for a proposal (RFP)
Here is what DA is all about (it’s from the class Syllabus)
Business decisions, both tactical and strategic, are frequently made difficult by the
presence of uncertainty in the resulting consequences. This reality cuts across all
functions of business, each of which has devised “shortcuts” for dealing with this
uncertainty. But all of these so-called shortcuts are grounded in the fundamentals of
decision analysis, which applies mathematical principles to the underlying economics of
In this course, we present a philosophy for framing, analyzing and proactively managing
decisions involving uncertainty, whether the uncertainty results from general conditions
or the actions of competitors. The course will focus on making the uncertainty managers’
face explicit so that it can be objectively analyzed.
One way to proactively manage risk is to reduce the inherent uncertainty one faces, for
example through better forecasting. Tools and techniques to support this objective
include risk profiles, expected value, simulation, sensitivity analysis, discounted cash
flows, analytical probability distributions, data analysis, sampling theory, and regression.
The course will also focus on proactively managing risk by recognizing and exploiting
opportunities to reduce exposure to uncertainty, for example through contingent
contracts. Tools and techniques will include decision trees, value of information, value
of control, downstream decisions, real options, and game theory.