Economists frequently characterize humans as “rational beings,” capable of subconsciously weighing the costs and benefits of every situation and taking appropriate action whenever possible. This assumption allows the construction of sophisticated models of human behavior used to predict your demand for Cheetos, how likely you are to speed on the way to work, or the likelihood that you will die from python-inflicted asphyxiation.
The economist Herbert Simon pointed out some glaring lapses in the rationality of mankind, like the continued existence of Las Vegas casinos, and the fact that mankind needs economists to point out principles that everyone supposedly knows and already uses. He posited that people weigh the costs and benefits of even their information gathering, and choose a level of “bounded rationality” that suits them. In other words, we don’t have enough time and energy to be completely informed, so we determine how informed we can afford to be and accept the consequences.
The practice of Decision Analysis allows an individual to become more functionally rational by providing tools that can be incorporated implicitly into ones thinking, or used explicitly on paper or a spreadsheet, to make more informed decisions. The fundamental process in decision analysis is to (1) clearly define your problem, (2) specify your objectives, (3) generate alternatives, and then (4) evaluate them in a way that allows you to more intelligently judge which option is best. This may require you to combine qualitative criteria (e.g. how exciting, obnoxious, or colorful something is) with quantitative variables (e.g. the money and time you have to give up). You may also need to account for the fact that some benefits occur now while others occur later, and some alternatives may involve more risk than others. The thought of evaluating all these potential factors and boiling them down into one final verdict may seem daunting, but these skills can actually be learned in a relatively short period of time.
Qualitative criteria can be evaluated in a straightforward manner. After determining your alternatives and objectives, simply form a matrix and use a 1-9 scale to score each of your alternatives on each objective. You then need to determine how important each objective is (i.e. split up 100% into weights for each one). Then you can calculate a weighted score for each alternative and compare them easily. The accompanying examples show how universally-applicable this technique can be: you can determine which language to learn next, which projects to incorporate into your work schedule, or (if you’re feeling Napoleonic) which country to invade. You can also build an instrument that tells you, based on your mood, what books you should read. In a more global application, Thomas Saaty, a pioneer in the field of decision analysis, even developed a more sophisticated version of this method to evaluate policy strategies for the Israel-Palestine conflict.
When we make decisions, we are often unsure about assigning definite numbers to fuzzy situations. That is perfectly fine—you can incorporate fuzzy numbers into your decision analysis by specifying a range of numbers and utilizing a Monte Carlo simulation to test many scenarios at once. This allows you to see an overall pattern and still make clear decisions even in the face of uncertainty. You can also use Excel to simulate how the results will change as one or two of your factors vary, which allows you to more effectively evaluate the complex relationships between different unknowns and determine your strategy.
Quantitative data such as time and money require other unique methods to evaluate. For example, say you are trying to decide between three investment opportunities: one is a sure bet, but doesn’t pay very much. Another pays much more, but requires you to wait for a few years. The third might pay off very well, but involves some risk. Which should you choose? Decision analysis can help here as well. You can account for time differences by calculating your time discount rate and decreasing the value by that amount. You can also calculate how risk-averse you are and discount the value of an alternative for risk as well. This allows you to level the playing field and translate very different alternatives into net-present, risk-adjusted values.
Donald Adolphson teaches a Decision Analysis class at Brigham Young University. Every September, Professor Adolphson receives a new group of graduate students from widely-varying backgrounds, and walks them through basic Excel skills and problem statement construction. After a few months, they are completing advanced analysis projects for government, nonprofit, and private institutions. Students build a toolkit that combines business strategy, economic modeling, and even some actuarial methods. The projects this year were as varied as the students’ backgrounds: one project evaluated an array of insurance policies to minimize cost; another analyzed water usage in the university bathrooms; one team of students worked with the State of Utah to determine how best to decrease waste from plastic bags. After only one semester, it is hard to believe that some of these students studied Humanities, and had never created a spreadsheet before.
Until recently, scientists were convinced that the brain is like quick-set concrete: starting in the teenage years, neural connections become set in stone and are hard or impossible to alter. Recent research has unearthed the concept of neural plasticity, or the brain’s ability to profoundly change itself even in old age. Professor Adolphson’s course shows us that people from many different walks of life can internalize sophisticated Decision Analysis techniques and incorporate them into daily life.
 The principles referred to in this essay are found in the book Smart Choices: A Practical Guide to Making Better Decisions, by Hammond, Keeney, and Raiffa. The quantitative techniques incorporate the curriculum of Dr. Donald Adolphson from the Marriott School of Management at Brigham Young University.
 There is a substantial body of literature on the Analytical Hierarchy Process, largely developed by Mr. Saaty. An Analytical Network Process, involving more complex relationships, is also growing in use.