Organizations are often disappointed with the results of key institutional decisions. Local communities are no exception. Bad results are not always the result of bad luck. High impact decisions are often made without even having the benefit of writing anything about the decision process. The point is that many important decisions are insufficiently analyzed and too influenced by personal prejudices. Lack of analysis is often correlated with bad decisions.
You can make a big decision and still have an unfortunate result. Conversely, bad decision-making processes are often bought out by decision-makers with good intuition or just luck. Everyone probably has personal experience with high impact decisions made by simply taking the gut reaction of someone in a position of authority. People with significant authority often have above average intuition. A little analysis, however, can often improve an above-average hunch. While analysis can’t improve your luck, better long-term decision-making processes will lead to better results, greater organizational effectiveness, and even make you look luckier.
Commercial and industrial engineering students are regularly trained in the concepts of decision analysis (DA). DA uses analytical tools such as linear programming and Monte Carlo simulation to solve decision problems. Unfortunately, many other programs neglect these topics entirely. Due to this neglect, many top managers have never been exposed to the concepts of AD. As a result, many organizations that could benefit from it have not integrated AD into their routine business processes.
The discipline of DA has various names. It is a subset of operations research or management science. DA was considered high tech in the 1970s, but it is much more widely used today. Many organizations incorporate DA into their decision policies. However, the use of AD is far from universal. Most organizations could benefit from a larger DA application. This includes government agencies.
A good decision is usually to select the alternative with the greatest likelihood of obtaining the desired results. Good decisions will usually appear rational after careful consideration. Cognitive psychologists consider two types of rationality which are both necessary for a decision to be rational. These are epistemic and instrumental rationality. Epistemic rationality refers to the degree to which the decision maker’s beliefs about the decision context match reality. Instrumental rationality is the degree to which a chosen course of action is likely to achieve the desired result. In the absence of epistemic or instrumental rationality, a decision can be considered irrational. There are countless case studies where, upon analysis, organizational decisions seem self-frustrating and irrational.
Much of this irrationality is due to the psychology of individual decision makers. The field of behavioral economics began with the publication in 1979 of an article by psychologists Daniel Kahneman and Amos Tversky entitled “Perspective Theory: An Analysis of Decision Under Risk”. This article ultimately led Kahneman to receive the Nobel Prize for Economics in 2002. In the article, Kahneman and Tversky experimentally document how the subject’s economic decisions differ significantly from the utility maximization rule of classical microeconomics. As the field of behavioral economics developed, more and more models of human decision-making experimentally emerged that appeared to violate rationality. These bizarre decision-making patterns, or cognitive biases, are experimentally reproducible, violate rationality, and appear to be the norm to some extent with every copy of the human brain.
Bias are powerful psychological constructs that can interfere with an individual’s ability to objectively process information and make optimal decisions. Our intuition is often negatively influenced by cognitive biases. Elected and appointed public officials are not exempt and democratic processes do not always filter out prejudices. These cognitive biases can lead to bad decisions that waste limited public resources. DA can help strengthen public decision-making and maximize the benefits we derive from public spending.
DA begins by systematically breaking down a decision in a way that allows for further analysis. Most AD academic problems use numerical methods to identify alternatives that will optimize the desired outcome. To mathematically solve decision problems, all aspects must be quantified. That said, decisions don’t need to be mathematically optimized to benefit from AD. Just doing the first step in problem definition can bring significant benefits and improve the quality of decisions.
The decision decomposition process begins with the creation of a problem statement. Once done, the different elements of a decision problem can be unmasked. The key elements of a decision problem are the objective, the alternatives, the constraints, the trade-offs, the consequences, the risks and the uncertainties. The simple act of separating these elements can often provide significant insight into the context of the decision.
Once the decision has been broken down, it is easy to list the evaluation criteria. A table can be created where the criteria are in the first column and another column is created for each alternative discussing or rating each alternative based on those criteria. This is called a compromise table or a compromise matrix. Often, this simple exposure can greatly illuminate the path of decision-makers.
The analysis can simply be a list of the pros and cons of a particular course of action. In fact, Benjamin Franklin recommended this approach in a letter of 1772. Franklin recommends thinking about a particular alternative and listing exhaustively the pros and cons of this course of action. Franklin then recommends removing the pros and cons in pairs of roughly equal weight. Once the numbers of advantages and disadvantages are thus reduced, the analysis is greatly simplified.
There are obviously low impact, low complexity decisions for which a formal DA process would not be warranted. DA is best suited for larger decisions or repetitive decisions where the overall impact is large. For the DA process to add value, the marginal benefit of a better decision must exceed the cost of the DA process. It should be borne in mind, however, that simply taking the first steps to break down the problem or applying Benjamin Franklin’s method can dramatically improve the outcome of the decision. It won’t make you luckier, you will just appear that way.
Jeff H. Edmonds, PE has nearly 35 years of professional experience related to the management, construction and design of public works. Edmonds has been the Director of Engineering Services at the City of Corpus Christi, Texas, since 2015.