Some decisions are fairly straightforward to make. This is true when the number of factors is limited and when their contributions are well defined. Other decisions, by contrast, involve more factors which contributions are less clear. It is also possible that the desired outcomes or actions leading to or resulting from a decision are not well defined, or if there is disagreement among the stakeholders.
The process of making decisions in similar the entire life cycle of a business analysis engagement — writ small. It involves some of the same steps, including defining the problem, identifying alternatives, evaluating alternatives, choosing the alternative to implement, and implementing the chosen alternative. The decision-makers and decision criteria should also be identified.
Let’s look at a few methods of making multi-factor decisions at increasing levels of complexity. It is generally best to apply the simplest possible method that can yield a reasonably effective decision, because more time and effort is required as the complexity of analysis increases. I have worked on long and expensive programs to build and apply simulations to support decisions of various kinds. Simulations and other algorithms themselves vary in complexity, and using or making more approachable and streamlined tools makes them more accessible, but one should still be sure to apply the most appropriate tool for a job.
- Pros vs. Cons Analysis: This simple approach involves identifying points for and against each alternative, and choosing the one with the most pros, fewest cons, or some combination. This is a very flat approach.
- Force Field Analysis: This is essentially a weighted form of the pro/con analysis. In this case each alternative is given a score within an agreed-upon scale for the pro or con side, and the scores are added for each option. This method is called a force field analysis because it is sometimes drawn as a (horizontal or vertical) wall or barrier with arrows of different lengths or widths pushing against it perpendicularly from either side, with larger arrows indicating considerations with more weight. The side with the greatest total weight of arrows “wins” the decision.
- Decision Matrices: A simple form of the decision matrix assigns scores to multiple criteria for each option and adds them up to select the preferred alternative (presumably the one with the highest score). A weighted decision matrix does the same thing, but multiplies the individual criteria scores by factor weightings. A combination of these techniques was used to compile the ratings for the comparative livability of American cities in the 1984 Places Rated Almanac. See further discussion of this below.
- Decision Tables: This technique involves defining groups of values and the decisions to be made given different combinations of them. The input values are laid out in tables and are very amenable to being automated though a series of simple operations in computer code.
- Decision Trees: Directed, tree structures are constructed where internal nodes represent mathematically definable sub-decisions and terminal nodes represent end results for the overall decision. The process incorporates a number of values that serve as parameters for the comparisons, and another set of working values that are compared in each step of the process.
- Comparison Analysis: This is mentioned in the BABOK but not described. A little poking around on the web should give some insights, but I didn’t locate a single clear and consistent description for guidance.
- Analytic Hierarchy Process (AHP): Numerous comparisons are made by multiple participants of options that are hierarchically ranked by priority across potentially multiple considerations.
- Totally-Partially-Not: This identifies which actions or responsibilities are within a working entity’s control. An activity is totally within, say, a department’s control, partially within its control, or not at all in its control. This helps pinpoint the true responsibilities and capabilities related to the activity, which in turn can guide how to address it.
- Multi-Criteria Decision Analysis (MCDA): An entire field of study has grown up around the study of complex, multiple-criteria problems, mostly beginning in the 1970s. Such problems are characterized by conflicting preferences and other tradeoffs, and ambiguities in the decision and criteria spaces (i.e., input and output spaces).
- Algorithms and Simulations: Must of the material on this website discusses applications of mathematical modeling and computer simulation. There are many, many subdisciplines within this field, of which the discrete-event, stochastic simulations using Monte Carlo techniques I have worked on is just one.
- Tradespace Analysis: Most of the above methods of analysis involve evaluating trade-offs between conflicting criteria, so there is a need to balance multiple considerations. It is often true, especially for complex decisions, that there isn’t a single optimal solution to a problem. And in any case there may not be time and resources to make the best available decision, so these methods provide a way to at least bring some consideration and rationality to the process. Decision-making is ultimately an entrepreneurial function (making decisions under conditions of uncertainty).
The Places Rated Almanac
I’ve lived in a lot of places in my life for I consider Pittsburgh to be my “spiritual” hometown. I spent many formative years and working years there and I have a great love for the city, even against my understanding of its problems. So, I and other Pittsburghers were shocked and delighted when the initial, 1985 edition of Rand McNally’s Places Rated Almanac (see also here) rated our city as the most livable in America. Not that we didn’t love it, and not that it doesn’t have its charms, but it pointed out a few potential issues with ranking things like this.
The initial work ranked the 329 largest metropolitan area in the United States on nine different categories including ambience, housing, jobs, crime, transportation, education, health care, recreation, and climate. Pittsburgh scores well on health care because it has a ton of hospitals and a decent amount of important research happens there (much of it driven by the University of Pittsburgh). It similarly gets good score for education, probably driven by Pitt and Carnegie Mellon, among many other alternatives. I can’t remember what scores it got for transportation, but I can tell you that the topography of the place makes navigation a nightmare. Getting from place to place involves as much art as science, and often a whoooole lot of patience.
It also gets high marks for climate, even though its winters can be long, leaden, gray, mucky, and dreary. So why is that? It turns out that the authors assigned scores that favored mean temperatures closest to 65 degrees, and probably favored middling amounts of precipitation as well. Pittsburgh happens to have a mean temperature of about 65 degrees, alright, but it can be much hotter in the summer and much colder in the winter. San Francisco, which ranked second or third overall in that first edition, also has a mean temperature of about 65 degrees, but the temperature is very consistent throughout the year. So which environment would you prefer, and how do you capture it in a single metric? Alternatively, how might you create multiple metrics representing different and more nuance evaluation criteria? How might you perform different analyses in all nine areas than what the authors did?
If I recall correctly, the authors also weighted the nine factors equally, but provided a worksheet in an appendix that allowed readers to assign different weights to the criteria they felt might be more important. I don’t know if it supported re-scoring individual areas for different preferences. I can tell you, for example, that the weather where I currently live in central Florida is a lot warmer and a lot sunnier and a lot less snowy than in Pittsburgh, and I much prefer the weather here.
Many editions of the book were printed, in which the criteria were continually reevaluated, and that resulted in modest reshufflings of the rankings over time. Pittsburgh continued to place highly in subsequent editions, but I’m not sure it was ever judged to be number one again. More cities were added to the list over the years as different towns grew beyond the lower threshold of population required for inclusion in the survey. Interestingly, the last-place finisher in the initial ranking was Yuba City, California, prompting its officials to observe, “Yuba City isn’t evil, it just isn’t much.”
One thing you can do with methods used to make decisions is to grind though your chosen process to generate an analytic result, and then step back and see how you “feel” about it. This may be appropriate in personal decisions like choosing a place to live, but might lead to bad outcomes for public competitions with announced criteria and scoring systems that should be adhered to.