What Ben Franklin Got Right (and Wrong) about Data-driven Decision-Making
Data is only as useful as the decision-making process. After all, what good is a mountain of data if the ultimate decision is based on in-the-moment reactions and emotion. While simple logic is often useful to make simple decisions, more complex problems can require a careful decision scheme.
Decision-making processes like this have been discussed for centuries. One of the most famous versions of this was described in a letter from Benjamin Franklin to his friend, Joseph Priestley in 1772. To quote his own hard-to-read letter:
"To get over this, my Way is, to divide half a Sheet of Paper by a Line into two Columns, writing over the one Pro, and over the other Con. Then during three or four Days Consideration I put down under the different Heads short Hints of the different Motives that at different Times occur to me for or against the Measure. When I have thus got them all together in one View, I endeavor to estimate their respective Weights; and where I find two, one on each side, that seem equal, I strike them both out: If I find a Reason pro equal to some two Reasons con, I strike out the three. If I judge some two Reasons con equal to some three Reasons pro, I strike out the five; and thus proceeding I find at length where the Ballance lies; and if after a Day or two of farther Consideration nothing new that is of Importance occurs on either side, I come to a Determination accordingly."
Franklin called this system Moral Algebra. He was convinced the best way to reach an optimal solution in a tough decision was to list pros and cons, weight them according to importance, and strike out reasons of equal value. His entire decision process took several days.
Versions of this process adapted from Franklin and others are precursors to the fields of decision theory. Decision theory uses many tools to create decision-making models under a variety of constraints. These constraints include uncertainty of all options, missing information, and decision risk. While Franklin’s decision scheme is not as scientific as statistical decision theory, it does have the advantage of being very practical. Yet, that doesn’t mean that it can’t be improved.
To start, Franklin’s system considers pros and cons for a single decision, and not multiple options. In cases in which you may have more options than “do or do not,” his system becomes clumsy. Many decisions can be either-or, binary decisions; but often the toughest of calls are due to a variety of options. In these situations, people have far more difficulty coming to a final decision. It’s a problem of analysis paralysis. This is why it’s easier to decide on what to order at a restaurant with a smaller menu than one with a large list of items.
Second, Franklin doesn’t provide any direction on how or why to weigh considerations. Considerations should be weighed because reasons for a decision are not always equal. Should I buy that car? In this consideration the price is likely more important than the car's leather seats. But how to weigh the decisions? There are two ways that are applied most
Scale of 1 to 10 – On a scale of 1 to 10, how much do you care about this decision?
Percentage – What percent importance would you give this factor in your final decision?
I prefer percentage in my own personal decisions. This is because the percentages allow the final decision score to be a small number. Smaller numbers are easier to compare and understand. Plus, if you use a 5-point decision scale (which I recommend) then the final decisions will always be out of 5.
Finally, Franklin’s process doesn’t say how to finally score decisions. He notes that pros and cons of equal weight cancel one another. But this eliminates the ability to record an overall numerical score for all options. For example, if Option A is a 3 and Option B is a 5, then Option B is the clear winner. However, if Option A instead had a score of 4.75, the decisions are quite close. This means you may need to reconsideration and take time to think over such a close call.
Now that I have spent paragraphs criticizing a Founding Father, I will now present my decision scheme. I have used this exact process for over a decade to decide where to move, when to buy a house, what college to attend, and what job to take.
First, ditch the notion of pros and cons. To create the most objective decision-making scheme, all decisions should be judged on the same principles and considerations. If deciding between two job opportunities, instead of listing “higher salary” as a pro for one option, list salary as a variable for both and then create a score.
The score is determined by giving every option a score between 1 and 5 for every variable in the decision. If something is perfect, give it a 5. If something is terrible, give it a 1. Next, weight every variable using the percentage. The total percent after weighting all considerations should equal 100%. So, for example, if I am moving to either Texas or California, I might decide that 50% of the decision is dependent on salary. Multiply the weights by the scores for all options. For example: Score of Option A’s Salary (3) x Salary weight (30% or 0.30) = Weighted Option A (0.9). Now sum the weighted scores for each column.
Your final list should look something like this:
This example is a decision matrix for deciding between three job opportunities, A, B, and C. Notice that all the important considerations are listed followed by score out of 5, weights, and the weighted score for those considerations. The bottom right shows the final scores for each option out of 5. In this case, option B was the best choice with a 3.3.
This system also allows for easy understanding of how close an option is to your own ideal option. For example, if a magical job opportunity scored all 5’s, then this options final weighted score would be 5. You can (roughly) say then that a score of 3.3/5 is 66% of an ideal job situation. This may seem low, but I highly doubt anyone ever reaches the ideal state in a decision. If you did find that perfect job, you shouldn’t have to do this decision-making process at all!
Why should you care? You can use this process in both personal and business decisions. Franklin routinely used his own Moral Algebra in business as well as public decisions. As stated in the beginning, data is only important if your decision and management techniques are solid. This decision-making process is a way to take complex emotions, beliefs and opinions, and boil them down into a useful formula. Of course, people have many preferences for how to make decisions. If a more intuitive (and less numerical) method is your style, you might try your usual approach and this one and see how they compare.
To understand data and research is one thing, but its only as good as the decision-making process employed. Without a well-established scheme to reach the best option, all the data in the world will be useless.
Franklin’s original letter can be found here: