Day: January 18, 2026

“The unquantified life is not worth living.”

 

While quantifying one’s life is an old idea, using devices and apps to quantify the self is an ongoing trend. As a runner, I started quantifying my running life back in 1987, which is when I started keeping a daily running log. Back then, the smartest wearable was probably a Casio calculator watch, so I kept all my records on paper. In fact, I still do, as a matter of tradition.

I use my running log to track my distance, route, time, conditions, how I felt during the run, the number of times I have run in the shoes and other data. I also keep a race log and a log of my weekly mileage. So, like Ben Franklin, I was quantifying before it became cool. Like Ben, I have found this useful. Looking at my records allows me to form hypotheses about what factors contribute to injury (high mileage, hill work and lots of racing) and what results in better race times (rest and speed work). As such, I am sold on the value of quantification, at least in running.

In addition to my running, I am also a nerdcore gamer. I started with the original D&D basic set and still have shelves (and now hard drive space) devoted to games. In these games, such as Pathfinder, D&D, Call of Cthulu and World of Warcraft the characters are fully quantified. That is, the character is a set of stats such as Strength, Constitution, Dexterity, hit points, and Sanity. These games also have rules for the effects of the numbers and optimization paths. Given this background in gaming, it is not surprising that I see the quantified self as an attempt by a person to create, in effect, a character sheet for themselves. That way they can see all their stats and look for ways to optimize. As such, I get the appeal. As a philosopher I do have concerns about the quantified self and how that relates to the qualities of life, but that is a matter for another time. For now, I will focus on a brief critical look at the quantified self.

Two obvious concerns about the quantified data regarding the self (or whatever is being measured) are questions regarding the accuracy of the data and questions regarding the usefulness of the data. To use an obvious example about accuracy, there is the question of how well a wearable, such as a smart watch, really measures sleep.  In regard to usefulness, I wonder what I would garner from knowing how long I chew my food or the frequency of my urination.

The accuracy of the data is primarily a technical or engineering problem. As such, accuracy problems can be addressed with improvements in the hardware and software. Of course, until the data is known to be reasonably accurate, then it should be regarded with due skepticism.

The usefulness of the data is a somewhat subjective matter. That is, what counts as useful data will vary from person to person based on their needs and goals. For example, knowing how many steps they take at work would probably not be useful to an elite marathoner. However, someone else might find such data very useful. As might be suspected, it is easy to be buried under an avalanche of data and a challenge for anyone who wants to make use of the slew of apps and devices is to sort what would be useful in the thousands or millions of data bits they might collect.

Another concern is the reasoning applied to the data. Some devices and apps supply raw data, such as miles run or average heartrate. Others purport to offer an analysis of the data, to engage in automated reasoning. In any case, the user will need to engage in some form of reasoning to use data.

In philosophy, the two basic tools used in personal causal reasoning are derived from Mill’s classic methods. One is the method of agreement (or common thread reasoning). Using this method involves considering an effect (such as poor sleep or a knee injury) that has occurred multiple times (at least twice). The idea is to consider the factor or factors that are present each time the effect occurs and to sort through them to find the likely cause (or causes). For example, a runner might find that all her knee issues follow extensive hill work, thus suggesting the hill work as a causal factor.

The second method is the method of difference. Using this method requires at least two situations: one in which the effect has occurred and one in which it has not. The reasoning process involves considering the differences between the two situations and sorting out which factor (or factors) is the likely cause. For example, a runner might find that when he does well in a race, he always gets plenty of rest the week before. When he does poorly, he is consistently tired due to lack of sleep. This would indicate that there is a connection between rest and race performance.

There are, of course, many classic causal fallacies that serve as traps for such reasoning. One of the best known is post hoc, ergo propter hoc (after this, therefore because of this). This fallacy occurs when it is inferred that A causes B simply because A is followed by B. For example, a person might note that her device showed that she walked more stairs during the week before doing well at a 5K and uncritically infer that walking more stairs caused her to run better. There could be a connection, but it would take more evidence to support that conclusion.

Other causal reasoning errors include the aptly named ignoring a common cause (thinking that A must cause B without considering that A and B might both be the effects of C), ignoring the possibility of coincidence (thinking A causes B without considering that it is merely coincidence) and reversing causation (taking A to cause B without considering that B might have caused A).  There are, of course, the various sayings that warn about poor causal thinking, such as “correlation is not causation” and these often correlate with named errors in causal reasoning.

People vary in their ability to use causal reasoning, and this would also apply to the design of the various apps and devices that purport to inform their users about the data they gather. Obviously, the better a person is at philosophical (in this case causal) reasoning, the better they will be able to use the data.

The takeaway, then, is that there are at least three important considerations regarding the quantification of the self in regards to the data. These are the accuracy of the data, the usefulness of the data, and the quality of the reasoning (be it automated or done by the person) applied to the data.

 

One interesting narrative about the riots in Baltimore involved the concept of the rule of law. Put roughly, the rule of law is the idea that the law should govern rather than the arbitrary decisions of those in power. The notion is sometimes applied to the citizens as well, that citizens should follow the rule of law to resolve conflicts—as opposed to engaging in activities such as riots or vigilantism.

Thinkers such as John Locke have argued that the rule of law is preferable to that of the state of nature. These arguments are generally persuasive, especially since Locke emphasizes the moral responsibilities of the state in regard to the good of the people. That is, he does not simply advocate obedience to whatever the laws happen to be but requires that the laws and the leaders prove worthy of obedience. Laws or leaders that are tyrannical are not to be obeyed but are to be defied and justly so.

Since I find Locke’s arguments appealing, it is hardly surprising that I favor rule of law when the laws are good and the leaders are acting for the good of the people. When the government has moral legitimacy, the laws and the leaders have the right to expect people to follow the laws and listen to the leaders. However, when the laws or leaders violate the basic agreement, then their legitimacy evaporates.

Some conservatives spoke of the tyranny of Obama and how the Democrats wished to create a tyrannical state. They are right to be worried about tyranny. However, their timeline is in error: tyranny was already present in 2015 and has strengthened since.  

John Locke provides the following definition of “tyranny”: “Tyranny is the exercise of power beyond right, which nobody can have a right to.  And this is making use of the power any one has in his hands, not for the good of those who are under it, but for his own private separate advantage.”

The United States meets this definition. In 2014, researchers at Princeton and Northwestern conducted a study to determine the extent to which laws reflect the views of the majority versus the interests of those in power. This study, titled “Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens” , used data gathered from 1981 to 2002.

The researchers examined about 1,800 polices from that time and matched them against the preferences expressed by three classes: the average American (50th income percentile), the affluent American (the 90th percentile of income) and the large special interest groups. The results were hardly surprising: “The central point that emerges from our research is that economic elites and organized groups representing business interests have substantial independent impacts on US government policy, while mass-based interest groups and average citizens have little or no independent influence.”

While following these laws would be to conform to the rule of law, it would also be to embrace tyrannical laws crafted for the advantage of those holding power and not the good of the people.

While the people who riot are probably unfamiliar with this research, they know the obvious: they live within a political and economic system that serves the “private, separate advantage” of the elite class and has little to offer them. As such, it should be no shock that some people do not embrace the rule of such law. If they are striking out against these laws and their riots are a revolt, they are revolting against a tyrannical system, one that serves the interests of the powerful few and not the good of the people. Or to be fair to those who were critical of the riots, perhaps they are just breaking things.

Continuing with tyranny, Locke notes that “Where-ever law ends, tyranny begins, if the law be transgressed to another’s harm; and whosoever in authority exceeds the power given him by the law, and makes use of the force he has under his command, to compass that upon the subject, which the law allows not, ceases in that to be a magistrate; and, acting without authority, may be opposed, as any other man, who by force invades the right of another.”

Sadly, this is an accurate description of the excessive use of force against citizens by some police officers. Baltimore, as has been widely reported, has paid out millions of dollars in settlements due to the wrongful use of force by police against citizens. As folks on the right love to say, not all police officers are bad and there are excellent officers. However, even a cursory examination of the problems with policing in American cities shows that Locke’s definition of tyranny is routinely met. As such, it is evident that the rule of law was already broken well before the riots. And is being shattered in 2025.

While Locke did not use this phrase, the rule of law is a two-way street and those who are charged with enforcing the law must also obey that law, otherwise it would be unreasonable to expect obedience from the citizens. As such, the most obvious step to restoring rule of law is to ensure that those charged with enforcing the laws are also following the laws. This was true in 2015 and is still true today.