See It, Measure It, Story It

Index

See It, Measure It, Story It
2 December 2025
Peter Coffee

William Thomson, perhaps better known as Lord Kelvin (as in ºK), is often quoted as having said that When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind. He wasn’t wrong, but he didn’t go far enough.

In 1883 (when Thomson said the above), it was still a novelty to have accurate numbers for a physics of mass, length, and time. The mass of the Earth had still been getting re-estimated by mountaintop measurements as recently as 1880. Several more years would pass after that before let’s synchronize our watches could become a battlefield phrase, with the 1890s advent of mass-produced timepieces. As for what might seem our simplest measures, of length, between 1919 and 1929 the estimated size of our galaxy would grow by a factor of a thousand: a whole lot more than a rounding error. In short, putting numbers on things hasn’t always been easy.

Thomson was therefore on the bleeding edge, in his time, when he said that you have scarcely, in your thoughts, advanced to the stage of science if you couldn’t offer a measurement of your topic. Today, though, that’s just a starting point, because mere numbers are now the easy part. Numbers come at us from all directions, with timeliness and precision: for example, more than 20 billion Internet-connected devices are quite ready to drown us in real-time data.

If we’re going to advance to the stage of science, this overwhelming volume of available measurements must be assembled into comprehensible stories – either to explain what’s already happening, or to offer a convincing prediction of what’s to come. Today’s sad state of both popular discussion and political posturing suggests that we’re not meeting the need to turn our numbers into compelling stories. This is why STEAM education, developing both a readiness to reason with numbers and a fluency in sharing what they mean, has to be considered a crucial component of climate-change mitigation strategy – because that’s going to require a shared understanding of the physical, political, and economic worlds.

Three news items have caught my eye this week to trigger this line of thought. The first was a comment on why trucks, rather than far more fuel-efficient trains, handle more of our freight transportation than simple math suggests they should. The second was a warning about the future prospects for math as part of the language for discussing our choices. I’ll get to the third in a moment.

First, the warped incentives favoring trucks over trains:

Highway transportation accounts for roughly 30% of all U.S. greenhouse gas emissions, with trucking representing a quarter of that total. Rail freight is four times more fuel-efficient than trucking and produces 75% less greenhouse gas emissions per ton transported. Overall, trucking is five times more expensive to society than rail – but trucking companies don’t invest in roadway infrastructure. The government handles construction, maintenance, expansion decisions, and financing. These government subsidies allow the industry to avoid the capital investment and strategic planning required of other modes while enjoying universal access to virtually every business in America. [My italics added]

This is not just about numbers. It’s about the story of why those numbers are what they are, including the history of the highway system and the political hazards of raising the fuel taxes that were initially planned to support it. Without that story, the numbers only describe the problem: they don’t illuminate any paths toward its solution.

Second, however, was this bleak assessment of the story skills required:

Five years ago, about 30 incoming freshmen at UC San Diego arrived with math skills below high-school level. Now, according to a recent report from UC San Diego faculty and administrators, that number is more than 900 – and most of those students don’t fully meet middle-school math standards… Last year, the university launched a remedial-math course that focuses entirely on concepts taught in elementary and middle school… One of the course’s tutors noted that students faced more issues with logical thinking than with math facts per se. They didn’t know how to begin solving word problems. [I added those italics, too]

Word problems. The mapping of a situation into a calculation, and the remapping of the math result into understanding – and recommendation – and decision – and action.

This leads us toward what Stephen Wolfram calls, more generally, computational thinking:

What is computational thinking? Its intellectual core is about formulating things with enough clarity, and in a systematic enough way, that one can tell a computer how to do them… Like math, it forces a certain precision and clarity of thinking. But like writing, it’s fundamentally about communicating ideas. And also like writing, it’s a fundamentally creative activity… It’s not so much about programming: it’s about what should be programmed; it’s about the overall problem of formulating things so they can be put into computational form.

That brings up the third thing that crossed my feed this week: a segment on 60 Minutes about high school students using gene editing tools to develop, it may turn out, diagnostic and treatment methods for Lyme Disease that may shrink diagnosis times from weeks to days. For starters. It moves me to quote Stephen once more, from the same source as above:

In many standard curriculum subjects, kids in school only get to do pale shadows of what professionals do. But when it comes to computational thinking, they’ve now got the same tools – and it’s now realistic for them to do the same professional-grade kinds of things.

The excuses, the I can’t do anything about a problem this big escape clauses, are being deleted from our social contract – because now we can see something, measure it, understand it, reason about how to change it, model that method and refine it, and tell the story in a way that leads people to say We can do that. Repeat. More to come.