Life is Simple – an appealing and comforting concept, that is both the title and central tenet of a recent popular science book by Johnjoe McFadden. Focused first on William of Occam (~1287-1347) and the concept now known as Occam’s Razor, the books subtitle, How Occam’s Razor Set Science Free and Shapes the Universe, captures the scope of the influence that McFadden ascribes to Occam. McFadden cites Occam as the first to establish simplicity as a core goal of scientific inquiry, and then follows the influence of this simple idea through a detailed history of advances in physics from our understanding of the cosmos down to the subatomic structure of matter.
Occam was an original and revolutionary thinker who confronted both the philosophical and theological barriers of his time by positing that explanations for physical phenomena should not be more complicated than necessary. He also would separate science, as the observation of physical realities, from religion, which operates on faith. The Catholic Church at the time was not amused and Occam’s life was not an easy one. But then he also once labeled the sitting pope as a heretic!
Occam’s texts are in Latin but one frequent translation of his central concept is:
"Entities are not to be multiplied without necessity.”
A simple and powerful statement; explain a set of measurements with as few parameters, or entities, as possible.
McFadden credits Occam with being the first to step away from the complex and unsupportable views of the universe provided by classical philosophers (e.g. crystal spheres and the like) and follows the trail of “Occamists” across several centuries. He credits Occam with influencing Copernicus, Kepler, Galileo and Newton, and on even to Bertrand Russel and Einstein.
At every step, McFadden links major advances in understanding of the physical world with the derivation of simpler models. Conversely, when something cannot be predicted accurately, McFadden says that scientists or philosophers tend to add complexity to the formulation until predictions match the data. Those additional “entitles” may have no basis in reality but just force the resulting “model” to fit the data.
Life is Simple is a very good read, following a consistent thread of improved understanding by the elimination of unnecessary and insupportable explanations. McFadden’s grasp of the major steps and major players involved in developing our current understanding of the physical world is nearly encyclopedic.
But this Substack site is about weather, climate and climate change, so what is the connection to a 700-year-old philosophy?
A somewhat older book (but new to me) comes at the notion of overly-complex explanations from another angle: the building and application of complex computer models. Aptly titled Useless Arithmetic – Why Environmental Scientist Can’t Predict the Future, the authors (Orrin H. Pilkey and Linda Pilkey-Jarvis) go on at some length about complex models and the study of complexity as a phenomenon and the potential for making models say whatever the builders want. Essentially, if you have enough variables to play with, you can make the model do anything.
The idea of “fudged” models is one of the more effective arguments against predictions of future climates – effective at least with doubters and the general public. Many years ago I added to this debate in a different setting with an editorial in a professional journal bemoaning loose thinking among ecosystem modelers and supporting this same idea - if you have more variables than observations, you can always make the model produce the observations.
I have been a modeler for my entire academic career, so that editorial opinion was not coming from an outsider, nor was it a knock against the value of models. Good ones are working hypotheses of how a scientific community understands the system they study. The evolving climate models highlighted in all of the successive IPCC reports are state-of-the-art, detailed representations of the complex climate system.
But I would propose that “necessity” as Occam has it varies with the purpose of the project. When we need to understand and predict the future of the climate system, models may need to achieve a level of complexity that is essentially impossible for those outside the community to understand. Constant validation (comparison of model predictions against measurements not used to build the model) is our only simple method for evaluating these models. Useless Arithmetic unfortunately convolutes calibration (modifying the parameters to get the best fit to the data) and validation (comparing model predictions with independent measurements) and misses ways in which even complex models can be challenged and validated.
And I would further propose that when the purpose of the model is to help explain to general audiences why the climate is changing and where we are all headed, we can trim the necessities way back.
The very first substantive essay in this Substack series was titled “Climate Change in Four Easy Steps.” That essay, and actually this entire series, arose from the difficulty in making a helpful and informative presentation to general audiences about how we are changing the climate system, and where we are headed, when I had to rely on “this is what the climate models predict.”
In an effort parallel perhaps to McFadden’s, I dove into older research on greenhouse gases and climate and was amazed to re-learn just how long we have known about the interactions among them.
Without repeating the details of that first essay, the four steps, and their historical sequence, are:
1850s – Eunice Foote and John Tyndall separately discover that carbon dioxide is an efficient absorber of infrared radiation (i.e. a greenhouse gas) and speculate that without this gas and others in the atmosphere, the Earth would be much colder than it is.
1908 – Nobel Laureate Svante Arrhenius publishes Worlds in the Making, a book written for general audiences that includes a series of very modern statements about the global carbon cycle and the impact of carbon dioxide in the atmosphere on global temperature. Based on a year of painstaking hand calculations completed in 1896, he predicts that a doubling of carbon dioxide in the atmosphere would increase global temperatures by 4 degrees Celsius. A classic Occam’s Razor outcome.
1956 to the present – Measurements of the carbon dioxide concentration in the atmosphere begun by Charles Keeling at a field station atop Mauna Loa in Hawaii document a consistent increase from less than 320 parts per million to now approaching 420 parts per million. In John Tyndall’s time, the concentration was closer to 280 parts per million.
Current – the Goddard Institute for Space Science is the keeper of a meticulously developed data set of change in temperature globally and by region.
This temperature record becomes relevant to Occam’s Razor and simpler presentations of climate change when combined with a one-parameter equation that has been used to capture Arrhenius’ findings. In the figure on the right, the line is predicted change in global temperature using that one-parameter model and carbon dioxide as the only predicting variable. The points are from the Goddard temperature data set. More details on this are provided in another recent essay, but these four steps offer a simpler connection between our generation of greenhouse gases and our changing climate. Occam might be pleased.
This is not to say that carbon dioxide is the only important greenhouse gas. The second essay in this series explains why it is such an effective index to temperature increase, and also when the relationship in that figure might break down.
So I come down on McFadden’s side of the complexity issue and admire Occam’s very simple statement in support of simplicity (a very nice recursion). Separating the goal of predicting the future of the complex climate system from the goal of presenting the essentials of that system to a general audience leads to different definitions of “necessities” and different models.
I have long been an admirer of the kind of simpler explanations and solutions proposed by Occam’s Razor, so this essay concludes with three of my favorite older examples.
In Longitude, Dava Sobel brilliantly describes the quest for a method to determine longitude on sailing ships at sea. In the 1700s, latitude was easily and routinely measured by determining the height of the sun at noon (think of those elegant sextants) and knowing the day of the year. Longitude had no such easy measure.
The simplest solution for longitude is knowing when noon occurs at a fixed point, like Greenwich, England, and at your current location. Sobel tells the tale of the elaborate and intellectually challenging methods proposed, involving the motions of the moon and Venus or the invention of very elaborate clocks that could keep time accurately under the turbulent conditions at sea (which standard pendulum clocks of that era could not).
The winner, after much intrigue and conflict, which makes for great reading, was a watchmaker who perfected a pocket watch that was accurate enough over long enough periods of time to keep track of “Greenwich Mean Time” for extended periods at sea, even under turbulent conditions. A simple and elegant solution.
A second example takes us to J. E. Lovelock’s Gaia. In the original version (1979), before the concept became mired by others in all kinds of teleological and pseudo-science rigmarole, Lovelock offers a simple planetary definition of life. His definition is the use of the sun’s energy to produce and maintain chemical disequilibrium in the atmosphere. Sounds theoretical, but the idea had important implications.
As Lovelock tells it, NASA was in the throes of developing a program to determine whether or not life existed on Mars. Much effort was going into developing landers that could sample Martian soil, do basic biological and chemical tests, and look for compounds that would be produced by living organisms, at least as we know them on our home planet.
Lovelock said that, based on the fact that the Martian atmosphere was at chemical equilibrium (unlike our own atmosphere dominated by nitrogen and rich in oxygen, the product of photosynthesis) that life did not exist there. Another simple and elegant solution. Lovelock was not invited to continue to work on the project.
A final example comes from the early days of modern biology. Lewis Thomas presented formative work on immunology and disease in The Youngest Science and The Lives of a Cell. One point he made has remained with me. When we do not understand a disease and can only treat the symptoms, those treatments can be laborious and complex. When we do understand a disease, and the organism that causes it, we can develop a vaccine that inhibits the cause. A simple shot replaces those complex therapies. Our ongoing experience with COVID-19 suggests that this generalization still holds.
Sources
McFaddens book is: McFadden, J. 2021. Life is Simple: How Occam’s Razor Set Science Free and Shapes the Universe. Basic Books. New York
The full citation for Useless Arithmetic is: Pilkey, O.H. and L. Pilkey-Jarvis. 2007. Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future. Columbia University Press. New York
My editorial comment on modeling can be found here:
Aber, J.D. 1997. Why don’t we believe the models? Bulletin of the Ecological Society of America vol. 78, pp. 232-233
A more complete treatment of “Four Steps” is here
The figure in the text and the sources from which it is derived are included in that essay as well.
And the follow-on discussion of why carbon dioxide is such a good predictor of changes in temperature, and when it might not be, is here
IPCC stands for Intergovernmental Panel on Climate Change, and their voluminous reports can be found here: https://www.ipcc.ch/
The four older books cited as excellent examples of the value of simple solutions are:
Dava Sobel. 1995. Longitude: The True Story of a Lone Genius Who Solved the Greatest Scientific Problem of His Time. Bloomsbury USA. New York
James Lovelock. 1979. Gaia: A New Look at Life on Earth. Oxford University Press. Oxford
Lewis Thomas. 1974. The Lives of a Cell: Notes of a Biology Watcher. Penguin Books. New York
and 1983. The Youngest Science: Notes of a Medicine Watcher. Penguin Books. New York