Two quick notes as introduction:
A couple of the author's early examples of sociological phenomena, especially the Arab Spring, appear in hindsight to have been propagated and possibly instigated by nefarious actors.
Because we try to be fashion-forward by adopting and incorporating (or at least linking to) academic research, we have a number of posts that may be 1) relevant in re: our headliner and 2) of interest to our readers. Links after the jump.
From Aeon Magazine, October 29, 2024:
Brian Klaas is an associate professor in global politics at University College London, an affiliate researcher at the University of Oxford, and a contributing writer for The Atlantic. His most recent book is Fluke: Chance, Chaos, and Why Everything We Do Matters (2024). He writes The Garden of Forking Paths Substack and created the Power Corrupts podcast.
The social world doesn’t work how we pretend it does. Too often, we are led to believe it is a structured, ordered system defined by clear rules and patterns. The economy, apparently, runs on supply-and-demand curves. Politics is a science. Even human beliefs can be charted, plotted, graphed. And using the right regression we can tame even the most baffling elements of the human condition. Within this dominant, hubristic paradigm of social science, our world is treated as one that can be understood, controlled and bent to our whims. It can’t.
Our history has been an endless but futile struggle to impose order, certainty and rationality onto a Universe defined by disorder, chance and chaos. And, in the 21st century, this tendency seems to be only increasing as calamities in the social world become more unpredictable. From 9/11 to the financial crisis, the Arab Spring to the rise of populism, and from a global pandemic to devastating wars, our modern world feels more prone to disastrous ‘shocks’ than ever before. Though we’ve got mountains of data and sophisticated models, we haven’t gotten much better at figuring out what looms around the corner. Social science has utterly failed to anticipate these bolts from the blue. In fact, most rigorous attempts to understand the social world simply ignore its chaotic quality – writing it off as ‘noise’ – so we can cram our complex reality into neater, tidier models. But when you peer closer at the underlying nature of causality, it becomes impossible to ignore the role of flukes and chance events. Shouldn’t our social models take chaos more seriously?
The problem is that social scientists don’t seem to know how to incorporate the nonlinearity of chaos. For how can disciplines such as psychology, sociology, economics and political science anticipate the world-changing effects of something as small as one consequential day of sightseeing or as ephemeral as passing clouds?
On 30 October 1926, Henry and Mabel Stimson stepped off a steam train in Kyoto, Japan and set in motion an unbroken chain of events that, two decades later, led to the deaths of 140,000 people in a city more than 300 km away.
The American couple began their short holiday in Japan’s former imperial capital by walking from the railway yard to their room at the nearby Miyako Hotel. It was autumn. The maples had turned crimson, and the ginkgo trees had burst into a golden shade of yellow. Henry chronicled a ‘beautiful day devoted to sightseeing’ in his diary.
Nineteen years later, he had become the United States Secretary of War, the chief civilian overseeing military operations in the Second World War, and would soon join a clandestine committee of soldiers and scientists tasked with deciding how to use the first atomic bomb. One Japanese city ticked several boxes: the former imperial capital. The Target Committee agreed that Kyoto must be destroyed. They drew up a tactical bombing map and decided to aim for the city’s railway yard, just around the corner from the Miyako Hotel where the Stimsons had stayed in 1926.
Stimson pleaded with the president Harry Truman not to bomb Kyoto. He sent cables in protest. The generals began referring to Kyoto as Stimson’s ‘pet city’. Eventually, Truman acquiesced, removing Kyoto from the list of targets. On 6 August 1945, Hiroshima was bombed instead.
If such random events could lead to so many deaths, how are we to predict the fates of human society?
The next atomic bomb was intended for Kokura, a city at the tip of Japan’s southern island of Kyushu. On the morning of 9 August, three days after Hiroshima was destroyed, six US B-29 bombers were launched, including the strike plane Bockscar. Around 10:45am, Bockscar prepared to release its payload. But, according to the flight log, the target ‘was obscured by heavy ground haze and smoke’. The crew decided not to risk accidentally dropping the atomic bomb in the wrong place.
Bockscar then headed for the secondary target, Nagasaki. But it, too, was obscured. Running low on fuel, the plane prepared to return to base, but a momentary break in the clouds gave the bombardier a clear view of the city. Unbeknown to anyone below, Nagasaki was bombed due to passing clouds over Kokura. To this day, the Japanese refer to ‘Kokura’s luck’ when one unknowingly escapes disaster.
Roughly 200,000 people died in the attacks on Hiroshima and Nagasaki – and not Kyoto and Kokura – largely due to one couple’s vacation two decades earlier and some passing clouds. But if such random events could lead to so many deaths and change the direction of a globally destructive war, how are we to understand or predict the fates of human society? Where, in the models of social change, are we supposed to chart the variables for travel itineraries and clouds?
In the 1970s, the British mathematician George Box quipped that ‘all models are wrong, but some are useful’. But today, many of the models we use to describe our social world are neither right nor useful. There is a better way. And it doesn’t entail a futile search for regular patterns in the maddening complexity of life. Instead, it involves learning to navigate the chaos of our social worlds.
Before the scientific revolution, humans had few ways of understanding why things happened to them. ‘Why did that storm sink our fleet?’ was a question that could be answered only with reference to gods or, later, to God. Then, in the 17th century, Isaac Newton introduced a framework where such events could be explained through natural laws. With the discovery of gravity, science turned the previously mysterious workings of the physical Universe – the changing of the tides, celestial movements, falling objects – into problems that could be investigated. Newtonian physics helped push human ideas about causality from the unknowable into the merely unknown. A world ruled by gods is fundamentally unknowable to mere mortals, but, with Newton’s equations, it became possible to imagine that our ignorance was temporary. Uncertainty could be slain with intellectual ingenuity. In 1814, for example, the French scholar Pierre-Simon Laplace published an essay that imagined the possible implications of Newton’s ideas on the limits of knowledge. Laplace used the concept of an all-knowing demon, a hypothetical entity who always knew the positions and velocities of every particle in Newton’s deterministic universe. Using this power, Laplace’s demon could process the full enormity of reality and see the future as clearly as the past.
These ideas changed how we conceived of the fundamental nature of our world. If we are the playthings of gods, then the world is fundamentally and unavoidably unruly, swayed by unseen machinations, the whims of trickster deities and their seemingly random shocks unleashed like bolts of lightning from above. But if equations are our true lords, then the world is defined by an elegant, albeit elusive, order. Unlocking the secrets of those equations would be the key to taming what only seemed unruly due to our human ignorance. And in that world of equations, reality would inevitably converge toward a series of general laws. As scientific progress advanced in the 19th and 20th centuries, Laplace’s demon became increasingly plausible. Better equations, perhaps, could lead to godlike foresight.
‘Small differences in the initial conditions produce very great ones in the final phenomena’
The search for patterns, rules and laws wasn’t limited only to the realm of physics. In biology, Darwinian principles provided a novel guide to the rise and fall of species: evolution by natural selection acted like an ordered guardrail for all life. And as the successes of the natural sciences spread, scholars who studied the dynamics of culture began to believe that the rules of biology and physics could also be used to describe the patterns of human behaviour. If there was a theoretical law for something as mysterious as gravity, perhaps there were similar rules that could be applied to the mysteries of human behaviour, too? One scholar who put such an idea in motion was the French social theorist Henri de Saint-Simon. Believing that scientific laws underpinned social behaviour, Saint-Simon proposed a more systematic, scientific approach to social organisation and governance. Social reform, he believed, would flow inexorably from scientific research. The French philosopher Auguste Comte, a contemporary of Saint-Simon and founder of the discipline of sociology, even referred to the study of human societies as ‘social physics’. It was only a matter of time, it seemed, for the French Revolution to be understood as plainly as the revolutions of the planets.
But there were wrinkles in this world of measurement and prediction, which the French mathematician Henri Poincaré anticipated in 1908: ‘it may happen that small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter.’
The first of those wrinkles was discovered by the US mathematician and meteorologist Edward Norton Lorenz. Born in 1917, Lorenz was fascinated by the weather as a young boy, but he left that interest behind in the mid-1930s when he began studying mathematics at Harvard University. During these studies, the Second World War broke out and Lorenz spotted a flyer recruiting for a weather forecasting unit. He jumped at the chance to return to his childhood fascination. As the war neared its end in 1945, Lorenz began forecasting cloud cover for bombing runs over Japan. Through this work, he started to understand the severe limitations of weather prediction – forecasting was not an exact science. And so, after the war, he returned to his mathematical studies, working on predictive weather models in the hope of giving humanity a means of more accurately glimpsing the future.
One day in 1961, while modelling the weather using a small set of variables on a simple, premodern computer, Lorenz decided to save time by restarting a simulation that had been stopped halfway through. The same simulation had been run previously, and Lorenz was running it again as part of his research. He printed the variables out, then programmed the numbers back into the machine and waited for the simulation to unfold as it had before.
At first, everything looked identical, but over time the weather patterns began to diverge dramatically. He assumed there must have been an error with the computer. After much chin-scratching and scowling over the data, Lorenz made a discovery that forever upended our understanding of systemic change. He realised that the computer printouts he had used to run the simulation were truncating the values after three decimal points: a value of 0.506127 would be printed as 0.506. His astonishing revelation was that the tiniest measurement differences – seemingly infinitesimal, meaningless rounding errors – could radically change how a weather system evolved over time. Tempests could emerge from the sixth decimal point. If Laplace’s demon were to exist, his measurements couldn’t just be nearly perfect; they would need to be flawless. Any error, even a trillionth of a percentage point off on any part of the system, would eventually make any predictions about the future futile. Lorenz had discovered chaos theory....
....MUCH MORE
Previously:
March 2013 - "The Joy of Randomness: Central Bank Strategy, Management Technique and Stock Selection":
...Ya
see, ya got your complex systems and ya got your chaotic systems and
then ya got your complex-chaotic systems like weather or the economy or
the stock market and when you endeavor at those levels of sophistication
you realize:
"Nobody knows anything"
-William Goldman
Complex systems are not to be confused with "the Caulk/Putty/Grout Complex" which was a post on trading the energy-efficiency aspects of the stimulus and is, actually, a different meaning of the word "complex".
And yes, contrary to the quote above, some people do know something, it's just that it is damn hard making money off it.
May 2013 - Marking the 50th Anniversary of Chaos Theory
October 2013 - "Physicists and the financial markets"
Markets are both complex systems and chaotic systems that have been modeled to a granularity analogous to Newton's physics. That is, the models work most of the time.
However the models based on that level of math are very, very far from emulating the real world and from time to time we are reminded of this fact.
We're still shooting for what might be called 'quantum' models but we sure aren't there yet....
November 2013 - "Using Chaos Theory to Predict and Prevent Catastrophic ‘Dragon King’ Events"
July 2014 - How to Choose With Less Than Perfect Information
October 2015 - Chaos Theory and Ecology: "A Twisted Path to Equation-Free Prediction"
Complex-chaotic systems are the shoals upon which all the models run aground....
June 2016 - Youth, Age and Fractal Complexity
July 2017 - Coin Flips and Fractals: At the Boundary Between Chaos and Order, Order Rules (eventually)
Say what? Entropy rules, dude.
April 2018 - "Machine Learning’s ‘Amazing’ Ability to Predict Chaos"
When you have one complex-chaotic system, say an ag or energy derivatives market overlaid on another complex-chaotic system, say, for example, weather; the ability to foretell the progression from the initial condition of one, or better yet both, systems would have some pecuniary advantage*
Researchers have used machine learning to predict the chaotic evolution of a model flame front.
Hey, I've made that bet! It's called "The ol' just light large-denomination banknotes on fire to avoid the hassle of feigning any type of skill or expertise in weird instruments you don't understand trade."**
May 2018 - "Google's Chaos Theory" (GOOG)
November 2019 - "Three Examples Of How Chaos Theory Affects Financial Markets"
December 2024 - "Weather Derivatives Are Booming in an Unpredictable Climate"
For when you're jonesin' for some complex/chaotic action but just can't seem to scratch that itch, superimpose one complex/chaotic system, markets, on top of another complex/chaotic system, weather, and away you go....
And one last example of what happens when you cross a butterfly flapping its wings with blogger short-sightedness/hubris, November 2019:
Ag Prices: "Disaster Avoided? Reviewing the 2019 Grain Ending Stocks Situation"
There were so many moving parts in play this year that just keeping track of things was difficult, much less forecasting.
From the wet, cold spring weather to the trade disputes to the swine fever crushing soybean demand [crushing: bean complex joke] the interplay of factors that pop-out a single end result, price, was really almost mind-boggling.
Talk about your complex-chaotic system, politics, overlaid on your complex-chaotic system, weather, overlaid on your complex-chaotic system, markets and I'm sure a few ag econ and market folks were left in the fetal position, drooling in the corner of the office....
Little did the blogger know, at that very moment, a new coronavirus was already spreading in a place called Wuhan, in China.
