Each Sunday I compose a list containing the most thought-provoking and interesting content I’ve consumed during the past week. Primarily as a way to keep inventory of material that influenced me and my way of thinking.
Papers/Notes
Risk Update: May 2024 – “Polanyi’s Paradox” by Convex Strategies.
To the everyday consumer, the enthusiasm of the ECB for their success in curtailing ‘inflation’, is not particularly shared. Ms. Lagarde mentioned multiple times in her responses that they have now twice “divided inflation by half”, with their rate hikes bringing it from its annualized peak of 10.6% down to 5.2%, then subsequently through holding rates steady at restrictive levels halving it again to 2.6%. We can’t help but note their own acclaim that it was their policy that is restoring price stability, but it was not as a result of their policies that price stability was previously destroyed. Back to the everyday consumer, far less concerned about the niceties of yoy% changes smoothed and averaged over time, and their view of things tracking the persistent increase in their cost of living. Hard to convince them that it all amounts to a job well done.
Coincidentally, and very much to our pleasure, we were able to attend a dinner last month where the keynote address was delivered by none other than MIT labor economist David Autor. For those not aware, Mr. Autor is credited with coining the name Polanyi’s paradox. “We can know more than we can tell.” Polanyi’s paradox.
This aligns directly with Polanyi’s premise of tacit knowledge from his 1958 works “Personal Knowledge”. Tacit knowledge are things that we, as humans, can know but aren’t able to explicitly explain. Mr. Autor gave a wonderful presentation on the implications of automation, and more particularly AI, on the labour force. Referencing Polanyi’s paradox, Mr. Autor noted its implication as “we cannot ‘computerize’ tasks that we don’t explicitly understand”. We have taken it upon ourselves to coin our own adaptation of Polanyi’s paradox as relates to the current versions (eg. LLMs) of AI. Please feel free to suggest naming alternatives! Convex AI version of Polanyi’s paradox: “It can tell more than it can know.”
Writings/Essays
Chaos and Cause by Erik Van Aken.
Classical physics not only presented an orderly universe among Newton’s followers, but it also instilled a profound sense of mastery over the natural world. Newton’s discoveries fostered the belief that the Universe, previously shrouded in mystery, was now laid bare, sparking an unprecedented optimism in the power of science. Armed with Newton’s laws and revolutionary mathematics, leading thinkers felt they had finally unlocked the secrets of reality.
Like the other two pillars Gleick identified, chaos theory challenges our understanding of classical physics. However, unlike QM and relativity, chaos theory operates within a Newtonian framework – it assumes a deterministic reality governed by specific laws. Yet chaos theory reveals a beguiling level of unpredictability, particularly at a macroscopic level. The unpredictability revealed by chaos theory, seemingly at odds with a deterministic worldview, arises from the complex nature of nonlinear systems. In dynamical systems, behaviour changes over time. The concept of determinism implies that future states are precisely determined by current conditions, without any randomness or chance involved. However, when dynamical systems exhibit nonlinearity, their behaviour becomes more complex and less predictable. This complexity arises from a disproportionate relationship between input or cause and output or effect.
In broader mathematical and scientific terms, ‘chaos’ refers to systems that appear random yet are inherently deterministic. Take the example of a roulette wheel, commonly perceived as a game of chance. While we might assume the outcome is purely random, the underlying mechanics of the roulette wheel, including the motion, friction and the force of the spin, adhere to deterministic physical laws. The true source of unpredictability stems from its extreme sensitivity to initial conditions: how forcefully the ball is dropped, the speed at which the wheel spins, subtle vibrations from environmental factors like an air conditioner, and even the movement of patrons around the table. These factors, often unnoticed, can significantly influence the outcome of each spin. Chaos theory teaches us that even seemingly insignificant variations in initial conditions – a fraction of a millimetre difference in the ball’s drop point – can lead to disproportionately large effects.
For want of a nail the shoe was lost. For want of a shoe the horse was lost. For want of a horse the rider was lost. For want of a rider the battle was lost. For want of a battle the kingdom was lost.
The chaotic nature of nonlinear systems impacts more than just mathematics. For instance, small genetic mutations or environmental changes in biological evolution can lead to significant evolutionary shifts over time. The path of evolution is not linear or predictable; instead, it is full of unexpected twists and turns, like the movement of a pebble down the mountain. Similarly, in economics, markets function as complex, nonlinear systems. Rumours about a company or slight changes in interest rates can act as triggers, setting off substantial and unanticipated shifts. The 2007-08 financial crisis provides a sobering reminder that minor perturbations in one sector can ripple into a global meltdown.
Bertrand Russell’s arguments in ‘On the Notion of Cause’ (1912-13) complicate the picture of causality in physics even further. Russell attacks the idea of cause and effect altogether. In essence, he argues that if A produces B, and A encompasses the environment (the past light cone of A), this broadens the scope of event A to such an extent that it becomes essentially unrepeatable. Russell’s argument leads us to a dilemma: to uphold the law of causality, we must define events by noting invariable uniformities and by abstracting away most of the physical influences on A. Yet, this abstraction may inadvertently exclude causal influences, undermining the principle of causality. Thus, Russell asserts two significant conclusions: first, that our conventional notion of causality is not grounded in physics; and second, if notions like ‘cause’ must be reducible to physics, we should eliminate our use of the term ‘cause’. According to Russell, there is no cause and effect at all.
. . . we can overcome Russell’s problem, the problem of causation in physics, by shifting our perspective. If we look at the world through the ordinary lens of human agency, rather than the lens of physics, we can talk about causes as handles, events within nature that make a difference to some effect and provide us with a sense of control. . . . When we shift our perspective from physics to agency and difference-making, we land on the most intuitive assessment of the butterfly effect. From our perspective, the butterfly is not a cause of the storm because we cannot affect storms by manipulating butterflies. And while the butterfly could have an effect on a storm, it does not make a difference to the occurrence of storms in a way that we can predict or control. Exploring the dichotomy between the perspectives of physics and human agency uncovers a paradox: our actions are simultaneously bound by the determinism of physical laws and enriched with intention, purpose and meaning that go beyond them.
Trump Conviction Pushes Up the 2024 Election Stakes by Neil Howe.
By defying the court outright, and by not cravenly crawling toward some misdemeanor escape hatch, Trump was telling his supporters: I’m all in, be all in with me. We conquer or die together. Like Hernán Cortés setting off from Veracruz (forgive me for grabbing yet another metaphor), he has burned his ships on the beach so that no one can contemplate retreat. If we lose, sure, we all live under tyranny—but I go to prison. Now that’s how a candidate fires up his core.
Podcasts/Conversations
Jeff Currie: Metals, Energy, Commodity Super Cycle & More, MacroVoices.
There’s No Crying In Correlation, Alpha Exchange.
What the Market Is Under Appreciating with Ben Rabidoux, The Market Huddle.
Long vol, short prediction models 