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Tariffs and Complex Systems Simplified for Democrats. [1]
['This Content Is Not Subject To Review Daily Kos Staff Prior To Publication.']
Date: 2025-04-08
In recent weeks I've seen quite a few really smart people try to either pre-game or post-game various aspects of the Donald J Trump rolling nightmare and reality television show. Many theories are interesting and astute, many thin as a playing card. Often, they lose steam and meander to a common conclusion: nobody knows anything.
I think we can take that as a given. Nobody knows anything! Attempts at predicting logical outcomes seem doomed to failure. We do know that whatever Trump does will be shot through with cruelty, pettiness, stupidity, laziness, malignant narcissism, mendacity, self-dealing, and corruption -- that much is clear. But as for specific outcomes: how many children will die needlessly in the next two years because of Bobby Jr's CDC "improvements"? How long will it be before we, inevitably, have an ebola outbreak on US soil because we shut down USAID? Will Putin's Ukraine adventure lead to a larger land war on the European continent, with or without nukes? How long and deep will be the impact of Trump's 19th Century trade policies and what will our economy (jobs, GDP, the markets, prices ... everything) look like in 6 months, a year, 2 years? Who the fuck knows?
¯\_(ツ)_/l
But why? Why doesn't anybody know anything? There are still a few smart, inquisitive, deep and independent thinkers out there. How long that will last is anybody's guess as our academic institutions continue to capitulate and become MAGA propaganda mills. But there are good folks out there -- writing, thinking, talking, trying to make sense of the ongoing Dadaist shitshow.
But really, nobody knows fuck all. Why? Because it may all be rigorously unknowable.
Our commons is a complex system, comprised of many other interlocking and hierarchically nested complex systems. Politics, the economy, the stock markets, social networks, cities, our regulatory infrastructure, agribusiness, the climate, climate policy, public health, on and on and on: all complex systems.
What is a complex system and why should you care?
Complex systems have several properties that identify them as such.
They’re more about the relationships between parts than the parts themselves.
They are sensitive to initial conditions. This is the idea of the Butterfly Effect: small changes in inputs can give rise to unexpected, nonlinear responses.
Complex systems exhibit emergent properties: large scale attributes that arise from many small scale interactions. Wetness is an emergent property that arises from weak interactions between water molecules. Our neurons have basically two states, firing and not firing. And yet, in aggregate, this miraculous thing called consciousness results — an emergent property.
Complex systems are open to their environment. Externalities affect their state and sometimes bi-directional feedback loops are created in which the systems impact the environment, which in turn further impacts the state of the system.
Complex systems typically reside far from equilibrium (in no small part due to those pesky nonlinear, bidirectional feedback loops).
Complex systems are often in competition for scarce resources — money, time, attention.
Complex systems reside in unstable equilibria, and can easily drift into failure as internal and external conditions change.
Because of all this, complex systems defy prediction. Hindsight will not give you foresight. Understanding this is one of the keys to managing complexity.
I wrote the 2-part Medium article linked below about complexity in large-scale technology systems 6 years ago. I hope the pointy-headed stuff won't be too burdensome, because the basic ideas are universally applicable and offer a framework for understanding our current situation. I also hope that you will forgive the nods to Adobe, my corporate overlords at the time.
Moving Beyond Newtonian Reductionism in the Management of Large-Scale Distributed Systems, Part 1
Moving Beyond Newtonian Reductionism in the Management of Large-Scale Distributed Systems, Part 2
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