Welcome, fellow adventurers, to the wild and wacky world of complex systems – where the ordinary becomes extraordinary, the predictable turns into the unpredictable, and chaos reigns supreme! Buckle up, because we're about to embark on a journey from the bustling colonies of ants to the untamed wilderness of Yellowstone National Park, all while cracking a few jokes along the way.
Our journey begins with a colony of ants, those industrious little critters buzzing about with all the energy of a hyperactive kid on a sugar rush. Picture this: tiny ants scurrying around, each with its own task – foraging for food, building mounds, and generally making a mess of things. But amidst the chaos, there's always that one slacker ant, lounging around like it's on a permanent vacation. Hey, we all know someone like that, right?
Now, let's zoom out and take a look at the big picture. Despite the antics of our lazy friend, the ant colony somehow manages to function like a well-oiled machine, with emergent properties that defy logic and leave us scratching our heads in wonder. It's like watching a sitcom where the characters constantly bicker and argue, but somehow manage to come together in the end – ants, they're just like us!
Next stop: "Starting in 1870, wolves in Yellowstone National Park were hunted by park employees in an attempt to eradicate all predators from the park. The idea being that by removing the top predators, the ecosystem as a whole should thrive. By 1926, the last wolf pack of Yellowstone had been killed.
Fast forward to Yellowstone in 1995: deer everywhere. Deer around every corner, deer destroying the vegetation, deer [expletive] on the path as you were trying to walk around. Recognizing that this deer [expletive] fest simply wasn't sustainable (yeah, that's a sentence I never thought I'd say), the US government realized that in order to control the deer population, the proper course of action would be to release more wolves into the park. Wolves, being the natural predators of the deer, would help regulate the population and thus rebalance the ecosystem. So in 1995, 14 wolves were released into Yellowstone.
However, over time, something really interesting started to happen. As the wolf population grew, deer started to avoid certain areas of the park—the riverbanks and valleys that had been destroyed by overgrazing deer began to regenerate. Beavers came back to the rivers. There was an explosion of mice, hawks, foxes, and bears.
One tiny adjustment in the system's conditions caused a profound change. However, something even more remarkable happened: as trees regenerated along the riverbanks, their roots stabilized the soil, protecting against erosion, narrowing the channels, and in some cases, quite literally changing the path of the river."
"So you're saying that bringing 14 wolves into Yellowstone changed the actual path of the rivers?"
"How many edibles did you eat before researching this?"
"Absolutely zero, only a few. But how does this relate to the financial gambling market?"
"Yeah, there was that Wolf of Wall Street guy, but I didn't know he got released into Yellowstone. Let's think about the example of the wolves. I can tell you exactly what a wolf does: it looks for food, eats, sleeps, drinks pints with its wolf friends on Friday. That's basically the extent of its life. Yeah, there's some howling, some random walking around, but in essence, a wolf's entire existence isn't made up of any incredibly complicated decisions that we couldn't predict."
"Okay, so if that's relatively predictable, let's just add the deer into that equation. After all, we can predict the actions of a deer fairly easily too. And if we can predict the deer, we can also predict x, y, and z. That should give us a complete view of the system. We know what deer do: they eat grass, they eat quickly, then later they cough up food and chew it again. They do whatever this is. Let's just say we can predict the actions of a deer fairly well with around 70 percent certainty. The problem is that with each degree we remove our prediction from those very precise starting conditions, things get significantly more complicated and unpredictable. You're making a prediction based on a prediction based on an assumption. So you can even change up the word if you want, but with each prediction you make, you're becoming more and more uncertain. Is it starting to make sense why economic theory doesn't actually work to predict anything? Why the degenerates of NSE would rather listen to a gecko for their financial advice than look at a 10K?"
"Well, I'm here to offer a solution. Not a good one, but a solution nonetheless. Think about the most basic economic model of the stock market. Every independent degenerate has only one goal: to make as much money as possible. But this is where the paradox arises: you can only buy a stock if someone is selling— a counterparty, probably an institution. The same institution that sold you those 20 out-of-the-money calls expiring in three days. Why would you buy those? See, at least I know when I open Zerodha that I'm making a donation to NSE. That money is better spent in their hands, you know, on another vacation trip to Indonesia. I didn't even want to eat this week. I'm dieting, dying."
"Interesting. Knowing what we know about complex adaptive systems, we can recognize that when large groups of people all have the same opinion, inefficiency arises. Asymmetry between risk and reward means if you're willing to roll the dice enough times, eventually the law of averages is going to carry you to the moon. And where might you find that asymmetry? In places where the masses can't fathom how they'd possibly lose. So maybe there is hope for us. Probably not, but maybe. Will any of this abstract theory help to predict anything in real life? I wouldn't count on it. Will I take my own advice to help manage risk and outsize returns? I wouldn't count on it.
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