It seems like all anyone wants to tell you these days is how you can't trust your own mind. Your reasoning is too flawed and fails in predictable ways.
For instance, you're too unwilling to make trades because of loss aversion (and anyway, you overvalue players on your roster because of the endowment effect, especially if you invested major resources to acquire them and therefore are falling prey to the sunk cost fallacy). You're too slow to update your rankings because of the anchoring bias (and also, you're too fast to update because of recency bias).
The worst part is that all of this is true. Our reasoning really is prone to failing in predictable and replicable ways. These fallacies and biases are easy to demonstrate experimentally; they've been some of the most robust findings in the social sciences, easily surviving the replication crisis.
But sometimes, the rush to condemn all mental shortcuts goes a bit too far. These supposed glitches in our mental operating system did not materialize ex nihilo. Each of these shortcuts was developed because it was the best strategy for survival in a particular environment.
In some cases, we're no longer in that environment, and therefore that shortcut is no longer useful; it trips us up and costs us value. But this is a problem with that specific mental shortcut (or that specific environment), not an indictment of the concept of mental shortcuts altogether.
The reason we developed those shortcuts in the first place is that when they are well-suited to our current environment, shortcuts are tremendously useful. Not just because they save us time and energy (although let's not discount the utility of saving time and energy here—I typically find both in short supply), but because they lead to better, more accurate, more useful decisions.
Good Heuristics, Good Decisions
To begin with, these mental shortcuts are known as heuristics (or, colloquially, "rules of thumb"). And while most psychologists have spent the last fifty years studying all of the ways that heuristics fail, a few have devoted themselves instead to studying all of the ways that they succeed.
Here's an interview with Gerd Gigerenzer, one of the foremost proponents of "fast and frugal heuristics". It is by far the most useful fantasy football article I have ever read, and I make a point to revisit it every year or so. (It doesn't actually have anything to do with fantasy football, but that's okay—some would say the same about this column.)
In the interview, Gigerenzer notes some surprising areas where heuristics outperform much fancier models. And where do heuristics tend to succeed? Well, let's consider chess. If I am playing my opponent, there is plenty of uncertainty involved. Mathematician Claude Shannon once famously estimated that there are more possible variations in a game of chess than there are atoms in the known universe.
But the limits of that uncertainty are well-known. My opponent might move her king's pawn to e4, or she might prefer to move her queen's pawn to d4 instead, but she isn't going to pass Go and collect $200, and she certainly isn't going to move her Dreadlothian Battlecruiser to the Harkfasher Nebula to task the Grand Wizard Council with making humanity forget the concept of monarchy entirely, overthrowing my king and granting her a first-turn victory. We have a very clear idea of what is possible in chess. This is known as "bounded uncertainty".
Contrast this with investing in the stock market. I can study the business fundamentals of a cruise line, and it's possible that their current pricing structure might be insufficient to cover their costs, resulting in a loss of value. Or it might be possible that their new CEO's plan of building smaller ships helps them tap into a new market, resulting in an increase in value.
But it's also possible that a zoonotic disease will jump from bats to humans in a wet market somewhere in China, and as a result, humanity will spend a year or more staying six feet away from each other, resulting in a decrease in value. Stock investing is a "game" where none of the participants know all of the possible "moves". This is, as you might have guessed, "unbounded uncertainty".
As it turns out, in situations with bounded uncertainty, complex models do exceptionally well. Deep Blue, the first computer model to ever beat a human grandmaster in chess, evaluated moves based on 8,000 different variables and had plans for 4,000 different opening positions and all endgame board states involving five or fewer pieces (plus many involving six). It was able to search 200 million chess positions per second and compare them to a reference database of 700,000 Grandmaster games.
But the more uncertainty increases, the more simple rules tend to outperform their more-complex peers. In the interview, Gigerenzer recounts the story of Harry Markowitz, one of the creators of Modern Portfolio Theory (or MPT), a mathematical approach to allocating investments in the stock market to maximize returns for a given level of risk. You can scroll through the Wikipedia entry to get a good idea of the math involved (and how quickly it scales as you increase the number of assets you're investing in).
Did Markowitz use MPT for his own personal retirement investments? No, he didn't; instead, he used a simpler rule called "1/N" (or "one over N"). For one over N, you identify all of the stocks you like, and you just put an equal amount of money in all of them. (Therefore, if you like N stocks, you will invest 1/N of your total portfolio in each.) This was good because, in most situations and time periods, 1/N has outperformed the more complex allocation optimizations.
Gigerenzer has studied many more instances where simple heuristics dominate complex models, and this tends to be the common feature: the more uncertainty is at play, the better simple rules tend to perform.