https://youtubetranscript.com/?v=qB4701pXMOA
Hello and welcome to Navigating Patterns. What I’d like to talk about today are linear projections. Sort of what they are, why they’re important, how we use them, how that can fool us, why this concept is sort of pernicious, maybe a little dangerous, and what we can do about spotting this so we don’t get fooled. So when I’m talking about a linear projection, I’m really talking about prediction. And linear things are things that are connected in a straight line. Basically, it’s a one-to-one relationship. And this is important because while there are things that are one-for-one relationship, you have one thing move, the other thing moves in concert or in some relation, right? It doesn’t have to be the same movement, it can be the opposite movement, it can be sort of an oblique movement, it doesn’t really matter. There are a lot of things that don’t have that quality, and so you need to know the difference. And this is important because we as humans tend to use linear projection for everything, by default. Just assume that that’s the way it works. And that isn’t the way a lot of things work. So maybe if you’re on the Serengeti, knowing which way the tiger is gonna go, you can kind of do a linear projection of all the possible ways he can go and figure out a path out. That’s probable. And so for a lot of things in the moment that have a direct participatory relationship, a linear projection, linear thinking works great, like it’s perfect. There’s no problem with it, easy to calculate, no problem. But what happens is we confuse complicated things with many linear relationships with complex things. And those are different. So complicated and complex are not the same, and understanding the difference is important. And so just giving you this linear side hopefully will help you to get it. So a lot of things unfold in time, and most of those things are nonlinear. And the reason why they’re nonlinear is because as they unfold, they complexify. So they start out complicated, they complexify, and it’s the time component that fools us. Because in the beginning, very linear. But then they reach a point of emergence or something, and everything changes. This is sort of in complexity theory today. And so one sort of thing to watch out for is that complex things do not have linear relationships. So you can’t use linear projections to understand them very well. It’s not that it doesn’t give you any predictive power, but it doesn’t give you enough to be useful. And the things it doesn’t give you predictive power about usually wipe you out completely. They’ll just get you off guard completely. So one example of a linear relationship might be two numbers. Two numbers, we can have a linear relationship, right? Whether it’s two and five, and they’re three numbers apart. Or one is you can multiply two by two, and then add half of two to get five. All these are linear relationships. Some are complicated, right? But none of them are particularly complex. And geometry can get very complicated. But most of that is just lots of linear relationships. Most things can be defined very nicely in linear relationships. In fact, there’s a whole bunch of math where you basically take everything linear to solve the problems instead of doing a continuous calculation and utilizing complexity. And that works great. But once you get into relationships with people, for example, like relationships with your parents, those are not linear. You can’t push a button and get a result out of a person. The bonds that you form with people as they start out might be linear, but as time goes on, they become so complex. And there’s so many other interactions that, for example, you can’t see. I mean, if you can’t see the interactions and the relationships, then linearity doesn’t help you at all, even if it’s true. Like, if you can’t see the tiger stalking you, all linear predictions in the world aren’t going to help you, right? They’re just not going to do anything for you. And on top of that, some of these things aren’t linear. And once they’re not linear anymore, and they may start linear and end differently, now the predictive power is gone. And so what we pretend, what we tend to do is we’ll say something like, oh, the stock market’s been going up for 60 days. What do you think it’s going to do tomorrow? Go up. Sure, that’s a linear projection. And you can argue that there’s still a linear relationship with how far it goes down over time. Sure, but once you add the time component, now you can’t predict the stock market anymore. So while you may have a good luck at using linear projection on a day-to-day prediction, you can’t use that on a week-to-week or month-to-month basis. And as you try to, your reliability goes down. This is why stock market prediction is actually not all that easy. There are certain aspects of it that are, but those involve complex relationships. They may be simple formulas, but they’re complex relationships. And those relationships, any relationships that end up being complex are nonlinear by nature. And those are the things you have to kind of figure out. So like how does this help? Right? What is it that I’m looking out for? How do I spot this? How do I identify these different things? Well, one way is simplistic explanations. So if someone tells you, for example, that, you know, oh, all you have to do to get your parents to agree to let you go out is to say X. It’s a pretty simplistic thing, and most of us have probably been there where, yeah, we thought we had a really good reason, and then what happened is they started to question us about our reason and found out that we didn’t know anything about the thing we asked about to go out, and we were bullshitting. And they catch us. And that happens all the time, right, to kids. They think they’re getting clever, right? They don’t. And you can argue, look, if you had a good enough story, you might have gotten away with it. That’s true. But a good story is no longer a linear thing, right? A story is very nonlinear in nature. And so I’m not saying there’s not a solution. I’m just saying the linear projection of, oh, you tell them this and they’ll give you that is not the relationship that you have. And that’s one way to spot it, is a simplistic explanation. So if you say, for example, oh, you know, in order to solve this problem, all we need to do is pass a law. First of all, passing a law is the least straightforward way to solve any problem that humans have invented to date. So likely never gonna happen. But even if it did, there’s all these knock-on effects and things you don’t understand. And even if they pass a perfect law, the implementation’s never perfect. And so what ends up happening is these things tend to degrade and get worse and cause all kinds of problems. And there are so many examples just from government regulations alone that it would be insane for me to try to cover any good ones. They’re all good. Most laws suck. And you just look at the effects, right? What did the law intend? What actually happened? Right? That happens all the time. Free healthcare, ACA did not give us free healthcare. Situation it tried to solve is now worse than it was when they started. You know, the no fault divorce law, complete disaster. And ended everything very badly for everybody involved, basically. Well, except the people making lots of money. You know, there’s tons of examples of this stuff. And you could just spend like a lifetime just exploring how regulations go wrong at the executive level, even when you don’t have to pass a law, just a regular regulation that the executive branch passes terribly wrong. All governments make these mistakes constantly because they don’t understand these relationships. They think, oh, there’s a problem, there’s a solution. Homelessness. Oh, we just buy the homeless people places to live and that’ll solve. That’s been tried so many times. It’s, it’s ridiculous. Even in New York City, they tried that. Didn’t work for lots of reasons. You know, one of the reasons is homelessness doesn’t have a single cause. And so it’s not a linear relationship between giving people a house and them not being homeless. Like that’s not what’s going on there. It’s not that they don’t have access to houses. That’s actually not what’s going on. And I’m not saying there aren’t people that don’t have access to houses. There are. It’s just not the majority of homeless people as it turns out. And this has been tested and studied in all kinds of scales in whole countries. It never works. Like homelessness is still pretty much everywhere. There are ways to make it worse for sure. And there are ways to make it better for sure. But there isn’t some relationship you can do like giving people homes. It’s a too simplistic a solution. So you, you have to watch for those simplistic solutions and go, wait a minute. Right. You have to ask, is this really just a one to one connection? Is this some, is this a problem with a single cause? Because if it is maybe a linear projection, a linear solution will work. Like maybe that prediction is correct. And maybe not even, but, you know, at least you get good odds. But if there’s more than one cause, right. Or if one symptom actually contains many symptoms, you know, we’re just using one word for it, like homelessness, then, you know, you’re not going to get the solution that you want. And you can always try to come up with counter evidence, right? That’s another way to do it. Like, okay, well, you know, if, if, if people just need houses for, for, to be not homeless, are there any instances where people have been given houses and still homeless or had access to housing and still were homeless? Well, the answer is yes. Right. But you, you have to do that work. Like you do that critical thinking work. And then you have to check what, how the relationships interact, right? Is this a, an isolated thing? Because if it’s isolated, it’ll be very straightforward. But if it’s not, then the linear projection again, it won’t work. And then the other question you can ask is, are the things involved agents, right? Two agents with their own agency are never going to work on a linear projection, right? So that’s an easy, that’s an easy one. Right. So if only one of them is an agent, probably still not going to work because agents are complicated. And beyond that, they’re complex, all their relationships to things are at least complicated. And so a single solution, a single projection is never going to work in that situation. And we’re, we’re prone to this because again, like, man, if you want to get away from a tiger on the savanna, bang, linear projections, you’re a friend. Beyond that, you’re kind of doomed. And we have to be careful, there are better ways to think about these problems. And we get very well fooled by people showing us a linear relationship where there isn’t one. And then saying, there you go, there’s your solution. Just follow me. linear relationship, problem solved. And that’s where the problems come in. So we have to be very, very careful about that. The complexity versus complicated thing is very hard to understand. But I’ll get that video out soon. And we’ll go over it. And hopefully I can make it accessible to you. In the meantime, I’d like to thank you for your time and attention.