Whether it be in science or business or politics or popular culture, we expend an inordinate amount of time and effort trying to figure out why people do whatever people are doing. We seem to have more analysts than actors, all desperately trying to explain what motivates people, either by asking them directly or by making inferences about them. For the most part, this is not merely a colossal waste of time and effort and money in itself, but it stimulates even greater wastes of time and effort and money chasing wildly incomplete or erroneous conclusions about why we do what we do.
Asking people why they did what they did, or why they are doing what they are doing, or why they are going to do what they are going to do, generally yields useless and misleading information. It is not clear that people actually have distinct reasons they can recognize let alone articulate. It is quite likely in fact that most of the decisions we make are made unconsciously based upon a myriad of complex neural network associations. These associations need not be rational. These connections don’t need to be internally consistent to each other or related to the actual outcome in any way. But in our post-rationalizations and post-analyses we impose some logic to our decisions to make them feel sensible. Therefore, the reasons we come up with are almost completely made-up at every level to sound rational or at least sane to ourselves and to those we are communicating to.
The truth is, we can’t usually hope to understand our own incredibly complex neural networks, let alone the neural networks of others. Yes, sometimes we can identify a strong neural network association driving a behavior, but most determinative associations are far too diffuse across a huge number of seemingly unrelated associations.
The situation gets infinitely worse when we are trying to analyze and explain group behaviors. Most of our shared group behaviors emerge from the weak-interactions between all of our individual neural networks. The complexity of these interactions is virtually unfathomable. The challenge of understanding why a group does what it does collectively, let alone figuring out how to influence their behavior, is fantastic.
If you ask a bird why it is flying in a complex swirling pattern along with a million other birds, it will probably give you some reason, like “we are looking for food,” but in fact it is probably largely unaware that it is even flying in any particular pattern at all.
So why point all this out? Do we give up? Does this imply that a rational civilization is impossible, that all introspection or external analysis is folly?
Quite the contrary, we must continue to struggle to understand ourselves and truly appreciating our complexity is part of that effort. To do so we must abandon the constraints of logic that we impose upon our individual and group rationalizations and appreciate that we are driven by neural networks that are susceptible to all manner of illogical programming. We must take any self-reporting with the same skepticism we would to the statement “I am perfectly sane.” We should be careful of imposing our own flawed rationality upon the flawed rationality of others. Analysts should not assume undue rationality in explaining behaviors. And finally, we must appreciate that group behaviors can have little or no apparent relationship to any of the wants, needs, or expressed opinions of those individuals within that group.
In advanced AI neural networks, we humans cannot hope to understand why the computer has made a decision. Its decision is based upon far too many subtle factors for humans to recognize or articulate. But if all of the facts programmed in to the computer are accurate, we can probably trust the judgement of the computer.
Similarly with humans, it may be that our naive approach of asking or inferring reasons for feelings and behaviors and then trying to respond to each of those rationales is incredibly ineffective. It may be that the only thing that would truly improve individual and thus emergent thinking are more sanely programmed neural networks, ones that are not fundamentally flawed so as to comfortably rationalize religious and other specious thinking at the most basic level (see here). We must focus on basic fact-based thinking in our educational system and in our culture on the assumption that more logically and factually-trained human neural networks will yield more rational and effective individual and emergent behaviors.