Why reality appears surprisingly detailed
A few years ago, John Salvatier wrote a great article called Reality has a surprising amount of detail. He argues that everything from stairs to computer programs to boiling water is far more complex than it initially seems. We often underrate how detailed things are, in a way that can set us back.
I think these are good points, and want to give an explanation. Why is reality so surprisingly detailed?
I think the answer isn’t to be found in reality itself, but rather in our representation of it. Reality appears surprisingly detailed because our thinking is surprisingly shallow. We think that we have rich, fine-grained models of the world, but when we look closer we realise that they are anything but.
This is, in turn, due to the combination of two facts:
We often have multiple models of different levels of granularity, that we switch between depending on how close we are to a phenomenon, and depending on how much detail we need. (Cf. Construal Level Theory.)
Much of the time, we think that the world is as it appears to be to us, even when we have little reason to believe that to be the case. (Naive Realism.)
Because of 1), we often represent the world in high-level, abstract ways that are very much lacking in detail. And because of 2), we often intuitively believe that these abstract models directly represent the world. Therefore, we are repeatedly surprised when we zoom in and learn that some aspect of the world is more detailed than what our intuitions said.
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It often makes sense to create abstract, depleted models of the world. Many phenomena are so complex that if we immediately tried to represent them in all their complexity, we would be bewildered and overwhelmed. For instance, when trying to understand society, we need to use constructs like “the working class” and “the Protestants”; and we often need to disregard the many ways in which different members of these groups differ from each other. Digging into those details is costly and often simply not worth it (depending on the nature of our investigation).
But while it makes sense to use such abstract simplifications, the problem is that we’re often not aware of the fact that that’s what they are. Instead, we often intuitively believe that they directly represent reality. We have an impoverished understanding of how our cognition works. We often fail to see the extent to which we use coarse-grained, first-pass models. That’s why we’re often surprised by reality’s level of detail.
The obvious solution is to be more aware of the fact that our abstract first-pass models are simplifications. There are many historical examples of that. For instance, there’s Max Weber’s insistence that concepts like “the working class” and “the Protestants” are simplifying ideal types whose purpose is to highlight patterns that are common in certain groups; and not to claim that all, e.g. Protestant groups are similar in every regard. Similarly, it’s often emphasised that scientific models (e.g. in economics or biology) deliberately abstract away from many features. This can help to set expectations and reduce the level of surprise upon contact with reality.
Still, I think that in practice, it’s not always easy to take this detached perspective on our models. Naive realism comes very naturally to us, and we often don’t take the time to ponder the epistemological status of our intuitive models. We create models on the fly, and in the moment (e.g. in conversation), it may often appear as if even quite abstract models are supposed to be full representations of reality. Fighting naive realism is not impossible, but it’s certainly hard work.