Taken from information technology and the process of compressing data into smaller subsections. For instance, a .jpg. When applied to ideas, compression enables an efficient overview but at the cost of data loss and therefore a distortion of reality. According to LIL INTERNET’s pet theory, people reflexively generate compressed models of the world in order to make an overly complex reality understandable. Like folktales and urban legends, conspiracy theories are a form of compression, giving Manichean narratives to the arbitrary and sociopathic maneuverings of the powerful. The monolithic categorizing of identities is another form of compression. Paradoxically, the more the real complexity of our world is revealed to us, the more likely we are to rely on cognitive compression to make sense of it.
LIL INTERNET: I've been reading about — I found this one paper that I don't actually agree with the hypothesis of the paper itself. The hypothesis was that music that seems complex, but is actually simple, is the most pleasurable music to listen to. And it was kind of based on information theory and this one guy's idea that, like, pattern recognition is pleasurable; that somehow at some point, a link was created between recognizing a pattern and feeling a bit of a little dopamine reward for recognizing the pattern. Which makes sense, the more you think of it, like conspiracy theories, things like that, identity politics... getting jokes was an example they used. But in information theory, right, when it comes to compressing data, patterns are compressible. Any pattern that repeats, you can just take that chunk of data and hit it times three, and you don't need to use that long string of it three times in a row, you can just compress the pattern.
And compression is ubiquitous in the online space, whether images or whatever, because it reduces costs, energy, money, bandwidth, and increases profit. But you know, we also see compression in the character limits of social media. And the way the medium operates, it forces you to compress your language, to compress your thoughts. You know, we also see compression in the tutorialization of creative production, which he talked about a little bit. But that's ultimately like finding the pattern, figuring it out, extracting it, and then repeating the pattern. That's the easiest compression, but in, you know, in online discourse, right, which is taking place on social media that forces compression, a good example is the narrative that racism is the sole cause of marginalization of black people in the country. I mean, it's definitely a reduction, right? It's like reductionist, but from an information theory framework of compression, and especially of platforms that require compression, it makes perfect sense, because racism is the most repeatable pattern in all of the factors that lead to the marginalization of Black Americans.
However, though, you have this compressed model, where you take the pattern that repeats the most, but the problem is, you're discarding all of the other factors, all of the more random and non-repeatable factors, which are a really important part of how we got to this place, but they are uncompressible. A string of random numbers cannot be compressed, because there's no repeating pattern. So any variable that's not a repeating variable gets discarded in compressed models. And we end up with this political landscape like we have now across the left and the right to be honest, where everything becomes a compressed narrative, because the platforms incentivize, if not require compression. So that what works best for compression works best on the platform. So I mean, the brain worm of the online is sort of the brain worm of forced compression. And what we're ending up with, as we're spending more and more time in this space, is a mind that operates in compression, like a mind that's like full of .mp3s instead of .wavs, like conceptual .mp3s instead of .wavs. And and this is something of course, that's always happened in the past, but it's accelerated and amplified. Now, we've always had to reduce [to simpler] patterns to understand things. But we're doing it with really big, really complex things. And we're discarding all the random and more complex variables. And we end up with this compressed pattern core in what is ultimately lossy models of the world. We use lossy compression for making models of the world.