I’ve spent some time talking about memes already and I plan on talking about them a lot more. Yet the supposed overarching theme of this blog, computational axiology, isn’t obviously connected to memetics. Why are we looking at memes and not partially observable Markov decision process or what have you?
Memetic cognitive algorithms, or what I somewhat sorrowfully misleadingly call memes for short, are a central class of valuing structures. They’re somewhere between genes and temes on the scale of algorithmic physicality versus universality (temes can run on simpler Turing machines, whereas genes often require specific physical laws). Some are satiable and some are arguably insatiable. Some are created by genes (technically genetic cognitive algorithms) and some are capable of being or creating temes. They’re under selection pressures observable to
humans human genes and memes over recorded history, over a lifetime, and over a day.
Memes are incredibly powerful. They’re what make humans the most intelligent life yet observed in the universe, and the source of an astonishingly wide array of values. In fact, for nearly everything valued by memes, there are yet more memes that value the exact opposite. Light and dark, good and evil, Blue and Green, Yankees and not-Yankees. They’re somewhat alien: some run on millions of minds at once and tell their host brains to do very strange things, like abstract math. Some are viral, some are parasitic, most are symbiotic. Humans made them and let them into the world, but the humans weren’t very careful in doing so, and now the comparatively stupid genes must tread very cautiously, lest the memes eat their brains or have them killed.
But there is also a more pragmatic reason to study memes than to understand their kind of valuing structures. In order to study computational axiology well, we will need to skillfully wield a wide array of very powerful memes. We should figure out what they want and what their true nature is, if we want to make sure that they’re on our side, and aren’t honey in a fly trap secretly waiting for their turn to turn ourselves against our selves.
It is for these reasons that I find memes very interesting. I was rather convinced by the gene’s-eye-view of evolution expounded upon by Dawkins in The Selfish Gene and The Extended Phenotype. Perhaps this is a large part of why reasoning in terms of genes and memes seems natural to me, whereas reasoning in terms of humans seems messy and obfuscatory. Most summaries of memetics focus on memes like music or language. These don’t interest me as much as memes like science, rationality, or philosophy: the memes that created a lot of what
I whatever cognitive algorithms are writing this seem to value, and what seem capable of engineering an even brighter future.
Unless you persuade me otherwise, memetic cognitive algorithms will be a common theme here as we explore what they are, what they want, and what they might do if they get it.