Memetics as a science

Memetics is highly promising and has the potential to be the grand unifying theory we need to understand human nature. However it is not yet considered a scientific discipline by contemporary scholars. The theory of Memetics has never been refuted, but it also isn’t mainstream. Most agree it’s a useful analogy, and there have been attempts at establishing Memetics as a science. There was even a Journal of Memetics, but it has remained a largely fringe interest. The primary criticism is that memes can’t easily be measured. This is because we don’t have a formal definition of memes, as we do with genes. If memes aren’t rigorously defined, we can’t reliably agree on where they start and end. For example my definition of the meme ‘batman’ is likely different to yours, based on our relative level of interest in, and exposure to, the movies and comics.

Without a formal definition, memes can’t be consistently identified and tracked over time, to observe how they replicate, spread, and develop. With no commonly accepted empirical methods for studying memes, very little progress can be made. If you can’t reproduce the results of an experiment, it’s difficult to trust the findings. If we had a mathematical definition of a gene, then we could make testable predictions & falsifiable hypotheses. We would need to make predictions about how the presence of specific memes will alter marketing outcomes, and those predictions would have to turn out to be correct. Ideally we would identify insights that nobody would have looked for had they not been starting from a theory of Memetics. If you know what will happen under certain conditions, you know something about how the World works. The key to popularising Memetics as a legitimate method will be finding a method that makes predictions possible and results replicable.

Many fields have started as pseudo science and dropped the pseudo: the practitioners of Alchemy – the quest to turn lead into gold – laid the foundation for the modern study of Chemistry. It’s also possible for a once proud Science to degrade into Scientism: see the replication crisis in Psychology and the birth of Critical Race Theory: a field populated 97% by the political left. As NPR put it “Experts find bias amongst bias researchers”. The quality of the “scholarship” in a field is often debased by the introduction of politics. Replication and Prediction go out of the window, in favor of staying on message. Memetics has the potential for being highly politicised, however its relative obscurity has protected it for now.

Proponents of Memetics have been caught up in the ontology of meme units — categorizing them into groups — rather than testing hypotheses for how memes form and interact. Memetics is treated as a useful analogy and way of thinking that gets results, but is too amorphous and unpredictable to standardize and formalize. This lack of provable best practice means the concept of a self-replicating meme was excluded from pre-eminent models used by the research community, who opted for gene-culture co-evolution instead (also called dual inheritance theory). Co-evolution has its roots in group selection, a theory that is no longer widely accepted by evolutionists. Practically speaking memes evolve at such faster speeds than genes that I find it unlikely that this will remain the leading theory for long.

We can identify genes from a long list of nucleotides, and where they begin and end by their ‘codons’, but we currently have no equivalent ‘codon’ for memes. Perhaps this is less needed than we expect. Comparative Phylogenetics from Biology – comparing trait inheritance rather than tracing specific genes that cause them – seems to have had some success in application to Memetics. One study used this method to trace the origins of folklore back to mass migrations of pre-historic people, and another tracked the evolution of creationist legislature as attempts failed and passed to enact it as law across various U.S. states. These studies are relatively laborious, taking months to catalogue the traits contained in the subject matter. However the rapid advancement of machine learning promises to automate much of this work.

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