The overall survival rate of a meme m can be expressed as the meme fitness F(m), which measures the average number of memes at moment t divided by the average number of memes at the previous time step or "generation" t - 1. This fitness can be expressed in a simplified model as the product of the fitnesses or survival rates for each of the four stages, respectively assimilation A, retention R, expression E and transmission T: F(m) = A(m) . R(m) . E(m) . T(m)
A denotes the proportion of memes vehicles encountered (or memes independently discovered) by the host that are assimilated. R represents the proportion of these assimilated memes that are retained in memory. Therefore, A <= 1, R <= 1. E is the number of times a retained meme is expressed by the host. T is the number of copies of an expression that is transmitted to a potential new host. Unlike A and R, E and T do not have an upper bound, although E is likely to be more restricted than T. Note that F is zero as soon as one of its components (A, R, E, T) is zero. This expresses the fact that a meme must successfully pass through all four stages in order to replicate. Also note that for a meme to spread (F > 1), you must have E > 1 or T > 1.
this is a legacy formula, i want to update it in time [6:45 PM] GRIT™: there are degrees of complexity [6:45 PM] GRIT™: and different environments suit different memes
on engineering meme fitness :
its timing based, which is exposure its usually socially dependent A/B testing.
automating this function via : neural net => fitness => evolutionary algos
I was just thinking how cool it would be to turn the training/tuning regimen into a mech turk task, so 1000 memes would get graded and categorized and the results fed back in an iterative loop. To quickly get from <some good some crazy stuff> to <shit that hits home almost every time>