I'm currently doing research in pseudorandomness, which involves a zoo of wonderful objects such as pseudorandom generators, randomness extractors, expander graphs, etc. I find it a fascinating topic, but one thing that drives me crazy is the glut of parameters that are involved. I understand that these objects are very complex, but I cannot help but break out into a sweat when I see "Let $G$ be a standard $(\alpha,V,\epsilon^2,k,\delta)$-pseudorandom widget...". Then I have to flip back in the paper or find another paper (which probably uses a different parameterization) and try to remember what all $\alpha,V,\epsilon,k$ and $\delta$ all meant.
It takes me quite a while to acquire a feeling for "good" parameter settings versus "bad" parameter settings, versus "natural" settings versus "easy" settings.
There's probably no magic bullet for this issue - but I was wondering if other people had some method of managing the "parameter explosion" so that it's easier to retain in memory for a longer period of time?