The Tyranny of Trendy Ideas in Academia

>Nobody reads JP to become a better person, they do it to feel better about being a shit person.

Isn't that the case for any other pseudo-profound pop-philosophy as well, not just JP?

And isn't JP so popular exactly because the more mainstream academic trend is so laughable?

My part of political science has been (attempting to) follow scientific method since the 1940s. Its results have been... underwhelming. The main reason for this, bluntly, is that people are really damned complicated. They're difficult to model, they behave irrationally, even a minimal model has way too many variables. Hell, they don't even vote using the same heuristics that they did 50 years ago. There's a reason why abstract social science (like model-based economics) can look like it has little connection to reality, while the less abstract you get then the woolier and less scientifically satisfying it generally becomes. It's because getting crisp, reproducible, cumulative, results is extraordinarily hard.

Faced with that, you have two choices: keep trying to get better at what we're doing, or decide that these questions aren't worth answering (or that it isn't possible to). I think that better understanding the behaviour of people collectively organising themselves is vital, so I pursue the first. But I have sympathy with those that would prefer to work on something else.

Luckily, no one takes psychoanalysis seriously in the cognitive sciences. I can't imagine getting funding for a neuroimaging study of the Id.

From the article:

> Mayr’s observation draws attention to the fact that fashion is never merely additive; it demands we abandon certain things, and those abandonments can be, at the very least, premature.

I've observed these premature abandonments in my own area (a particular subfield of fluid dynamics), but I think its cause is more complicated than "academics follow fads". I think fads are part of it, but the grant funding cycle is another part. Perhaps grants are largely driven by fads. I wouldn't really know, but I can recall calls for proposals on fad X as applied to Y. I think part of it is that people rarely do actually comprehensive literature reviews, so they don't have a good idea of what's been done and what hasn't been done, so they might assume something isn't as novel as it actually is. Or their literature review might make them think that X is solved, but a little-known paper showed that X is actually more complicated than is commonly believed and not solved.

This dynamic means there's a lot of "low-hanging fruit" available that won't look like low-hanging fruit to most people. I'm happy to grab such things myself. A few years ago I made a large data compilation for a problem I'm interested in (~1200 data points from ~50 studies) and have written two papers from that already, with several more planned. Many people seem to implicitly assume this work has already been done because it seems "obvious", but I think large data complications of this variety are rarely ever done. Many things that are commonly believed are actually easily shown to be false with the right data. I'm planning to make a career of this. It's not sexy right now but that could change with time.

Edit: Data compilation is a commonly missed "last step" in a review. Most reviews I've read seem to collect the conclusions of analyses of small amounts of data. It's better to instead collect all the data, filter out the bad data, and draw conclusions from the compilation instead. A collection of the conclusions of small studies will have bad data, biases, and/or not generalize as well.

Hmmm, I'm not so sure about this. With things like microarrays and other tech, sure there were promises that were too big. But the underlying idea (gene expression matters, there are lots of genes, we should measure them all) is correct.

To wit, RNA-seq has transformed cancer biology. And Nanostring is now offering a microarray-like technology with extremely impressive signal to noise. And we still use lots of tools initially developed for microarrays (Limma, for example). So I would argue the investment in microarray technology has absolutely paid off.

I think you're throwing the baby out with the bath water to say that, because a few bad eggs are effectively salesman and not scientists, that we wasted "hundreds of billions" of dollars (i.e., a large fraction of the NIH budget). The fact is, most research doesn't hold up not because the researchers weren't honest, or because they were salesmen, but fundamentally, because science is hard and it's really hard to be right for the right reason in science.

Alternatively, if you have a formula to determine, in real time, which results are hype and won't stand up vs. which are real and will, well, that would be something too.