Hacker News new | ask | show | jobs
by melonrusk 1363 days ago
> 20% of PhD-granting institutions in the United States supplied 80% of tenure-track faculty members

80:20

The Pareto Principle at work. All these figures show is that universities have the usual distribution of quality.

2 comments

You'd hope that quality requirements could be used to make a bar that is high enough so any accredited institution would be delivering highly educated and developed people. Anything beyond that would be nice to have, but not enough to be a deciding factor when judging merit.

When you think about it, what is it that we want? Someone with fancy labels who went to places that also had fancy labels, or someone who can do the thing we need?

The minimum bar (accreditation) is very different from top performers.

You may call every person who graduates medical school & passes the boards "doctor", but they're definitely not all created equal either. Same for lawyers, engineers, computer scientists, etc.

But doesn't that mean that either the bar is too low, or the additional performance of top performers skews the entire field?
the Pareto principle doesn't really provide a lot of insight into process:

https://www.technologyreview.com/2018/03/01/144958/if-youre-...

Garbage model, garbage methodology.

Their model has a set of agents with a 'talent' normally distributed between 0 and 1 (mean 0.6, SD 0.1). Each agent gets a random series of doubling and halving events. The doubling is applied with probability = 'talent'.

The lengths of the series of events have an exponentially decreasing distribution (four longer is x10 less probable, approximately). The application of these series of events to the agent is supposed to simulate a life history of random opportunities, exploited according to talent.

BUT...

Half the events are losses, half the events are possible (P<1) gains. So for an agent with average luck (which is every agent in the long term), their capital decreases with time. The simulation has been rigged so agents can only win with luck.

DOI: 10.1142/S0219525918500145

Late addition:

It's actually worse than that. If we take a population with only median and maximum talents, the probabilities for each event are: halving: p=0.5, doubling because median-or-above talent: p=0.5*0.6. Meaning that in 80% of the events for a given actor the talent is not considered at all.

So the result that luck plays a larger part isn't an emergent property of the model, it was explicitly baked into the model from the start.

It wouldn't be unfair to call this paper fraudulent.

DOI: 10.1142/S0219525918500145