This is very belated, but back in the summer of 2014 I was approached to write a bunch of sections — all of them to do with dinosaurs, naturally — in the book Evolution: The Whole Story. I did seven group overviews (Dinosauria overview, prosauropods, sauropods, stegosaurs, ankylosaurs, marginocephalians, and hadrosaurs), having managed to hand the theropod work over to Darren.

My author copy arrived in February 2016 (which, yes, is over a year ago. You point?) It’s really nice:


And at 576 heavy, glossy pages, it’s a hefty tome.


My contribution was fairly minimal, really: I provided about 35 pages. Darren wrote a lot more of it. Still, I’m pleased to have been involved. It’s nicely produced.

Here a sample spread — the first two of a four-page overview of sauropods, showing some nice illustrations and a typical timeline across the bottom of the page.


And here’s one of the ten “highlights” sections I did, mostly on individual dinosaurs. This is the best one, of course, based on sheer taxon awesomeness, since it deals with Giraffatitan:


Unfortunately, not all of the artwork is of this quality. For example, the life restoration that graces my spread on Argentinosaurus makes me want to stab my own eyes out:


Still, putting it all together, this is an excellent book, providing a really helpful overview of the whole tree of life, each section written by experts. It’s selling for a frankly ludicrous £16.55 in the UK — it’s easily worth two or three times that; and $30.24 in the US is also excellent value.

Highly recommended, if I do say it myself.

The previous post (Every attempt to manage academia makes it worse) has been a surprise hit, and is now by far the most-read post in this blog’s nearly-ten-year history. It evidently struck a chord with a lot of people, and I’ve been surprised — amazed, really — at how nearly unanimously people have agreed with it, both in the comments here and on Twitter.

But I was brought up short by this tweet from Thomas Koenig:

That is the question, isn’t it? Why do we keep doing this?

I don’t know enough about the history of academia to discuss the specific route we took to the place we now find ourselves in. (If others do, I’d be fascinated to hear.) But I think we can fruitfully speculate on the underlying problem.

Let’s start with the famous true story of the Hanoi rat epidemic of 1902. In a town overrun by rats, the authorities tried to reduce the population by offering a bounty on rat tails. Enterprising members of the populace responded by catching live rats, cutting off their tails to collect the bounty, then releasing the rats to breed, so more tails would be available in future. Some people even took to breeding rats for their tails.

Why did this go wrong? For one very simple reason: because the measure optimised for was not the one that mattered. What the authorities wanted to do was reduce the number of rats in Hanoi. For reasons that we will come to shortly, the proxy that they provided an incentive for was the number of rat tails collected. These are not the same thing — optimising for the latter did not help the former.

The badness of the proxy measure applies in two ways.

First, consider those who caught rats, cut their tails off and released them. They stand as counter-examples to the assumption that harvesting a rat-tail is equivalent to killing the rat. The proxy was bad because it assumed a false equivalence. It was possible to satisfy the proxy without advancing the actual goal.

Second, consider those who bred rats for their tails. They stand as counter-examples to the assumption that killing a rat is equivalent to decreasing the total number of live rats. Worse, if the breeders released their de-tailed captive-bred progeny into the city, their harvests of tails not only didn’t represent any decrease in the feral population, they represented an increase. So the proxy was worse than neutral because satisfying it could actively harm the actual goal.

So far, so analogous to the perverse academic incentives we looked at last time. Where this gets really interesting is when we consider why the Hanoi authorities chose such a terribly counter-productive proxy for their real goal. Recall their object was to reduce the feral rat population. There were two problems with that goal.

First, the feral rat population is hard to measure. It’s so much easier to measure the number of tails people hand in. A metric is seductive if it’s easy to measure. In the same way, it’s appealing to look for your dropped car-keys under the street-lamp, where the light is good, rather than over in the darkness where you dropped them. But it’s equally futile.

Second — and this is crucial — it’s hard to properly reward people for reducing the feral rat population because you can’t tell who has done what. If an upstanding citizen leaves poison in the sewers and kills a thousand rats, there’s no way to know what he has achieved, and to reward him for it. The rat-tail proxy is appealing because it’s easy to reward.

The application of all this to academia is pretty obvious.

First the things we really care about are hard to measure. The reason we do science — or, at least, the reason societies fund science — is to achieve breakthroughs that benefit society. That means important new insights, findings that enable new technology, ways of creating new medicines, and so on. But all these things take time to happen. It’s difficult to look at what a lab is doing now and say “Yes, this will yield valuable results in twenty years”. Yet that may be what is required: trying to evaluate it using a proxy of how many papers it gets into high-IF journals this year will most certainly mitigate against its doing careful work with long-term goals.

Second we have no good way to reward the right individuals or labs. What we as a society care about is the advance of science as a whole. We want to reward the people and groups whose work contributes to the global project of science — but those are not necessarily the people who have found ways to shine under the present system of rewards: publishing lots of papers, shooting for the high-IF journals, skimping on sample-sizes to get spectacular results, searching through big data-sets for whatever correlations they can find, and so on.

In fact, when a scientist who is optimising for what gets rewarded slices up a study into multiple small papers, each with a single sensational result, and shops them around Science and Nature, all they are really doing is breeding rats.

If we want people to stop behaving this way, we need to stop rewarding them for it. (Side-effect: when people are rewarded for bad behaviour, people who behave well get penalised, lose heart, and leave the field. They lose out, and so does society.)

Q. “Well, that’s great, Mike. What do you suggest?”

A. Ah, ha ha, I’d been hoping you wouldn’t bring that up.

No-will be surprised to hear that I don’t have a silver bullet. But I think the place to start is by being very aware of the pitfalls of the kinds of metrics that managers (including us, when wearing certain hats) like to use. Managers want metrics that are easy to calculate, easy to understand, and quick to yield a value. That’s why articles are judged by the impact factor of the journal they appear in: the calculation of the article’s worth is easy (copy the journal’s IF out of Wikipedia); it’s easy to understand (or, at least, it’s easy for people to think they understand what an IF is); and best of all, it’s available immediately. No need for any of that tedious waiting around five years to see how often the article is cited, or waiting ten years to see what impact it has on the development of the field.

Wise managers (and again, that means us when wearing certain hats) will face up to the unwelcome fact that metrics with these desirable properties are almost always worse than useless. Coming up with better metrics, if we’re determined to use metrics at all, is real work and will require an enormous educational effort.

One thing we can usefully do, whenever considering a proposed metric, is actively consider how it can and will be hacked. Black-hat it. Invest a day imagining you are a rational, selfish researcher in a regimen that uses the metric, and plan how you’re going to exploit it to give yourself the best possible score. Now consider whether the course of action you mapped out is one that will benefit the field and society. If not, dump the metric and start again.

Q. “Are you saying we should get rid of metrics completely?”

A. Not yet; but I’m open to the possibility.

Given metrics’ terrible track-record of hackability, I think we’re now at the stage where the null hypothesis should be that any metric will make things worse. There may well be exceptions, but the burden of proof should be on those who want to use them: they must show that they will help, not just assume that they will.

And what if we find that every metric makes things worse? Then the only rational thing to do would be not to use any metrics at all. Some managers will hate this, because their jobs depend on putting numbers into boxes and adding them up. But we’re talking about the progress of research to benefit society, here.

We have to go where the evidence leads. Dammit, Jim, we’re scientists.

I’ve been on Twitter since April 2011 — nearly six years. A few weeks ago, for the first time, something I tweeted broke the thousand-retweets barrier. And I am really unhappy about it. For two reasons.

First, it’s not my own content — it’s a screen-shot of Table 1 from Edwards and Roy (2017):


And second, it’s so darned depressing.

The problem is a well-known one, and indeed one we have discussed here before: as soon as you try to measure how well people are doing, they will switch to optimising for whatever you’re measuring, rather than putting their best efforts into actually doing good work.

In fact, this phenomenon is so very well known and understood that it’s been given at least three different names by different people:

  • Goodhart’s Law is most succinct: “When a measure becomes a target, it ceases to be a good measure.”
  • Campbell’s Law is the most explicit: “The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”
  • The Cobra Effect refers to the way that measures taken to improve a situation can directly make it worse.

As I say, this is well known. There’s even a term for it in social theory: reflexivity. And yet we persist in doing idiot things that can only possibly have this result:

  • Assessing school-teachers on the improvement their kids show in tests between the start and end of the year (which obviously results in their doing all they can depress the start-of-year tests).
  • Assessing researchers by the number of their papers (which can only result in slicing into minimal publishable units).
  • Assessing them — heaven help us — on the impact factors of the journals their papers appear in (which feeds the brand-name fetish that is crippling scholarly communication).
  • Assessing researchers on whether their experiments are “successful”, i.e. whether they find statistically significant results (which inevitably results in p-hacking and HARKing).

What’s the solution, then?

I’ve been reading the excellent blog of economist Tim Harford, for a while. That arose from reading his even more excellent book The Undercover Economist (Harford 2007), which gave me a crash-course in the basics of how economies work, how markets help, how they can go wrong, and much more. I really can’t say enough good things about this book: it’s one of those that I feel everyone should read, because the issues are so important and pervasive, and Harford’s explanations are so clear.

In a recent post, Why central bankers shouldn’t have skin in the game, he makes this point:

The basic principle for any incentive scheme is this: can you measure everything that matters? If you can’t, then high-powered financial incentives will simply produce short-sightedness, narrow-mindedness or outright fraud. If a job is complex, multifaceted and involves subtle trade-offs, the best approach is to hire good people, pay them the going rate and tell them to do the job to the best of their ability.

I think that last part is pretty much how academia used to be run a few decades ago. Now I don’t want to get all misty-eyed and rose-tinted and nostalgic — especially since I wasn’t even involved in academia back then, and don’t know from experience what it was like. But could it be … could it possibly be … that the best way to get good research and publications out of scholars is to hire good people, pay them the going rate and tell them to do the job to the best of their ability?

[Read on to Why do we manage academia so badly?]



Here is a nicely formatted full-page version of the Edwards and Roy table, for you to print out and stick on all the walls of your university. My thanks to David Roberts for preparing it.

It’s been pretty quiet around here, huh?


It’s all just too awful to write about sauropod vertebrae at the moment.

Trump. Brexit. Perverse incentives in academia. I can’t even get up enough enthusiasm to do the revisions for my own accepted-with-revisions manuscripts, let along write blog-posts.

Oh, western civilisation. And you were doing so well.