Tooting one's own horn

A few years ago ... well OK more like six ... Mike Runge of the USGS gathered a group of regular attendees to the Adaptive Management Conference Series with a number of USFWS employees who had ... issues. Three of them, in fact, and the goal was to see if the sort of quantitative decision theory approaches developed for the Mid-continent Mallard Harvest could be applied to endangered species. It has taken a while, but there will soon be a special issue describing the outputs of that workshop, and the ones that followed.

In the meantime, I was recently asked to summarize what my group did for bull trout in the Lemhi Basin for laypeople. In 800 words. The result looks sharp, but that's because of the pictures more than the words, I think!

Why we should lead with values, not facts

Over the past couple of years I've had some major paradigm shifts. One of those relates to the value of science in debates - recognizing that sometimes, no amount of science is enough. I just read an article by Chris Mooney in MotherJones.com reviewing some very interesting research on how political values affect how we perceive evidence. I've quoted the last few paragraphs below to give context to the very last sentence, which says it all for my new paradigm.

The upshot: All we can currently bank on is the fact that we all have blinders in some situations. The question then becomes: What can be done to counteract human nature itself?

Given the power of our prior beliefs to skew how we respond to new information, one thing is becoming clear: If you want someone to accept new evidence, make sure to present it to them in a context that doesn't trigger a defensive, emotional reaction.

This theory is gaining traction in part because of Kahan's work at Yale. In one study, he and his colleagues packaged the basic science of climate change into fake newspaper articles bearing two very different headlines—"Scientific Panel Recommends Anti-Pollution Solution to Global Warming" and "Scientific Panel Recommends Nuclear Solution to Global Warming"—and then tested how citizens with different values responded. Sure enough, the latter framing made hierarchical individualists much more open to accepting the fact that humans are causing global warming. Kahan infers that the effect occurred because the science had been written into an alternative narrative that appealed to their pro-industry worldview.

You can follow the logic to its conclusion: Conservatives are more likely to embrace climate science if it comes to them via a business or religious leader, who can set the issue in the context of different values than those from which environmentalists or scientists often argue. Doing so is, effectively, to signal a détente in what Kahan has called a "culture war of fact." In other words, paradoxically, you don't lead with the facts in order to convince. You lead with the values—so as to give the facts a fighting chance.

That's it. Values matter. Lead with the values.

Expert Blogging

I recently wrote a few lines about the need to be able to identify expert bloggers to help non-experts weed out bad information in social networks. There's an interesting article in today's Financial Times on the effect of social networking on access to information. If you're like me, and you don't have a subscription to FT, you can read the excerpts and additional commentary by Roger Pielke, Jr..

Wise decisions and predictions

Daniel Sarewitz is a leader in the Science-Policy interface area, and last year he had this to say in an opinion piece in Nature last year:

If wise decisions depended on accurate predictions, then in most areas of human endeavour wise decisions would be impossible. Indeed, predictions may even be an impediment to wisdom. They can narrow the view of the future, drawing attention to some conditions, events and timescales at the expense of others, thereby narrowing response options and flexibility as well.

Would “projections” also lead to the same trap? According to Kevin Trenbarth, the difference is that a projection makes no effort to start from the actual initial state of the system, and so all that can be evaluated is the change from the assumed initial state. As a result, there is no expectation on the part of the “projector” that the projection will actually come to pass. In contrast, a prediction is made in the expectation that the future will look similar to the prediction, although as far as I can tell, the same tools are used for both. Intriguingly this is yet a third way to define the difference between a projection and a prediction. Either way, I think predictions and projections run the risks described by Sarewitz.

The first science blogger?

I nominate Johannes Kepler as the first science blogger. Dedre Gentner, in her paper Analogy in Scientific Discovery: The Case Johannes Kepler (2001) writes that

[Kepler] provided a running account of his feelings about the work, including the kind of emotional remarks that no modern scientist would consider publishing.

As an example she offers the following quote from Kepler's Astronomia novae

If I had embarked upon this path a little more thoughtfully, I might have immediately arrived at the truth of the matter. But since I was blind from desire I did not pay attention to each and every part [...] and thus entered into new labyrinths, from which we will have to extract ourselves. (Kepler 1609, pp. 455-456)

Gentner provides a few other choice quotes too - hence I think that if Kepler were around today, he'd be blogging.

Predicting the future

This is good.

Don't Transform

One of my pet peeves about my ecological colleagues is their tendency to transform binomial data using arcsine of the squareroot of the proportion in order to use a linear model. OK, once upon a time, it might have made sense to do this. But we have better tools now, honestly! Travis Hinkelman brought a great paper by David Warton and Francis Hui to my attention this morning. I'm just going to quote one line, which sort of says it all:

The most striking result in power simulations was that logistic regression and GLMM always had higher power than untransformed and arcsine transformed linear models ...

So, don't transform your binomial data. And please, if you are collecting proportion data, write down both the numerator and denominator! This will be required reading in my Ecological statistics class next fall.