Killing the linear deficit model of science communication

The idea that "the public" will make better decisions about health and the environment "if only they knew what I know" is widespread among ecologists and nutrition scientists. And probably other scientists too. Science-policy interface experts call this idea the "linear deficit" model. And it's wrong.

Reflecting on research: Structured Decision Making coaching

My work as an SDM coach is by far the most impactful, meaningful contribution I make to wildlife management in this country. 

Blood sugar targets

or how low should I go? How high is too high? Is having targets even a good idea for Adaptive Management?

Horn tooting

One of the things I've been interested in for quite a while is making decisions with poor or no information - what social scientists since Keynes and Knight call uncertainty, meaning that there are no probability distributions available for the outcomes. If we're being honest with ourselves, this characterizes alot of circumstances when dealing with endangered species management. In such circumstances, one possible response is to "satisfice" rather than optimize the management actions.

Earlier this year Max Post van der Burg and I published an article in Ecological Applications Integrating Info-gap Decision Theory With Robust Population Management: A Case Study Using The Mountain Plover" where we used a combination of methods borrowed from robust control theory and satisficing to understand the value of a particular management action to a threatened species. This was a piece of Max's dissertation, and as usual in such things, he did all the hard and important work!

The core idea of "satisficing" is to find a decision that performs good enough, but over the largest possible number of ways of being wrong. In contrast, optimisation focuses on maximizing performance assuming that the system is perfectly understood - i.e. all the parameters are known perfectly and the system model is exactly correct - circumstances that are never true even in the best of times. So an optimal decision will usually outperform a satisficing decision if one's knowledge of the system is perfect, the satisficing decision will continue to do well even if the system model and its parameters are incorrect.

Of course, it is possible that a satisficing strategy is also the optimal strategy, and then we're happiest, but this doesn't seem to happen very often.

Max's contribution was to couple a matrix population model of Mountain Plover with the idea of satisficing to look at how well "nest marking" of Plovers performs as a conservation strategy. The upshot is that even if we are not sure about the life history of this species, nest marking increases the range of "wrongness" under which we will see positive population growth. What we didn't do was evaluate different types of actions against each other - this could easily be done, but was beyond the scope of what we wanted to achieve in the paper.

It's values folks .... values all the way

Dr. John Marburger, former science advisor to the Bush Administration, was often castigated by the science community for Bush administration policies on things like stem cell research. He passed away at the age of 70 yesterday, and in his obit in the Washington Post there was this quote:

“No one doubts stem cells are valuable to research and hold tremendous promise — on that, there’s no scientific controversy,” he said in 2001. But he added that the matter “is not going to be decided by science.”

This echos the theme I've written about before, that values matter, and we scientists need to get used to that fact.

I'm writing from the National Conference on Ecological Restoration in Baltimore, MD. Plenty of evidence that values matter, and equally much evidence that scientists don't understand that fact.

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.

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.

Decision Support in your stocking

'Tis the season to shop! I just received an email from the National Academies of Sciences with their recommended gift list for scientists and engineers - and #2 was Informing Decisions in a Changing Climate - great gift for myself!
On a closer look, this book has a lot to offer. The chapter on decision support and learning is a great review of a broad interdisciplinary domain, and offers some novel synthetic insights of its own, including Table 3.1 on learning modes. I found the two right hand columns, Adaptive Management and Deliberation with Analysis particularly informative. These two columns are pretty much the same, except for two key, and inextricably linked, differences: the assumed decision maker and the goals. Adaptive management assumes a unitary decision maker who sets goals that persist for the life of the program. In contrast, Deliberation with Analysis assumes a diverse decision maker with goals emerging from collaboration and subject to change.
I found this distinction interesting because the description of AM given in the book is a dead ringer for what I've called the "North American School" before, and more recently my student Jamie McFadden described as the "Experimental Resilience School" in a forthcoming paper. This isn't surprising as Kai Lee was one of the panel members for the report. Deliberation with Analysis sounds much like Adaptive Co-management to me - clearly there are some linkages to follow up on.

Population Viability Management

One of the most difficult things to agree on is what the goal of management ought to be - and the more controversial the species the harder this is! Maybe its not controversy, but rather what we ("we" meaning society as a whole) have to give up as a tradeoff to meet perceived goals. For harvested species there are an increasing number of examples in both marine and aquatic systems of coupling population models with management to help resolve uncertainties - the North American waterfowl harvest management plan is the best terrestrial example. In those cases, there is a clear and desirable goal - to be able to continue harvest into the future. Similarly, it is pretty straightforward to work out the ideal goal for an invasive species - zero. Species in need of conservation are trickier - more of them isn't obviously better for society (as food or recreation) even if having none of them is clearly bad. This sets the stage for scientific uncertainty about a species to take on political dimensions - even if everyone agrees we don't want a species to go extinct, ones willingness to accept new management prescriptions is negatively related to how much you personally will have to change behavior as a result. In the most recent issue of Frontiers in Ecology and the Environment, Victoria Bakker and Dan Doak argue that using population models to predict relative extinction risk can help make these tradeoffs - they call it "Population Viability Management" - but their diagrams and arguments are pure Department of Interior Adaptive Management. They give an extended and detailed example using Channel Island Foxes of how PVA models can improve monitoring, guide management actions, and generally make the world a better place. It is a really great review of the recent literature on how population models can be integrated with management decision making.

The only thing I was a bit disappointed by was the way Bakker and Doak skirt past the issue of tradeoffs - although they mention that cost-viability tradeoffs occur, in their example they have chosen not to evaluate them. Any actions that exceed the current available budget are not evaluated. In that sense, the tradeoff IS made, and at a rather extreme level. I agree with their assertion that conducting a full analysis of the ecological risks makes the economic assessment more meaningful - and thus all the more surprising that such ecological assessments are not conducted more often.

They conclude with a reference that I'll have to pursue - about how iterative cycles of explanation and improvement ultimately led to uptake of the recommendations of the model by managers - what they call the "handshake approach".

Bakker, Victoria J. and Daniel F. Doak. 2009. Population viability management: ecological standards to guide adaptive management for rare species. Frontiers in Ecology and the Environment 7:158-165. doi:10.1890/070220