Credibility eliminates uncertainty

or rather - honestly incorporating uncertainty into forecasts or projections can undermine the credibility of the forecast. And worse:

It is known that perceived usefulness (which presumes credibility) in the eyes of policy makers often depends on whether the forecast (or other expert input) corresponds to the potential user’s preconceived beliefs, ...

This from the 2005 report of the National Academy of Sciences on decision making for the environment.

I knew it! Expressing prior beliefs as prior distributions is important. Now, what to do about undermining your own credibility by demonstrating the effects of users beliefs on your forecasts .. .

Predictions, Forecasts, projections and scenarios

OK, so looks like the theme for the week is why I'm not making predictions. Or rather, that I am making predictions but really I ought to be building scenarios. Audrey Coreau and colleagues have a nice paper in Ecology Letters (2009 12: 1277–1286) on "The rise of research on futures in ecology". Their essential thesis is that we need to spend more time talking about what hasn't happened yet, i.e. the future, because it will help strengthen the basics of our science. Oh, and incidentally it will help with decision making. They build on the distinctions MacCraken made between predictions, forecasts, projections and scenarios primarily emphasizing the value of qualitative scenarios for developing possible futures. They do see a role for predictive models within those scenarios, so phew, I'm not out of a job yet.

I find their definition of a projection curious: "a statement about what would happen, based on the extrapolation of past and current trends (e.g. population projections)." The way they use the term projection it is based on observed data extrapolated out into the future - assuming no change in what is presently going on. This is consistent with how Hal Caswell describes matrix projection models of populations, although those are quite different from simple extrapolation based on trend data. What makes this definition curious is that it is different from how MacCraken defines a projection: "a projection is a probabilistic statement that it is possible that something will happen in the future if certain conditions develop. " Specifically - what happens if conditions ARE NOT the same as they are now, and in particular, if conditions are affected by management options between now and then.

Hmm, so I guess I'm making projections sensu MacCracken.

Forecasts are not predictions!

I've been wrestling with what to call the outputs of my models - forecasts or predictions. Whenever I have one of this epistemic debates with myself, my writing suffers because I start using the terms interchangeably, much to the detriment of clarity. I just stumbled across an old Webzine article on one view of the distinction between these terms, from of all people, a weather guy - Mike MacCraken. Or actually a climate guy. Anyway - interesting viewpoint that essentially makes a forecast conditional on the person making it - independent of how they arrived at the forecast. Thus one key difference is that the prediction is conditional on the methods. If a mathematical model is used then if the assumptions are true, then the conclusion is true. Can't argue with that. But when a prediction is used to make a forecast, then the credibility of the person making the assumptions comes into play.

Food for thought. Still don't know whether to use forecast or prediction.

Communities of practice

One of the ideas that I first encountered at the National Conservation Training Center (NCTC) during the first meetings that tried applying structured decision making to conservation problems from the USFWS was the notion of a "community of practice". There sort of is one based on the SDM workshops, loosely operating through a mailing list managed at the NCTC. This list is great for announcing things, and occasionally asking questions, but doesn't generate alot of traffic or two way interactive dialog.

I think this notion of a CoP for AM/SDM in conservation/environmental management is crucial if we're going to get better at what we do. I participate in a couple of other communities - the R-Help list and www.plantedtank.net. Both are publicly accessible, unlike the SDMCOP mailing list, although in order to add material you have to register - which means at least providing a valid email address. This provides for a mixture of public and private participation, as well as many different levels of participation - both aspects that are thought to promote successful communities of practice.

I think one of the things a CoP website/forum could do is provide a place to vent about things that don't work - although maybe that's better not done in public ... nonetheless it would be great to be able to write about successes, failures, and works in progress. There is a tremendous amount of experience out there that is growing rapidly, but it is hard to get it exposed in traditional academic outlets.

The three bombs

Nonlinearity and uncertainty - great editorial in the NY Times by Tom Friedman - describing the three bombs of the nuclear, climate, and debt threats. I hadn't heard the debt bomb described that way before, but certainly seems like a plausible mechanism to me. Perhaps the first part of that threshold is the shift to price oil in something other than US$.

AM for parks and uncertainty vs. risk

Tony Prato is an economist at the University of Missouri-Columbia who works on Adaptive Management. Last summer he published an article in Park Science on applying AM to a management question in a national park. What is particularly interesting to me is his distinction between risk and uncertainty - a distinction often made by social scientists but rarely if ever made by ecologists. It was also interesting to see how he deals with the uncertainty case. Although he set out to argue that experimental manipulations of management actions are highly desirable, his example glossed over the experimental part completely.

Adaptation science needs adaptive management

Climate change is real, its coming and we won't stop it, so we need to do some science to adapt our current natural resource management practices to it. Meinke et al (2009) lay out a framework for doing that in an article in a new journal "Current Opinion in Environmental Sustainability". What I find interesting is that their framework teeters on the brink of describing adaptive management, but never quite gets there. The notion is that once we evaluate an adaptation option, and call it good, it will be implemented. No uncertainty. No need to iterate. I think this arises from the agricultural science background of the paper, where replicated experiments are the rule, and uncertainty comes from variation in the external environment, rather than the dynamics of the system itself. Not true for ecological systems, I fear!

Holger Meinke, S Mark Howden, Paul C Struik, Rohan Nelson, Daniel Rodriguez,
Scott C Chapman, Adaptation science for agriculture and natural resource management
-- urgency and theoretical basis, Current Opinion in Environmental Sustainability,
Volume 1, Issue 1, October 2009, Pages 69-76,
ISSN 1877-3435, DOI: 10.1016/j.cosust.2009.07.007.