Eliciting and valuing information

Before you can value information you have to have it, and Mike Runge, Sarah Converse, and Jim Lyons provide a superb example of expert elicitation from a structured decision making workshop on managing the eastern migratory population (EMP) of whooping cranes. They calculate the partial expected value of information for each of 8 hypotheses about why the EMP is experiencing reproductive failures, and use this to figure out which management action would provide the greatest benefit to learning. Interestingly (is that a real word? Apparently yes.), the strategy that maximizes the weighted outcome under uncertainty is not the one that produces the greatest partial expected value of information - thus there is a tradeoff to be made between performance and information gain. Cool stuff. A great example of expert elicitation and entry point for that literature, as well as an example of the value of information calculations I mentioned here.
I like that they clearly identified the decision maker (singular).

AM Entrepreneurship?

There's been much talk here at UNL about "entrepreneurship" - social, business, intellectual, you name it, its all about being entrepreneurial. So I found the notion of a "policy entrepreneur" very intriguing, and a connection to AM in the title of this article was all I needed to take the time to skim through it. Disappointingly, the connection to AM was flimsy and inconsequential, and solely based on the Experimental-Resilience school. The idea is that if you have someone who can influence the policy process you will have greater resilience, because the system can be more responsive. Although the term "policy entrepreneur" appears to have a lengthy pedigree, I'm still not entirely sure what they are after reading this article. They sound like effective "issue advocates", to use Roger Pielke Jr.'s term for people that try to narrow the range of options available.

Valuing Information

Information, we all want more of it to enable better decision making, but how much should we pay for it? There are always costs involved in getting more information - real monetary costs, as well as lost opportunities. Ken Williams, Mitchell Eaton and David Breininger recently published an article outlining in detail how to calculate various forms of the value of information. Here's the abstract:

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time.

This article is essential reading for anyone wanting to discuss the value of information in ecological management.
And in other news, it turns out that more information is not always better for starlings pecking colored keys to get their food:

Both human and nonhuman decision-makers can deviate from optimal choice by making context-dependent choices. Because ignoring context information can be beneficial, this is called a “less-is-more effect.” The fact that organisms are so sensitive to the context is thus paradoxical and calls for the inclusion of an ecological perspective. In an experiment with starlings, adding cues that identified the context impaired performance in simultaneous prey choices but improved it in sequential prey encounters, in which subjects could reject opportunities in order to search instead in the background. Because sequential prey encounters are likely to be more frequent in nature, storing and using contextual information appears to be ecologically rational on balance by conditioning acceptance of each opportunity to the relative richness of the background, even if this causes context-dependent suboptimal preferences in (less-frequent) simultaneous choices. In ecologically relevant scenarios, more information seems to be more.

So, past experience with context is good for sequential choices, but bad for simultaneous choices. This "less is more" effect would contribute to differences among stakeholders in preferences between options; we're almost always making simultaneous comparisons rather than sequential ones in AM.  Thanks to Gregory Breese for passing along the starling link.

My strongest memory of New Zealand’s South Island, unfortunately, will be driving two fisted, white knuckled down narrow streets twisting over insanely steep mountains, and crossing one lane bridges. Did I mention New Zealand is a country where high octane fuel is available at every pump? I finally realized that the “keep left” signs weren’t meant for foreign tourists, but rather Kiwis gleefully taking shortcuts around right hand bends at 140 kph.

Q: Why did the Weka (a native bird that looks a bit like a long legged chicken) cross the road?

A: Unlike the chicken, the Weka didn’t have a good reason to cross the road, but like all good Kiwis he doesn’t feel comfortable unless risking life and limb on the highway.

Back to the Great Plains now, for the Xmas season, turning in final grades, and preparing for next semester’s classes. Have a great holiday season everyone!

Schooling again

It seems pretty clear that the term “Adaptive Management” has gone the way of “Sustainable Development” – it’s popular so everyone wants to do it, and as a result there is a proliferation of interpretations of AM. Rather than waste time arguing over whose interpretation of AM is the right and true path, Jaime McFadden and I tried to identify attributes of different interpretations, and then classify exemplars in the literature into a few, or in fact two, schools of thought. At the time, we had a 3^rd category “other”, where we stuck everything that didn’t obviously fit in the other two. By nature I’m a lumper, not a splitter, so it causes me great pain, but I’ve concluded that there needs to be a 3^rd school of thought on AM.

Credit for identifying this new school goes to Mike Runge, and the “Redefining Adaptive Management” symposium that we partially sat in on at the ICCB meeting. The key attributes are 1) having measurable objectives, 2) carrying out a management action intended to move the system closer to the objectives, and 3) effectiveness monitoring to determine if the system has in fact moved closer to the objectives, and if not 4) try something else. For the moment, I’m going to dub this the Foundations of Success school, after the consortium of international conservation organizations that put together the FoS umbrella, and the ICCB symposium. In the Artificial Intelligence literature they call this “trial and error learning”. I'll write some more about this later. 

End of the story

I’m outta here, they are talking about effectiveness monitoring again. Although it is an interesting dataset - snares found per km walked in a forest park in Rwanda. Looks like they need to  control for observation effort - otherwise the ranger posts are attracting snares. 

Big Partnerships

Kim Lutz … or no, some other person talking about Environmental Flow Prescriptions: an adaptive partnership

This is a pilot project partnering with USACE on retiming flows from dams – gee, where have I heard that before.  They want to get both high peaks and subsequent low discharge periods as well. At least one of the projects has peak flows < 1 kcfs, so … although it looks like they include the Missouri River.

On the Savannah River, where to release, how much and when, what change to expect – phrased as a research project. They were surprised by fish response to a managed flow – warm water caused fish to move downstream – an example of learning! Trial and error, not AM.

On the Connecticut River they have management models of the system, a big one for the whole system, and a mini Stella model to interact with managers “hands on”. That’s cool. But no indication that the models make ecological predictions, or that they use them to predict the effects prior to choosing a strategy.

OK, so they've used this on some small rivers so far, not yet on the Missouri. 

Takehome lessons: translate between modelers and ecologists – OK, I’m not always easy to understand, I get it. Iterate stakeholder analysis – also a good idea.

Hmm, still not seeing any AM.