Is venting blogging?

This morning we had our weekly IGERT colloquium. Two of my cohort people presented. The first presentation was clearly well prepared . The person knows a lot about the subject and has some potentially very profitable ideas. Essentially he wants to create a decision supprt software to make remote sensed data from drones accessibly to precision farmers. The thing is, it isn't academic. His question is essentially how can we

'make big data usable to farmers?' and his answer is -with software. Why does this bother me, you might ask? Because at the end of his presentation, Shafik, who was nearly falling out of his chair exclaimed that 'this is what will define our generation'. That he sees the analysis of big data as the key. And then he offered up that  'That no, it isn't academic, it's actionable, and that's what we need'. How lame am I- I want academic. I came here for academic. If I wanted to design cool software  i wouldn't spend 5 years with nearly no income here. I want deep,  interesting, and currently relevant questions - I want peer critique and rigorous methods. I am trying hard to hang on, but want to scream! It isn't that there aren't great and intelligent people all around me, it's that I feel like a boat without a rudder, floating out...

-ok. I feel better now. Thanks blog world.

Flooding and alternative forms for risk assessment

So over the last series of blogs I have written down nearly a dozen ideas for what we can discuss, research and write about. This is one I am really serious about- I see it as the core of my research- but I don't know what it looks like really- just a jumble of ideas. 

As you may know one component of my research interest is to show that there is a mismatch between actual and 'expert defined' flood risks. By that I mean, as an example, fema's flood plain maps. My interest is not in 'updating' the models to get a more accurate fit, but rather integrating a plurality of 'assessments' or conceptual models of risk that are more 1) inclusive (of heterogenous perspectives) 2) dynamic (update or adapt based on new knowledge) and 3) owned (again, just a jumble if ideas, but here I am thinking of both active choice, or consensus building, or even par in that people have an active voice in decision making and have 'agreed'(committed) and that the decision is context specific, relevant, or fit. The (big!) question I have is how I can design a process for developing this alternative measure of flood risk?

Harvard risk symposium day 1

Panel 1. Cox and wang

This panel had both intensly over complicated presentation (almost textbook examples of how not to present) and fantastically relevant ideas to collective risk management under high levels uncertainty. Cox is someone who it might be very interesting to talk to further. His motto was Discover, don't guess what works best. While not directly talking about collaborative adaptive managent he alluded to two important dimensions- how when risks are highly uncertain, a voting majority will often result in suboptimal decision making. And two, that experimentation and revision is key to responding to uncertain outcomes- we must continually learn from our previous decisions. The other speak whose presentation was highly relevant was yang - he came from the marketing world - he asserted that in situation of high vulnerability, individuals rely more heavily on anecdotal evidence that analytics (statistics). During the break I talked to him a fair bit to see if -1) these relationships (or trends) might be consistent in collective decisions and 2) what the role of social capital or network might play in tempering these affects. 

There was a third presenter from Finland who was difficult to understand and had highly complex slides - but I think his article would be very useful to take a closer look at- he talks about ideals of decision making - respond more to theory than practice. 

Panel 2. Issues of omission 

Again, two presenters stood out - bansback  talked about testing the impact the including imprecision measures has on public opinion (stated preferences for treatment). It was shown that the exclusion of precision was more effective than quantitative inclusion. Falzer discussed how treatments generally fall into three categories - recommend (white area), don't recommend (black area) and everything else (grey area). He discussed alternative methods for making decisions within the grey area, including aleatory decisions, judgment, and epistemic analysis. He explained how the traditional model relies only in epistemic analysis which does not necessarily reflect the gap in findings that needs to be addressed- In other words, the treatment passes the test, but it's the wrong test to apply. During the discussion section we discussed the importance of changing the cultural expectation around precision and certainty. Teaching patients to expect adjustments and to question implications. 

Panel 3. The last session I attended focused on transportation and occupational hazards - one of the speakers was Sadiq, the rest, whole interesting were not highly televant to my thinking.  During his talk he discussed the benefit of risk managers for institutions. I actually found issue with his method and results, but found his work very relevant and hope to look further into his article. Afterwards we spoke for a while, about everything from scenario planning to adaptive management and improvisation. I have his card and I want I plan to contact him for some follow up questions about strategies he sees for coping with uncertainty (I'll expand more on this idea in another blog)

Lastly, I sat next to michael hand from the usfs - actually from Missoula! So your neck of the woods ;) we talked a bit about this idea of scales, resilience and adaptation - we used the example of fire suppression to discuss how scales of intervention and risk perception can have Unintended consequences. We discussed the problem of 'selection bias' (I put this in quotes because it isn't quite this, so that term is just my place holder) but the idea that with large scale events, you are usually wrong much more often than you are right - and more to the point, very rarely right when you need to be. And that this has implictions on adaptation vs mitigation interventions. 

PAR tinkerings

I am writing an article for the Carolina Planning Journal special issue on "Innovative Collaborations" about NECAP that emphasizes the potential for participatory action research to build the capacity of communities to prepare for and manage climate change risks. While I had initially planned to steer away from PAR as a frame and to instead focus on "collaborative action research" (since I don't know if NECAP fits the mold of PAR as put forth by Flyvbjerg et al), Larry kiboshed this, saying I couldn't just go out and coin my own term, but that I need to connect my ideas and work to existing bodies of literature. Of course, I fought this, and of course (after thinking about it long and hard) I decided he is right.

Now, I am trying to revise my article to explain how NECAP is PAR. I have come around to see NECAP as fitting with the mold of PAR, at least in accord with to the Greenwood and Levin (2007) conception of action research, which they argue, must be participatory. But I am finding it challenging to draw lessons from NECAP that should inform people trying to do PAR. I think that NECAP is an excellent example of action research, I think it is a great example of what I am calling "research as capacity building," but I suck at inferring lessons and key ideas from the experience. What about NECAP should be broadcast to the world of academics and local stakeholders in terms of 1) you should do this kind of action research to help support local adaptation (and progress on other tricky planning issues) and 2) here are some guidelines to help you do it? There's so much there, but I DON'T KNOW WHAT TO SAY!

This ties into a bigger problem I am having right now, which is not being able to see the forest for the trees: I feel like the ideas and information in my head are such a mash up that I can make sense of anything any more. What should my dissertation focus on? I have all of these ideas I want to get at and include, and I have all of this research data to draw on, but I don't know what the heck to focus on and what is worth saying to the world. How does one go from lots of crazy bits and pieces floating around in one's brain to saying something meaningful to the academic and professional world?

An interesting thought popped into my head while writing this: I am a dialogic rationality person; I need to talk through the ideas floating around in my head and my perceptions and concerns to help make sense of them. Hence, why I love that we have our PhD thought blog and that I have a partner in PhD thinking. May we help each other overcome writers block when it hits, make sense of ideas when they appear, and generally move forward in our academic and professional pursuits!

PS: I left a comment on your last blog!

Feedbacks in risk frameworks

Risk is generally defined as threat times vulnerability, where sometimes vulnerability includes exposure and sensitivity, and some add adaptive capacity inside our outside vulnerability. And, adaptation options attempt to reduce vulnerability - which is more locally focused and short term, while mitigation points at threat, which is more global/regional and longer term. (Ie the Srex diagram). A frequent assumption (need to cite) is that vulnerability and resilience are two sides to the same coin - when you reduce vulnerability, you increase resilience (or vice a versa) but as we discussed before, and as taleb describes, in complex systems that depend on finer scale shocks, you can unintentionally reduce the resilience at a higher scale when you reduce vulnerability. Simply put, by reducing a local community's flood vulnerability you may be decreasing a region's resilience to flooding. This conception implies a feedback between adaptation and mitigation that is critical to consider when dealing with complex emergent patterns - do we need to consider an adaptation options' mitigative power? How does this cost/benefit play into the framework and ultimately the assessment?

Balancing adaptability with stability/predictability

Really, this is the last post for today

One other thought that I'm stewing over is: there is an inherent tension in the adaptive management literature around the need for socio-ecological management system to be adaptive and able to learn by doing, on one hand, and the need for stability and predictability (such as policies that keep people in line and enforcement) on the other. How do we reconcile these two facets, when adaptability goes in the face of our traditional bureaucratic governance systems and stability seems impossible in a totally decentralized, unregulated approach?

One idea I have found that might be useful is Eckley's (2002) idea of "dependable dynamism." As Noelle (who now is Noelle Selin, and is on my committee) writes: dependable dynamism is "the quality of assessment and decision-making processes that allows policymakers with ease to put off particular decisions for addressing in the future, with confidence that issues so put off will indeed be addressed later. The ability to modify such conclusions at a later time facilitates decision-making processes by offering a new dimension of compromise on both scientific assessment and policy decisions, and lowering the threshold of credibility necessary for decision-making."

While her definition of the term doesn't directly translate, I think the notion of dependably dynamism might be a concept to guide what we want to achieve in adaptive management--we want systems that are at once dynamic and adaptable, allowing local decision-makers to do what they need to do, but are also dependable, ensuring environmental quality, social justice, etc across space and time.

Should we develop this notion of dependable dynamism and how it relates to local governance (rather than international negotiations, which is what Noelle's paper is about)?

Reference

Eckley, N. (2002). Dependable dynamism: lessons for designing scientific assessment processes in consensus negotiations. Global Environmental Change, 12(1), 15–23.

 

Complex Adaptive Systems and Adaptation Governance

Ok, a second post for today (is that allowed?)

I won't elaborate on this now, but I am really curious about the connectivity between complex adaptive systems thinking and how we should go about doing climate change adaptation. Can we take some of the theory about what allows complex systems to be complex adaptive systems (e.g., decentralized self-organizing networks of diverse agents with many interactions and set rules of the game; short feedback loops that allow for localized learning; etc) and apply them to social systems to make some prescriptions for what adaptive systems that are able to cope with unpredictable changes such as climate change might look like? I don't know, but the idea intrigues me.

Measuring collective risk perception and concern, and a couple research ideas

One thing I found striking will reading for, preparing for, and writing my general exams is how difficult it is to effectively measure risk awareness and concern. The most standard ways of doing this are, at least as far as I can tell, to:

 1) ask people to rate their perceptions of risk via questionnaires (e.g., how risky do you think a nuclear accident is? Or, how risky do you think a nuclear accident is in relationship to a car accident?);
2) use "decision problems" along the lines that Tversky and Khaneman (1981) used in their seminal "The Framing of Decisions and the Psychology of Choice" article (e.g., what would you choose: X number of certain deaths or Y number of possible deaths? Morbid, I know); or
3) Do some sort of cost benefit analysis, where you ask people what is their willingness to pay (WTP) to avoid a certain risk or how much of a certain risk are they willing to accept (WTA) (e.g, how much money would you pay to not have a hazardous waste facility sited near you? Or, how much risk is too much risk for you to continue smoking?)

A couple interesting things here: First, all of these approaches really rely on self-reported estimates of risk tolerance/avoidance/concern. Questionnaires and WTP/WTA rely entirely on people saying how risky they think something is and/or their estimate of what they would pay to avoid a risk or how much risk they are wiling to accept. As such, these approaches are fraught with bias--does someone really know how much they are wiling to pay, when push comes to shove, to avoid having a hazardous waste facility sited next to them? I am pretty darn sure (and plenty of research shows) some people tend to over-report their WTP and others under-estimate what they would pay to avoid a risk. Similarly, if you ask me how risk I think a nuclear accident is, I might say one thing before an accident like Fukishima and something quite different immediately following a disaster of this sort. While it could be argued that the questionnaire is accurately capturing my risk perception at a given moment, this enormous subjectivity raises some interesting questions about the accuracy of this approach (as well as highlights the inherent subjectivity and, as I say, "subject to change-ivity") for measuring risk concern and awareness. Even decision problems, which force people to select between two options and compare what risk they would be more willing to take, and thus seem to have a bit more grit to them, don't really tell us what people would do in a real life risk decision-making situation. This is all particularly problematic given that research in psychology, cognitive science, and other human sciences shows that people's perceptions of risks are heavily influenced by a range of heuristics such as the "availability heuristic" (people tend to draw on information that is readily available, rather than doing a thorough analysis, when making decisions about complex information. Hence, why nuclear accidents seem so much more risky to many people after an accident like Fukishima) and the "proportionality effect" (people tend to prefer saving 100 lives out of 1000 rather than 200 people out of 2 million). Since we tend to fall back on these funny heuristics in evaluating complex information and risks, and our conception of, response to, and reactions regarding risk are largely subconscious, context dependent, and psychologically complex, self reported measurements of risk awareness and concern seem particularly fraught with hazard. And this is just in trying to measure individual perspectives on risk!

Now, let's take this question of how to measure risk perception and concern to the collective level. How do we measure a group's or a community's risk awareness and concern? Do we just aggregate the responses of all of the individuals into some group risk awareness and concern metric? Do we average across community willingness to pay to avoid a risk? Or do we do something different, such as have a group dialogue about risk and see where people come out at?

This last option is particularly interesting, since I have a pretty strong hunch that people's concern and awareness of a given risk might change noticeably as a result of their participation in a well structured dialogue about a particular risk, such as local climate change risk or flooding risk. Has any specific research on this been done? If not, we should do this, Michal. Get a group of people together, measure their individual perceptions of a risk, have them dialogue about the risk (with some information), then see where they come out at in their risk perception/concern. This is kind of like deliberative polling, but the whole idea is to analyze whether perceptions of risk can be directly influenced by communicative rationality/collaborative rationality.

Also and related, I think it would be really interesting to do some sort of a decision problem, along the lines of the Tversky and Khaneman model, with groups. What if we did a decision problem with individuals, and then asked them what they would do as a group, giving the group time to dialogue about whether they would choose option A or option B. Then we could contrast whether there is any difference (individual choices as contrasted with group choices) to see if communicative rationality/collaborative rationality influences risk awareness/concern/choices.

Ok, that's my big brain dump, it may not be nonsensical, but at least it's a start.

Assuming I pass my exams tomorrow, I'm super excited to keep adding to this blog of thoughts. If I don't pass, I might by in Idaho by this time next week becoming a ski and mountain biking bum.

Refernce

Tversky, a, & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science (New York, N.Y.), 211(4481), 453–8.

 

Dichotomies of current water news

For Jim wescoat's class we are asked to read current events focusing on water and report back to the group what we found interesting. I noticed who dichotomies in reviewing the news which I think reflect strongly (but poorly) on urban infrastructure in the us. The first is the simultaneous 3-year severe drought in California and accentuated withdrawals of lake mead while we have severe flash flood warnings in la. The second is the citizen based reform At the house to revise the revised FEMA insurance rates and europe's 2050 assessment of projected changes to flood risks. The reform is based on reducing insurance to low income households who are essentially grandfathered into a floodplain or reducing premiums so you don't pay as high as 1% of your coverage per year (I.e. if you have a $200,000 coverage you pay $2,000 a year in flood insurance). The report in europe showed how by as early as 2030 Europe may see extreme flood disasters on an annual basis. This stems form the convergence of increased urbanization and population growth, climate changes, and increase in wealth concentrated along floodplains. In New England, a report by the new England aquarium in 2012 projected that by 2030 in Boston, a 100 year flood  will occur ever 30 years (this number changes for different cities along the east coast).  how can FEMA stay (is it currently?) solvent if it has to pay out insurance claims at 3 times the frequency without raising premiums?

Need to add references.