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Lilian Na'ia Alessa

Response: Hmmm...let me think on that. Doing this at lightspeed. More later.

Response: I think the question is loaded. Let's rephrase it to "a. does synthesis imply that there may exist greater degrees of complexity in arctic processes than previously thought and b. will synthesis yield insight into emergent patterns/processes and guidelines for them"?

I would argue that significant ommissions

Response: I think we need a separate forum on the issues of SCALE. Too much to write here in short time.

Response: Yes and yes but on much smaller scales (see our website http://ram.uaa.alaska.edu).

I believe that I was also taught to "synthesize" as a child by playing a game where words were hidden in songs. The game was that the words would pop up randomly in the song and constituted a description of an entirely different thing or process (or set of processes). You were supposed to pick them out (but not write them down) and then tell the singer what it was. Anyhow, was hard but fun!

Response: Yes, my Grandmother (T'te), my Mother and my Aunts. They were the model that married logic (in their case practicality) with intuition. What made them good is that "outlying data" were merely indicators of processes that were not yet known or understood (in their case, phenomena were little flags that suggested an unfamiliar process was at work but still existed as a process nonetheless).

They also cautioned me to remain humble, that accepting something unusual was a way to acknowledge that we (humans) could not approximate the Creator.

Translated for application in this forum's context: our scientific process is often forced to simplify and from this simplication draw conclusions that we extrapolate to broader scales and other processes without factoring in scaling issues (have I mentioned scale? :).

While this is mainly due to our very real need to simplify things in order to begin to understand them, it has created a culture of hubris where "understanding" relies too strongly on statistics and linear equations. In a holistic (i.e., complex) system, statistics become less important than emergent patterns.

We've not yet developed a great mathematical framework to capture the operationalization of complexity in the 'real' world (i.e., outside computer runs) and this is a need that is being gradually met. Still some way to go.

Response: The process that "reduces complexity" is called linear equation modeling and it would be great if we could evolve beyond this (though not to its exclusion).

Humility (see above): and the flexibility by an agent (in this case a scientist) to accept that there are many different disciplinary perspectives that can yield insight IF they are accepted as useful until proven otherwise. Some may call this a cultural paradigm shift, others have referred to it as a ontological revolution.

Subsequently, it requires the agent (scientist) to re-educate itself such that it's aware of approaches and technologies that enable diverse data collection, analyses and integration. Awareness doesn't have to be understanding but an idea of which other agents to collaborate with in order to achieve a goal. I know many folks find this aspect of approaching a system differently intimidating.

The "goal" should also be flexible without compromising focus. In other words, develop a broad conceptual framework within which approaches are clearly articulated. Gather data/information which apply to your system and synthesize it. From this point, develop a "map" (you've heard this from me before) that takes these data and spatiotemporally anchors them. This is your "starting" point. It helps reduce redundancy, optimizes existing knowledge and, in some cases, reveals patterns that would otherwise not emerge on their own.

So summary in a nutshell:
1. Gather existing data, compile.
2. Map to determine gaps and strengths.
3. Fill gaps through std'zed, systematic data collec'n. Analyze both as output (numeric) and pattern of process.
4. Integrate into your "map". Use a robust platform that allows diverse data to be incorporated i.e., so you can articulate and analyze "pattern" and "process".
5. Repeat.
6. Attempt to see if there are broad "rules" or "guidelines" for your system at the appropriate scale.
7. Determine modifiers (e.g., topography, policy, technology, etc.).
8. Quantify modifier output.
9. Go back to Step 6.
10. Repeat.

Don't quote me on those but that's roughly what we do...have to go back and check for the details.

We should have a SEPARATE FORUM JUST TO DISCUSS SCALE!! Rules and guidelines start "falling apart" at local and fine scales for ARCSS.

Response: 1. We should have a SEPARATE FORUM TO DISCUSS SCALE and SCALING ISSUES. This is a serious issue which warrants attention particularly w.r.t. "synthesis".

2. I have been interested in what I perceive to be the dyanmics of researchers who (as agents) are facing changes in their resources (usually funding $$s) as a consequence of national and global shifts in priorities and attitudes. My research is yielding insights toward how communities can cope with resource constraints by cooperating. In times of plenty you have more fish, in times of empty, you have less fish...but still fish.

You may shake your head as you read this but consider this: productivity in science is the yield of a Body of Knowledge. We are attempting to move toward a "Culture of Understanding" and so the dynamics of cooperation do affect the ability of our community to "synthesize and integrate" data. I believe they can be quantified to show that in cooperative networks their are higher yields and more "breakthroughs". I hypothesize that in these networks, when resources become scarce, agents are more willing to expend similar or even greater effort with fewer resources. The Canucks know what I'm talking about!
My two cents.

3. It's interesting that we fail to educate our science students in the worldviews of diverse scholars. In the late 19th century Georg Simmel postulated that society was not a "thing" but a process and then went on to essentially describe its interaction with the biophysical world as a feedback system (aka a complex system). Fiere in the 50s and 60s yielded the basis for "Holling's" resilience. I could go on and on.

4. (Philosophical)The challenges we're facing are in part instrumental/logistics, etc. and in part that we are products of a scientific culture which rejected process-driven science in favour for status-driven outputs. There are other things as well and this constitutes a challenging philosophical debate that questions how we can understand the complexities of our environment given that our brains are but one product of a set of emergent processes? Try this:
http://serendip.brynmawr.edu/local/scisoc/emergence/brain.html

and this for fun:
http://arxiv.org/PS_cache/cond-mat/pdf/9806/9806113.pdf

Sorry, about that tangent...signing off!