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Question 6

Lil Na'ia Alessa
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Lilian Na'ia Alessa
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.

Matthew Sturm
I would distinguish two types: solo synthesis (Darwin) and group synthesis. In both cases, there is the ability to ingest and sort through a lot of seemingly disparate data. The end result of successful synthesis (or at least one hallmark) is that it launches other efforts.