Here's my YouTube on the data deluge.  It's a big deal: http://youtu.be/dPEhq4Mpw24 .

This blows me away; I had no idea about these interconnected, synapse-like links from one tree to the next. 

Probably the clearest explanation I've read of the distinction between complicated, complex, and adaptive.  Well worth a read

This looks like a good platform on which to build Decision Intelligence: combining collaboration, GIS, videos, and more.

Barry's discovered what I call a "systems pattern":

"As I assay it, the ten most intractable plagues of western civilization are conflict, violence, oppression, injustice, corruption, poverty, ignorance, alienation, suffering, and terrorism.

All ten of these hellish problems have something in common. Like cancer, they tend to reseed themselves, round-robin, from one instance to the next, in a never-ending cycle of recursion.

Systemic problems call for a systems approach to problem-solving. But that’s not going to happen until we elevate our collective problem-solving skills to near-genius levels."

Yes!  Or maybe I should say "duh" :-)  Good job Julien.  "Ovolo Hotels takes a different path: only questions with a purpose are used. If the answer to a question cannot impact the business directly, the question shall not be asked."

This looks pretty interesting.  The idea of a community-based approach to implementing systematic change is relatively new to me; this group looks pretty informed on some of the complex systems dynamics.  I'll join, and read,and see what it's about.

Some bits that are particularly tantalizing:

"Our System is stuck in self-reinforcing feedback loops...the interactions between the multiple human and natural systems that make up our world, and the flows and accumulations they generate."

"We end up with resources attracted, captured and extracted by a few 'winners', cumulating advantage (opportunity, success, power, credit, capital) in a reinforcing feedback loop, dedicated to reinforce the mechanisms that maintain the status quo..."

"We need to embed the reproduction of distributed factors of opportunity & renewal in the code of the system at all levels and scales for the system to become generative rather than extractive."

I bet World Modeler could help.  I'd appreciate an introduction if anyone here knows folks in this group.

I've long talked about the "complexity ceiling" in my work, and now here's a study that illustrates it.  The actionable item here, is that when communicating complex social issues, we need to break them down into understandable pieces.  (Yet, if we stop there, we'll miss key dynamics: there must be a reduction/synthesis cycle).

From the article:

The participants who received the complex description indicated higher levels of perceived helplessness in getting through the economic downturn, more dependence on and trust in the government to manage the economy, and less desire to learn more about the issue.

“This is despite the fact that, all else equal, one should have less trust in someone to effectively manage something that is more complex”

Decision: Ban fisherman from throwing away unwanted fish

Intended consequence: Improve fish stocks

Unintended consequence: Decrease fish stocks

Analysis: A key link in the chain was not understood: discarded waste from humans is valuable in the ecosystem

To construct an effective decision model, which shows how actions lead to outcomes, we need to be sure to get our lines of causation right.  And in a world of Big Data, there are an awful lot of things that can appear to cause one another, but do not.  Such as per capita consumption of mozzarella cheese / industrial manufacturing doctorates.  Divorce rate in Mississippi / murders by bodily force.

Now, these may appear to be obviously ridiculous causal links, but there are many situations where we don't all share a common systems model that would rule these out.  So precipitation in Indiana may indeed cause more people to drown after falling out of a canoe or kayak.  Or maybe not. 

The graphs on this site help to illustrate the critical importance of a) making our causal assumptions explicit so that b) we can QA them (either through a systems modeling exercise or through the right randomized controlled trial (RCT) design).  Otherwise we may end up reducing California slip/tripping deaths by awarding more mechanical engineering doctorates.  Probably not a good idea :-)

Be sure to try the "discover a new correlation" link at the top of the page.

A case study for creating risk models and rapidly deploying them into production.

One of these things is not like the other: we must understand how a decision, once made, will ripple through interdependencies - whether across the earth or across our business (or both) to impact the future. We must decide how this cause-and-effect chain warps as external circumstances shift.  We must decide, and re-decide, in a complex dance.