Quickie Post — Brother Leachman’s Thermodynamics of Creativity

Mike and Chuck Older - 1

That’s me and my older soul brother, Mike Beiser.  Though Jake did take this picture.  Who says you can’t have fun with your collaborators?  July 2016, Main Salmon River

Here’s another post from one of my chief collaborators, Jake Leachman, in the School of MME here at WSU.  It’s great stuff —  titled:

Social Thermodynamics: The mathematics of creativity

It’s not exactly for the faint-of-heart, so if you just got done with the Weight Loss post and thought I’d lob you another softball, well, uh, sorry.

For those who are into design, though, it’s great, in that it gives a field theory/probabilistic interpretation of averaged complexity that’s useful in creating larger organizational boundary conditions and forces for change.  As with all probabilistic modeling, it’s not so much descriptive on how individual actors inside a system work together to create new ideas.  But it’s more useful.  I’ve been badgering Jake to make up some Labview models of “virtual instruments”, with little dials and such on the front to show, quantitatively, how these variables are related.  Methinks a quickie creativity-meter looms in our shared future.    It’s a wicked combo — bright, innovative thinkers and me — the iconic old nag.

Contrast this with the deterministic work on creativity in social networks I’ve done, based on nonlinear differential equations.  One of the fascinating meta-things that this little comparison shows is also how the various v-Memes create knowledge structures that, over time, can converge to a larger truth.  Nonlinear differential equations are as Legalistic/Algorithmic as you get — one initial condition gives you one unique solution that may indeed be extremely complex.  But running a bunch of these to make sure it’s the only answer takes forever.  Contrast that to the probabilistic approach of thermodynamics, more at home one v-Meme level up, in the Performance/Goal-based heuristics space.  Here, thermodynamics inherently works on averages inside a larger system boundary.  More compact and potentially easier to calculate.

While both approaches give insight, I’m going to go out on a limb here and say that Jake’s approach might be more useful for setting organizational policy, whereas the nonlinear ODE understanding is more useful in understanding individuals interacting, and create lower level training.  And that would also map to the insights of the v-Memes.  Legalistic/Absolutistic v-Memes for rules for an organization, and Performance-Based thinking for larger goal setting.

It’s all great!  We’re figuring it out!

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