Happy E day

E, a.k.a. Euler’s number or the base of the natural logarithm, is near and dear to dynamic modelers. It’s not just the root of exponential growth and decay; thanks to Euler’s Formula it encompasses oscillation, and therefore all things dynamic.

E is approximately 2.718, and today is 2/7/18, at least to Americans, so this is the biggest e day for a while. (NASA has the next 1,999,996 digits, should you need them.) Unlike π, e has not been contested in any state legislature that I know of.

Vi Hart on positive feedback driving polarization

Vi Hart’s interesting comments on the dynamics of political polarization, following the release of an innocuous video:

I wonder what made those commenters think we have opposite views; surely it couldn’t just be that I suggest people consider the consequences of their words and actions. My working theory is that other markers have placed me on the opposite side of a cultural divide that they feel exists, and they are in the habit of demonizing the people they’ve put on this side of their imaginary divide with whatever moral outrage sounds irreproachable to them. It’s a rather common tool in the rhetorical toolset, because it’s easy to make the perceived good outweigh the perceived harm if you add fear to the equation.

Many groups have grown their numbers through this feedback loop: have a charismatic leader convince people there’s a big risk that group x will do y, therefore it seems worth the cost of being divisive with those who think that risk is not worth acting on, and that divisiveness cuts out those who think that risk is lower, which then increases the perceived risk, which lowers the cost of being increasingly divisive, and so on.

The above feedback loop works great when the divide cuts off a trust of the institutions of science, or glorifies a distrust of data. It breaks the feedback loop if you act on science’s best knowledge of the risk, which trends towards staying constant, rather than perceived risk, which can easily grow exponentially, especially when someone is stoking your fear and distrust.

If a group believes that there’s too much risk in trusting outsiders about where the real risk and harm are, then, well, of course I’ll get distrustful people afraid that my mathematical views on risk/benefit are in danger of creating a fascist state. The risk/benefit calculation demands it be so.

Reforesting Iceland

The NYT has an interesting article on the difficulties of reforesting Iceland.

This is an example of forest cover tipping points.

Iceland appears to be stuck in a state in which “no trees” is locally stable. So, the system pushes back when you try to reforest, at least until you can cross into another basin of attraction that’s forested.

Interestingly, in the Hirota et al. data above, a stable treeless state is a product of low precipitation. But Iceland is wet. So, deserts are a multidimensional thing.

A Bongard problem

Bongard problems test visual pattern recognition, but there’s no reason to be strict about that. Here’s a slightly nontraditional Bongard problem:

The six on the left conform to a pattern or rule, and your task is to discover it. As an aid, the six boxes on the right do not conform to the same pattern. They might conform to a different pattern, or simply reflect the negation of the rule on the left. It’s possible that more than one rule discriminates between the sets, but the one that I have in mind is not strictly visual (that’s a hint).

If you’re stumped, you might go read this nice article about meta-rationality instead.

I’ll post the solution in a few days. Post your guess in comments (no peeking).

Data Science should be about more than data

There are lots of “top 10 skills” lists for data science and analytics. The ones I’ve seen are all missing something huge.

Here’s an example:

Business Broadway – Top 10 Skills in Data Science

Modeling barely appears here. Almost all the items concern the collection and analysis of data (no surprise there). Just imagine for a moment what it would be like if science consisted purely of observation, with no theorizing.

What are you doing with all those data points and the algorithms that sift through them? At some point, you have to understand whether the relationships that emerge from your data make any sense and answer relevant questions. For that, you need ways of thinking and talking about the structure of the phenomena you’re looking at and the problems you’re trying to solve.

I’d argue that one’s literacy in data science is greatly enhanced by knowledge of mathematical modeling and simulation. That could be system dynamics, control theory, physics, economics, discrete event simulation, agent based modeling, or something similar. The exact discipline probably doesn’t matter, so long as you learn to formalize operational thinking about a problem, and pick up some good habits (like balancing units) along the way.

Snow is Normal in Montana

In this case, I think it’s quite literally Normal a.k.a. Gaussian:

Normally distributed snow

Here’s what I think is happening. On windless days with powder, the snow dribbles off the edge of the roof (just above the center of the hump). Flakes drift down in a random walk. The railing terminates the walk after about four feet, by which time the distribution of flake positions has already reached the Normal you’d expect from the Central Limit Theorem.

Enough of the geek stuff; I think I’ll go ski the field.

Rats leaving a sinking Sears

Sears Roebuck & Co. was a big part of my extended family at one time. My wife’s grandfather started in the mail room and worked his way up to executive, through the introduction of computers and the firebombing in Caracas. Sadly, its demise appears imminent.

Business Insider has an interesting article on the dynamics of Sears’ decline. Here’s a quick causal loop diagram summarizing some of the many positive feedbacks that once drove growth, but now are vicious cycles:

sears_rats_sinking_ships_corr

h/t @johnrodat

CLD corrected, 1/9/17.

Remembering Jay Forrester

I’m sad to report that Jay Forrester, pioneer in servo control, digital computing, System Dynamics, global modeling, and education has passed away at the age of 98.

forresterred

I’ve only begun to think about the ways Jay influenced my life, but digging through the archives here I ran across a nice short video clip on Jay’s hope for the future. Jay sounds as prescient as ever, given recent events:

“The coming century, I think, will be dominated by major social, political turmoil. And it will result primarily because people are doing what they think they should do, but do not realize that what they’re doing are causing these problems. So, I think the hope for this coming century is to develop a sufficiently large percentage of the population that have true insight into the nature of the complex systems within which they live.”

I delve into the roots of this thought in Election Reflection (2010).

Here’s a sampling of other Forrester ideas from these pages:

The Law of Attraction

Forrester on the Financial Crisis

Self-generated seasonal cycles

Deeper Lessons

Servo-chicken

Models

Market Growth

Urban Dynamics

Industrial Dynamics

World Dynamics