I spent a little time working out what Clark’s Causal Calamity might look like as a well-formed causal loop diagram. Here’s an attempt, based on little more than spending a lot of time wandering around the Greater Yellowstone ecosystem:
The basic challenge is that there isn’t a single cycle that encompasses the whole system. Grizzlies, for example, are not involved in the central loop of pine-cone-seedling dispersal and growth (R1). They are to some extent free riders on the system – they raid squirrel middens and eat a lot of nuts, which can’t be good for the squirrels (dashed line, loop B5).
There are also a lot of “nuisance” loops that are essential for robustness of the real system, but aren’t really central to the basic point about ecosystem interconnectedness. B6 is one example – you get such a negative loop every time you have an outflow from a stock (more stuff in the stock -> faster outflow -> less stuff in the stock). R2 is another – the development of clearings from pines via fire and pests is offset by the destruction of pines via the same process.
I suspect that this CLD is still dramatically underspecified and erroneous, compared to the simplest stock-flow model that could encompass these concepts. It would also make a lousy poster for grocery store consumption.
My son spotted this hilarious CLD in the local coop:
In this case, CLD stands for “confusing loop diagram.” Here’s how things really work:
- Grizzly bears lay pinecones.
- The cones hatch into nutcrackers.
- Nutcrackers spontaneously combust, causing wildfires.
- Like the phoenix, seedlings emerge from the nutcracker ashes.
- Seedlings mature into grizzly bears.
Hoisted from a comment by Luzi on Vi Hart on positive feedback driving polarization:
Can you find the point where the many positive feedback loops can be balanced or broken?
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’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.
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.
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).
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.
In this case, I think it’s quite literally Normal a.k.a. Gaussian:
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.
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:
CLD corrected, 1/9/17.