Bookmarks – You haven't been paying attention

But you better pay attention to this, especially the systems theory one on leverage points. Don’t trust me, I’m an expert.

This is a huge, unnecessary, and expensive loss of talent in a field facing a supposed talent shortage. Given that tech is currently one of the major drivers of the US economy, this impacts everyone. Any tech company struggling to hire and retain as many employees as they need should particularly care about addressing this problem.

If you think women in tech is just a pipeline problem, you haven’t been paying attention by Rachel Thomas (372 words).

Counterintuitive—that’s Forrester’s word to describe complex systems. Leverage points frequently are not intuitive. Or if they are, we too often use them backward, systematically worsening whatever problems we are trying to solve.

I have come up with no quick or easy formulas for finding leverage points in complex and dynamic systems. Give me a few months or years, and I’ll figure it out. And I know from bitter experience that, because they are so counterintuitive, when I do discover a system’s leverage points, hardly anybody will believe me. Very frustrating—especially for those of us who yearn not just to understand complex systems, but to make the world work better.


Numbers, the sizes of flows, are dead last on my list of powerful interventions. It’s not that parameters aren’t important—they can be, especially in the short term and to the individual who’s standing directly in the flow. People care deeply about such variables as taxes and the minimum wage, and so fight fierce battles over them. But changing these variables rarely changes the behavior of the national economy system. Whatever cap we put on campaign contributions, it doesn’t clean up politics. The Fed’s fiddling with the interest rate hasn’t made business cycles go away. After decades of the strictest air pollution standards in the world, Los Angeles' air is less dirty, but it isn’t clean. Spending more on police doesn’t make crime go away.


Balancing feedback loops are ubiquitous. Nature evolves them and humans invent them as controls to keep important stocks within safe bounds. A thermostat loop is the classic example. Its purpose is to keep the system stock fairly constant near a desired level. Any balancing feedback loop needs a goal (the thermostat setting), a monitoring and signaling device to detect deviation from the goal (the thermostat), and a response mechanism (the furnace or air conditioner, fans, pumps, pipes, and fuel).

Leverage Points: Places to Intervene in a System by Donella Meadows (6996 words).

Just read the damn thing. It’s awesome.

If you consider yourself an expert in something or another, you might want to stop pretending you understand things you’ve never heard of. In a new study, researchers found that self-proclaimed “experts” in a topic were more likely than others to profess knowledge of terms that were actually made up for the purpose of the study.

Self-proclaimed ‘experts’ more likely to fall for made-up facts, study finds by Rachel Feltman (541 words).

The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about ­correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”.

But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down. One explanation of the Flu Trends failure is that the news was full of scary stories about flu in December 2012 and that these stories provoked internet searches by people who were healthy. Another possible explanation is that Google’s own search algorithm moved the goalposts when it began automatically suggesting diagnoses when people entered medical symptoms.

Big data: are we making a big mistake? by Tim Harford (3315 words).

have reached the point where browser vendors have to start implementing or aliasing these WebKit prefixes just to allow their users to browse the Web, see Mozilla in Gecko and Microsoft in Edge. The same thing is happening over again. In the past, browser vendors had to implement the quirks of IE to be compatible with the Web. As much as I hate it, we will have to specify the current -webkit- prefixes to implement them uniformly.

Vendor Prefixes And Market Reality by Karl Dubost (1219 words).