shanghai-night-wallpaper-4

The Magical Appearance and Disappearance of Stuff

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In the three years between 2011 through 2013, China used more cement than the U.S. used in the entire 20th century. These images below show Shanghai’s development of the same area of land from 1987 to 2013 as a result of exponential use of raw materials – specifically, concrete, steel, and glass.

Screenshot 2014-07-24 23.21.40
Shanghai in 1987 and 2013; Photographed by Carlos Barria

A key skill in systems thinking is the ability to think operationally – where does stuff come from and where does it go? People are inherently bad at doing this because we draw convenient boundaries in our mental models.

We eat delicious prime rib without thinking about the cow it came from. We in the developed world take long showers without seeing the complex network of pipes that provide water to our city. We throw away our trash into recycling, compost, and waste bins for the weekly trash collector to take away, and we don’t see where that waste goes. Our cars are built from steel, our roads from concrete. But we usually see these in their final form, ready for us to use to get to places.

Economists Predict Milk Production

Systems educator Barry Richmond shares that a well-known economic journal had published a complex econometric model predicting milk production in the United States, and yet one strange variable was missing: the number of cows. How sound can a sophisticated model be if it derives empirical conclusions based on correlations and fails to factor in the actual source of milk?

Richmond explains that an operational approach to predicting milk production would include [1]:

  • start with the number of cows
  • the reasons farmers would decide to increase or decrease herd size
  • factors affecting average milk productivity per cow

What if mad cow disease breaks out? What if an agricultural drought occurs and a large population of cows don’t have access to their usual feed? What if the cow population is multiplying exponentially and we have an oversupply of milk as a result? What then happens to the price of milk? Because milk doesn’t exist without cows, a thorough model must consider key factors that impact the cow population.

“Doing econometrics is like trying to learn the laws of electricity by playing the radio.” – Guy Orcutt

Operational Thinking vs. Correlational Thinking

The de facto tool (regression analysis) that economists use to make predictions relies heavily on correlations rather than operational thinking. Such models overemphasize the possibilities for economic growth and increasing GDP without paying attention to the physical limits – the natural resources, the amount of land, quality of our soil, and health of our climate that make production of new things possible.

Operational thinking, however, grounds the student in reality. It attracts people who strive to understand how things really work. And more importantly, it helps us identify the key levers to change the dynamics of a problem.


[1] “Systems Thinking: Critical Thinking Skills for the 1990s and Beyond” by Barry Richmond, 1993


Further Reading: Learn Systems Foundations

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