Category: SystemDynamics

Life Expectancy and Equity

Today ScienceDaily brought the troubling news that, “There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population.” The full article is PLoS Medicine Vol. 5, No. 4, e66 doi:10.1371/journal.pmed.0050066. ScienceDaily quotes the authors,

Ezzati said, “The finding that 4% of the male population and 19% of the female population experienced either decline or stagnation in mortality is a major public health concern.” Christopher Murray, Director of the Institute for Health Metrics and Evaluation at the University of Washington and co-author of the study, added that “life expectancy decline is something that has traditionally been considered a sign that the health and social systems have failed, as has been the case in parts of Africa and Eastern Europe. The fact that is happening to a large number of Americans should be a sign that the U.S. health system needs serious rethinking.”

I question whether it’s just the health system that requires rethinking. Health is part of a complex system of income and wealth, education, and lifestyle choices:

Health in context

Continue reading “Life Expectancy and Equity”

On Limits to Growth

It’s a good idea to read things you criticize; checking your sources doesn’t hurt either. One of the most frequent targets of uninformed criticism, passed down from teacher to student with nary a reference to the actual text, must be The Limits to Growth. In writing my recent review of Green & Armstrong (2007), I ran across this tidbit:

Complex models (those involving nonlinearities and interactions) harm accuracy because their errors multiply. Ascher (1978), refers to the Club of Rome’s 1972 forecasts where, unaware of the research on forecasting, the developers proudly proclaimed, “in our model about 100,000 relationships are stored in the computer.” (page 999)

Setting aside the erroneous attributions about complexity, I found the statement that the MIT world models contained 100,000 relationships surprising, as both can be diagrammed on a single large page. I looked up electronic copies of World Dynamics and World3, which have 123 and 373 equations respectively. A third or more of those are inconsequential coefficients or switches for policy experiments. So how did Ascher, or Ascher’s source, get to 100,000? Perhaps by multiplying by the number of time steps over the 200 year simulation period – hardly a relevant measure of complexity.

Meadows et al. tried to steer the reader away from focusing on point forecasts. The introduction to the simulation results reads,

Each of these variables is plotted on a different vertical scale. We have deliberately omitted the vertical scales and we have made the horizontal time scale somewhat vague because we want to emphasize the general behavior modes of these computer outputs, not the numerical values, which are only approximately known. (page 123)

Many critics have blithely ignored such admonitions, and other comments to the effect of, “this is a choice, not a forecast” or “more study is needed.” Often, critics don’t even refer to the World3 runs, which are inconvenient in that none reaches overshoot in the 20th century, making it hard to establish that “LTG predicted the end of the world in year XXXX, and it didn’t happen.” Instead, critics choose the year XXXX from a table of resource lifetime indices in the chapter on nonrenewable resources (page 56), which were not forecasts at all. Continue reading “On Limits to Growth”

The blank sheet of paper

I’ve been stymied for some time over how to start this blog. Finally (thanks to my wife) I’ve realized that it’s really the same problem as conceptualizing a model, with the same solution.

Beginning modelers frequently face a blank sheet of paper with trepidation … where to begin? There’s lots of good advice that I should probably link here. Instead I’ll just observe that there’s really no good answer … you just have to start. The key is to remember that modeling is highly iterative. It’s OK if the first 10 attempts are bad; their purpose is not to achieve perfection. Colleagues and I are currently working on a model that is in version 99, and still full of challenges. The purpose of those first few rounds is to explore the problem space and capture as much of the “mess” as possible. As long as the modeling process exposes the work-in-progress to lots of user feedback and reality checks, and captures insight along the way, there’s nothing to worry about.