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Modeling for Impact in the Era of Big Data

Modeling for Impact in the Era of Big Data

On the occasion of the field’s 50th anniversary, in 2007, Jay Forrester spoke to us (SDR 23:359-370) about prospects for the field of System Dynamics—his view of the situation summarized by the phrase “aimless plateau”.  He lamented our “slight impact” in government and placed the blame primarily on our failure to ask the big questions, to write books to influence the public (in the tradition of Urban Dynamics, World Dynamics, and The Limits to Growth), and to maintain rigor rather than “dumbing down” our models with “unwise simplification”.

In calling for courage and rigor in SD modeling, Jay was echoing the words of Dana Meadows and Jenny Robinson more than 20 years earlier, in “The Electronic Oracle” (Wiley 1985).

  Meadows and Robinson added a third component that they felt was critical for having influence in the public sphere, namely working collaboratively with other researchers and influencers.

Jay, Dana, and Jenny all hoped and believed that, if only we acted with quality and high purpose, the unique attributes of SD modeling would emerge and ultimately be recognized as more powerful than the intrinsically limited modeling paradigms of statistics, econometrics, and input-output analysis.  

I have to admit I’m not so sure.  Looking at the world of modeling today and who has influence, I’m struck by how statistical approaches still seem to have a strong hold on much of public policy—whether the subject is climate change economics, sustainable development goals, or even the (obviously dynamic and nonlinear) COVID-19 pandemic.  There are some exceptions, such as the Forrester award-winning work by Thompson and Tebbens on polio eradication, but such examples of strong SD public policy impact in recent years are few and far between. 

Consider, for example, climate change and its likely damages for the global economy.  The prominent Yale economist William Nordhaus (a vocal opponent of SD since the 1970s) first published work in this area in 1991, and in 2018 was awarded the Nobel Prize for it.  Yet, this econometric work is surprisingly narrow in its outlook, with its damage estimates biased downward, as described by the Australian economist Steve Keen in his recent article, “The appallingly bad neoclassical economics of climate change”. 

It doesn’t necessarily require an SD model to overcome some of the shortcomings of the Nordhaus work.   An influential study from 2015 is also statistical, but looks at data not only cross-sectionally across many geographical locations (as Nordhaus does) but also longitudinally across 50 years (1960-2010).  This study’s estimates of economic damages over the next several decades are at least ten times greater than those of Nordhaus.  This longitudinal analysis does not shy away from data but dives further, and more productively, into it.

Conclusions?  First, it seems to me that if one is hoping to impact public policy these days, it is important to draw from a large number of cases across diverse locations—the more the better.  This is the era of Big Data, after all, and your model needs to be calibrated and applied to many separate cases to prove its worth if you want to influence the public conversation. 

A good recent example is a study of the effect of weather and air pollution on COVID-19 transmission.  This collaboration between SD modelers and quantitative health scientists drew from data on more than 3,700 different locations around the world, and it garnered significant public attention.

Second, I think we should emphasize our ability to work with longitudinal data.  We know (more than most statisticians and economists do) how to move from longitudinal data to properly estimated parameters, and how to situate these parameters within robust dynamic models.

Of course, we should always remember the importance of courage, rigor, and collaboration in modeling.  But, these days, to be heard widely in the public forum we must also employ both cross-sectional and longitudinal data and calibrate our models to many individual cases when possible.

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