Old and new media have produced an unprecedented tide drawing us into a sea of competing mental models of the world. Even in a “post-fact world,” we link bits of information together to form these mental models, a.k.a. “intuition.”
Most of us try to fit new facts into established mental models, only occasionally testing our original assumptions, if ever. Can anyone’s mental models cope with the increasing complexities and myriad interconnections of our globalized economic, political and social system?
In a democratic society, the answer to that question is found in elections, which are an exercise in turning the levers of government over to individuals who have persuaded us of the rightness of their mental models, or at least the ruin that their opponents’ models would rain down upon us.
Successful politicians and corporate CEOs play their part by exhibiting extraordinary confidence in their own intuitive grasp of the complex issues facing the institutions and constituents they represent. Yet, in spite of the power that democracies and corporate cultures grant to these individuals under the rubric of “leadership,” history is clear that the mental models emerging from these crucibles offer no guarantee of avoiding unpredictable, disappointing, or even disastrous outcomes.
Classic examples are easy to find. The US invasion of Iraq was supposed to bring about stable democratic institutions; instead, among other problems, it opened the field to ISIS.
The most brilliant of autocrats are not immune to this phenomenon. Why did Napoleon attack Russia in winter?
The human tendency to mismanage complex systems extends to fields other than warfare. When wolves are removed from deer habitat for the benefit of human hunters, exploding deer populations succumb after destroying the food resources that sustained them.
Human social and economic systems are similar to complex ecosystems except that, as the historian Joseph Tainter pointed out, our society has been supercharged on a diet of high-density fossil fuels that have driven the human enterprise to unprecedented levels of complexity.
What is the consequence for the use of mental models in decision making and public policy? Typically one can find winners and losers as the result of any public policy or action and, as long as we can collectively afford to fail in some measure without undermining the entire system, decisions will be made and policies will prevail that achieve mixed results at best.
These mixed results produce an inevitable voter backlash, which in the US predictably lead to severe losses in mid-term elections for the party in power.
Although Donald Trump’s opponents may take solace in the likelihood that his highly intuitive approach to decision- and policy-making will fail in this manner (or worse), the cumulative effect of getting things wrong in multiple administrations have inevitable consequences for American global leadership and standards of living.
To make matters worse, peak oil (or peak energy return on investment) will continue to narrow economic and political options. There is little solace in staying slightly ahead in a race to the bottom.
The inadequacy of mental models was recognized more than 50 years ago by Jay W. Forrester (1918–2016), who was one of the first to use computer models to obtain insights into organizational behavior from corporations to the global system.
During the 1950s and 60s, Forrester’s work at the Massachusetts Institute of Technology gave birth to a field called “System Dynamics.”
The most famous of these dynamic models remains “The Limits to Growth,” of 1972, which was much vilified after its publication, but has proven to be far more accurate and comprehensive than conventional economic analysis in which energy and other physical resources are considered to be practically infinite.
System Dynamics recognizes that mental modeling is a poor method of understanding the world since the human mind can manage only a small number of parameters, and is overwhelmed when trying to keep track of their interactions as the system evolves with time (and, obviously, “alternative facts” can further gum-up the works).
Dynamic computer models help us by managing the low-level task of keeping track of the evolution of the variables, while the modeler observes the behavior of the system as a function of changing external inputs or internal correlations.
And if you understand the behavior of a complex system, you can often gently steer it by avoiding what Forrester called “pulling the levers in the wrong direction.” More recently, the field was expanded as a management tool, in particular by Peter Senge, under the name of “The Fifth Discipline.”
As Forrester pointed out, complex social, political, and economic systems share one key characteristic: They will react to new inputs in ways that almost always surprise us, often generating results opposite to those intended. There is a high probability that this fate awaits the new US federal tax code that President Trump and others in the Republican leadership have touted as tantamount to the second coming.
Complex social systems are counterintuitive, with the corollary that they “always kick back” as John Gall noted, and may kick back with a vengeance.
Unfortunately, Forrester’s insights have not made inroads into political and economic decision-making, nor in the media frenzy for stories, which remain rooted in the mental models of individuals, with all their limitations.
At least as a stopgap measure, New York Times columnist David Brooks was right to call for more humility in our political discourse, and there is no better reason than the inadequacy of all of our mental models to cope with the phenomenon that Joseph Tainter called the “energy-complexity spiral,” or the tendency of society to collapse under the weight of the complexity of its own creation.
“Make America Great Again” is the literal poster child for the absence of humility.
Even if we could agree on what greatness is, a failure to acknowledge all of the many uncertainties surrounding any policy goal should evoke only one response: skepticism on the same grand scale.
Teaser photo credit: By User:Wikimol, User:Dschwen – Own work based on images Image:Lorenz system r28 s10 b2-6666.png by User:Wikimol and Image:Lorenz attractor.svg by User:Dschwen, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=495592
