Center for Strategic Communication

[ by Charles Cameron — starting with the news, closing with Jay Forrester & the impact of systems dynamics on our understanding of cause and effect — a catchup post ]

Clearing the decks grom the last few days, I found this DoubleQuote in the Wild from Ferguson staring out at me from my twitter feed — suggesting just how intricately interwoven our world really is:


Souad Mekhennet has a piece titled Even the Islamists of ISIS are obsessing over Ferguson in the Washington Post:

You can understand if President Obama would rather talk about the fight against Islamic State militants in Iraq, where he has scored some victories, than talk about the unholy mess in Ferguson, Mo. Surprisingly, though, ISIS militants are following developments in the St. Louis suburb, and some of them would rather focus on that. According to interviews and social media, members of the group and sympathizers with its jihadist ideology are closely tracking the events in the St. Louis suburb, where protesters and police have clashed. In it, they see opportunity.

Here are a couple of ISIS-fan tweets:


Look, the point I’m making isn’t about Ferguson, it isn’t about the Islamic State, it has to do with the way that an event in one place whill have myriad unexpected effects downstream. The classic case which really opened my eyes to this was Aum Shinrikyo — the group that released sarin in the Tokyo subway system — sending a planeload of its members to Zaire in an attempt to collect Ebola samples for their biochem weapons labs.

Someone in a medium size yoga cult in Japan read the New Yorker and learned that Ebola esisted and was lethal, and the next thing you know there’s a religious terror group, led by a guy who reads Nostradamus, Asimov and Revelation — and has been granted a photo op with the Dalai Lama — working diligently to get that capability.

That was back in the last century, but Ebola’s in the news again these days, and it turns out that epidemiology needs to take into account pervasive belief in some affected corners of Africa that the whole business is a conspiracy designed to imprison Africans in “clinics” — the result being riots against at least one clinic, and blood-stained bedclothes and live virus carriers being dispersed into a poorly protected slum.

Epidemiology as theorized and modeled should be cleaner than that. But then there are other factors — in the case of polio, there’s CIA use of a vaccination team as cover for an attempt to obtain bin Laden’s DNA in Abbottabad, resulting in widespread rumors of conspiracy, refusal of vaccinations, and a resurgence of the disease.


Big question: how can you figure out the unknown unknowns represented by riots affecting quarantine? words spoken when a mic supposedly off is in fact on? the impact of large scale climate engineering.

One of the ideas that has most influenced me in my thinking about games, simulations and models over the last dozen or more years comes from Jay Forrester. I’ll quote him from section 4.1, Cause and Effect Not Closely Related in Time or Space, in his 2009 paper, Learning through System Dynamics as Preparation for the 21st Century, though I think I first ran across the idea in one of his books, probably Urban Dynamics (1969) or World Dynamics (1971):

Most understandable experiences teach us that cause and effect are closely related in time and space. However, the idea that the cause of a symptom must lie nearby and must have occurred shortly before the symptom is true only in simple systems. In the more realistic complex systems, causes may be far removed in both timing and location from their observed effects.

From earliest childhood we learn that cause and effect are closely associated. If one touches a hot stove, the hand is burned here and now. When one stumbles over a threshold, the cause is immediately seen as not picking the foot high enough, and the resulting fall is immediate. All simple feedback processes that we fully understand reinforce the same lesson of close association of cause and effect. However, those lessons are aggressively misleading in more complex systems.

In systems composed of many interacting feedback loops and long time delays, causes of an observed symptom may come from an entirely different part of the system and lie far back in time.

To make matters even more misleading, such systems present the kind of evidence that one has been conditioned by simple systems to expect. There will be apparent causes that meet the test of being closely associated in time and in location. However, those apparent causes are usually coincident symptoms arising from a distant cause. People are thereby drawn to actions that are not relevant to the problem at hand.

That stunned me. But it gets a little worse:

Comments such as I have just made about cause and effect carry little conviction from being stated in a text. Only after a student has repeatedly worked with models that demonstrate such behavior, and has had time to observe the same kinds of behavior in real life, will the idea be internalized and become part of normal thinking.

I don’t think that’s quite right, I think we’re now seeing generations arise for whom system dynamics and networked thinking seem progressively more “intuitive” — more in tune with the zeitgeist.

But the decision makers? As far as I can see, they are largely impervious to the kinds of thinking necessary to navigate our complexly interwoven envirorment.