Last year my colleagues Peter and Patrick and I took our university’s community oral history project to the two local rallies for Donald Trump. We talked with a number of the ralliers in what might be described as a naive, unstructured ethnographic style. Recordings led to transcripts (thanks, Patrick!), and then to a proposal to present our findings as this year’s faculty research lyceum (thanks again, Patrick!). We got the gig.
Each of us has his own take on what is, of course, not so much a ‘data set’ (let alone a ‘representative sample’) as a particular interactive assemblage, a massively contingent co-production. We conducted the interviews as interested parties and with leading ideas about what was happening; we interpret them now with those same ideas and all of the resources of partisanship, prejudice, bias, selective perception, agenda, etc. etc. at our disposal. We are not reliable narrators. But as historians we are used to speaking for the dead. And for the living we think talking with people, taking them seriously, and trying to understand them is better than any alternative we are aware of.
The other thing that’s been on my mind lately is my sabbatical project on the history, theory, and pedagogy of complex adaptive systems. So of course what I’m doing with these interviews is to mash them up with the complex systems stuff. The general question I’m asking of the data then is, ‘How do these folks (seem to) think things work’?
We’ve got about 8 minutes each. Here’s the rough draft I just put together for my partners and the commenters. I’ll be filling in citations and interview quotations next, and I can tweak the whole thing until the actual presentation later this month. So, comment is welcome:
I’m interested in what we think about how things work. When I’m not interviewing Trump ralliers, my research is on the history and theory of societies as complex adaptive systems. People have always noticed that social processes do not seem to correspond very well to simple cause and effect explanations, or to respond very well to simple cause and effect engineering. Social processes routinely go sideways and defy prediction and control, much like the weather. Back at the tail end of the Renaissance Machiavelli warned the Prince about this ‘fortuna’, and some kind of ‘fortune’ or ‘luck’ explanation is one of the more common ways of accounting for the wonkiness of social processes.
We now know that with the weather, even short term unpredictability is because there are many systems actually involved in the ‘weather system’, all of them are active and effective but none of them are in control, they are all oscillating and linked and dynamically interdependent, and there’s lots of feedback that can amplify very small causes into very large effects, or dampen very large causes into very small effects. This disparity between causes and effects is called ‘nonlinearity’. It is characteristic of complex systems, as are self-organization (there is no designing hand at work) and emergence (the whole is more and other than the sum of the parts).
Plans are worthless, but planning is everything, Ike Eisenhower remarked. Despite Machiavelli’s early attunement to the issue and the routine awareness by better leaders and strategists that you have to expect the unexpected, getting serious about grappling with societies as complex systems that work a lot like the weather has been slow going. For one thing, we have a species prejudice that our reasons and intentions are different and more effective kinds of causes than ocean currents and snow melt. And for another, our own evolutionary adaptation disposes us to act on simplifications rather than get lost in complexity. In most action windows there’s not much advantage in prediction or control to be gained by sorting through dozens, hundreds, or thousands of oscillating, interacting, feedbacking variables with massive uncertainty factors, so our default is to make a best guess and take a stab at it. Styles and strategies of guessing distribute across the population and this diversity, like our distribution across the political spectrum, assures that for most processes and contingencies, a bunch of them will be good enough. Sub-optimality is also characteristic of complex systems.
So I was not surprised to find that our interview partners had accounts of how things work that, shall we say, left some things out. At a first pass, they all confidently articulated a crudely simplistic, personalized story of current American politics. Crooked politicians messed things up; immigrants abused our kindness and stole our jobs. Trump will toss the bums out and fix everything. They were strongly focused on individual intention and agency, motivated by personal character, morals, and formal ideas, as their primary explanation for political processes and actions. Systems routinely appeared in their accounts as illegitimately powerful, anonymously personal (“they”), generally malevolent intentional corruptors of wholesome individual action.
Fascinating corollaries included Trump’s personal incorruptibility due to his already having plenty of money of his own, and unquestioning faith in their ability to peer deep into Trump’s soul and detect the authentic care and concern for America there. From a complex systems perspective, their anger at the “rigged system” and eagerness to find a powerful leader to overturn it come into sharp focus as perpetually frustrated and frustrating attempts to enforce legible, predictable linearity on irreducibly non-linear processes. They would have just as much luck understanding how politics work if they believed in witchcraft, fate, or a shadowy global cabal of all-powerful dentists.
I have already said, however, that hurling spitballs at the yawning abyss of complexity is pretty much standard operating procedure. It is hardly a unique failing of these folks, or even a failing at all. Good enough is good enough. And complexity can in fact be managed and engineered down to mere complication or even simple linearity in local settings through rigorous organization and massive effort. Our interview partners all had robust histories in these kinds of engineered systems, and the dispositions to match. They were military and ex-military, nurses, librarians, postal and factory workers. They were mostly religious. They were used to other people having more power than them and making things happen. They were steeped in the everyday strategies of complexity management by orderly hierarchy, leadership, function, and procedure.
But in the parts of the interviews where they were not explaining how they think things work but reflecting on what worried them, a powerful countertext emerged. They perceived only too well the unmanageable complexity of things. It frustrated and terrified them. It kept them up at night and troubled their waking. The uncanny complexity of the world was so far beyond their scope, so realistically out of their reach and uncaring of their wellbeing, so stubbornly resistant to every normal effort and procedure in their experience, so unfair and irrational and amoral, that they lived in anxiety and dread. None of the law, rules, discipline, hard work, the nation, the flag, kittens, puppies, authenticity, guns, and ammo, hold up against the infinite confounds of complexity.
And then Trump said he could fix all that. They knew it was a gamble, and said so. But they were going to hurl him at the abyss and hope.