First, since this is surely the last issue of this newsletter in 2016, we send the warmest wishes from everyone in the Changeist point cloud to you and yours. After such a turbulent year, we hope you’ll join us to take a moment to shut down, reflect, and regain some energy to get a running start into 2017. There’s work to do. We hope, like many of you, we’ll get a chance to make plans to face the epic challenges ahead—not just to provide a viewpoint, but to build: capacity, knowledge, and not a little resilience when confronted with deep uncertainty.
I had an opportunity to speak to a private event in Canary Wharf this month, which is interesting for the ethnographic value alone (stateless vacuums of capitalism etc, etc). What do you talk about to a room full of financial folks? Uncertainty, and how to live with it instead of trying to (pretending to) eliminate it.
I didn’t have to dig far to find useful examples. After a brief trigger warning, I took my captive audience through three recent experiences with certainty that melted into uncertainty quickly—three white swans turned black: the 2015 UK General Election, the Brexit referendum, and the US presidential election (has it really concluded yet?). In each case, data models strongly suggested the result would be A, and the result was B. These cases were related (and may still drive other, less uncertain, but no less nasty) in part by the cascading discontinuities triggered by the first (and Cameron’s pledge of a referendum), which continued to cascade through the second to the third and beyond.
Add to this the blindness brought on by Big Data, the related proliferation of competing forecasting models, and the inability to adapt many of these models to a world where the assumptions underpinning them no longer operate (how do you poll when respondents not only don’t want to respond, but may in fact willfully respond to distort outcomes?), and you have a whole pond of black swans.
Two days after the US election, the Chief Economist of the Bank of England, Andy Haldane, delivered a typically Haldanian
talk for the GLS Shackle Biennial Memorial Lecture called “The Dappled World,”
in which he argued for a deep reassessment of models used in finance in the face of a world that isn’t
full of rational economic actors, that doesn’t conform to the rigid assumptions embedded in these spreadsheets and formulae. He pointed out that economics is one of the least open fields to transdisciplinary influence—significantly less so than, say political science or sociology.
As the characters in one of my favorite movies about the Global Financial Collapse of 2008, Margin Call, discover when playing with a fired colleague’s Excel models
, reality has escaped the confines of these black boxes, with dangerous results. “At root, these were failures of models, methodologies and mono-cultures,” wrote Haldane. “It has been argued that these models were not designed to explain such extreme events.” The killer quote comes from another economist cited in his talk: “The charge is that the [..] forecasting model failed to predict the events of September 2008. Yet the simulations were not presented as assurance that no crisis would occur, but as a forecast of what could be expected conditional on a crisis not occurring”. Yep.
The title of Haldane’s talk comes from a book of the same name by Nancy Cartwright, a professor in philosophy of science. In it, Cartwright makes a similar point about the fallibility of models. "To affect the world around us we need to make reliable predictions about it and that requires regularities. What can be relied on to happen in a given situation is what regularly happens in it, or what would regularly happen it in if enough repetitions were to occur.”
This has direct implications not only for economic models, but for how we think about—and model—possible futures. The tools aren’t magical or infallible, nor are traditional methodologies. For example, a scenario isn’t a special box that reveals futures—it’s a platform for surfacing and exploring tensions of the present as much as the future. The mechanistic process for deriving a scenario isn’t predictive any more than a coin flip is itself predictive. But both provoke consideration about uncertainty, tensions, and possibility.
Uncertainty can be dangerous when it drives fearful responses, but it can also be a tool for exploration—for probing deeper to find the root, for considering, testing, and responding to "what ifs”. Uncertainty should be a reason to go looking for new tools, points of view, and unrealized insights.
I concluded my talk by suggesting we need to question a lot of what we do, whether we are high-powered financiers or not. We should question our favorite models and worldviews, as Haldane suggests, but also our mythologies and metaphors—the forces we believe control our direction.
Most importantly, it’s important to adopt and become comfortable in what, in his essay on futures and the restoration of hope, “Facing the Fold
,” Jay Ogilvy called the scenaric stance
. We don’t have to cling blindly to optimistic (fantastic) or overly pessimistic visions exclusively, but must build and exercise the ability to maintain both in our heads at the same time, and still operate.
This not only a useful professional stance, but one that very well may be critical to survival in the days ahead. Ogilvy writes “…when one faces the fold, one is relieved of the intellectual dishonesty involved in holding either branch of the fold as a single-point forecast. One is relieved of the naiveté of callow optimism, even as one is spared the amoral defeatism of the all-knowing cynic. You’ve looked at the dark side; you’ve seen the very real risk; and still you’re able to move ahead constructively.”
And move ahead constructively we must.
Thanks for your support and interest this year. If we didn’t meet in 2016, we hope to see you somewhere soon.