Two drivers of not-knowing, inexperience and difficulty are often conflated—information overload and cognitive undercomprehension—and the conflation increases the sense of more complexity in policy and management.
–Think of information overload as the “right” information lost in the glut of information before us. Cognitive undercomprehension, in contrast, is our cognitive limitation to recognize anything like “the right information.”
Overload means we would be high-performing analysts and managers if only we were to tease out the right information from all the noise obscuring it; undercomprehension means we are held to such high-performing standards we couldn’t possibly know the right information, even if it were visible before our very eyes. “I could do my job if only I had the right information” is not “No one could do the job I’m tasked with, whatever the information available.”
Over-complexification comes into play when remedies for one produce the other or complicate both.
–For example, making sense of the masses of Big Data requires algorithms no human beings on their own can comprehend. To that degree, what was information overload ceases to be that by triggering cognitive undercomprehension.
On the other hand, reducing high information overload can be associated with increasing cognitive comprehensibility, but here the costs of doing so may be too high. You reduce the complexity of your conceptual model in order to make it comprehensible, but in the process you’ve increased the chances your model is biased, i.e., that it differs from the correct one. “There are no 99 per cent probabilities in the real world. Very high and very low probabilities are artifices of models, and the probability that any model perfectly describes the world is much less than one,” underscores UK economist, John Kay.
–There is also the problem of society demanding both information overload and cognitive undercomprehension at the same time, however unintentionally. A common enough observation is that when the task is to surveil, as it is for our regulators of record, they will always want more information, no matter how much they already have. In this way, the regulators suffer the double-whammy of information overload and cognitive undercomprehension: They have more information for use but not enough cognitive capacity and skill to extend their limits of cognition on using it.
–Many upshots follow on information overload and cognitive undercomprehension occurring together. Two obvious ones deserve more highlight here.
First, at or beyond the limits of cognition, not only is prediction and forecasting difficult, so too is identifying the counterfactual conditions, not least of which is what would happen if overload and undercomprehension were assuaged. We become very much like amateurs in all this.
Second, arguments presented to us as policy relevant solely because of their diamond-sharp clarity rarely get beyond the joke stage. The usual criticism—policy and politics have become an endless stream of stark media images competing for scarce attention—is true, but even so: The joke is that the stream of photo-clarity means more and more murk, both as information overload and cognitive undercomprehension.
The third implication follows on from the preceding two: Don’t give in to the temptation to simplify, even here as amateurs for a topic this complex.
When an experienced county emergency manager told a group of us, “Floods are complex events, they have many variables,” it wasn’t helpful to tell him, as some did, he’d be much better off first simplifying those events for the purpose of modeling and simulation. To assume he needed to understand the flooding better ignored that he was already managing the complexity there.
A much more effective starting point, it seemed to me then as now, would be to identify professionals who are themselves already managers of complex risks and uncertainties, such as this county emergency manager, and then ask how can we help them, if at all.
Related blog entries on over-complexifying: “Even if what you say is true as far as it goes, it doesn’t go far enough…,” and “Public Policy Analysis, c.1970 – c.2020: In Memoriam?”