|by Nick Charney|
A few weeks ago I wrote about the problem of asymmetric scrutiny as it relates to the change agenda and public sector innovation culture more broadly speaking (See: Asymmetric Warfare: Agents of Changes vs Agents of the Status Quo). In short I think the problem of asymmetric scrutiny significantly impacts our organizations and the innovation agenda writ large. What I didn't explore last week was how the cultural practice applies to the development of public policy options and public opinion. However, before we can make that connection more explicit, there's an important bridging concept that's worth introducing: superforecasting.
Superforecasting: The Art and Science of Prediction is a book written by Philip E. Tetlock and Dan Gardner (who was later hired as an advisor to the current Prime Minister) released in 2015; the book details findings from The Good Judgment Project and generally explains the art and science of prediction. Its worth noting that the book itself was rumoured to be making the rounds politically in Ottawa (though this was largely overshadowed by talk of deliverology) and the concept was the subject of one of presentations at the last Policy Ignite.
I won't walk you through the whole thesis but essentially the books makes the case that people are generally pretty bad at making predictions about the future (i.e. most people are bad forecastors). Superforecastors are different in that they represent a very small subset of people who can assign a numeric probability (i.e. make odds) to the likelihood of particularly complex global event occurring (e.g. Brexit) with a high degree of accuracy. If you are looking for a good introduction to the topic I would suggest listening to an episode of Freakonomics entitled: "How to be less terrible at predicting the future" as it provides a great introduction to the concept. The podcast included an interview with Superforecasting co-author Phillip Tetlock where he summarized some of the most important characteristics of superforecastors. While all of these characteristics may be important for superforecasting some of them are more important than others when it comes to improving how we understand the problem of asymmetric scrutiny; more specifically:
- Starting with an outside view rather an inside view
- Willingness to change your view in the face of new information
My basic premise being that asymmetric scrutiny is prevalent precise because we are generally terrible at these two things; and since we can't accurately predict the future we measure its worth, or hold it to account, with the yardstick of the past. In other words, the two phenomena go hand in hand. Let's explore each of these characteristics in turn.
Starting with an outside view rather an inside view
First, 'starting with an outside rather than an inside view' means looking at the broader trends (rather than the specifics of the particular situation) and using the broader context as an anchor for prediction (rather than the specifics of the immediate and narrow circumstances). This isn't generally something that we do from either a change or public policy perspective. In my experience the downward pressure within the bureaucracy typically comes to bare on the specifics of a given change initiative rather than the broader context from within which it is being advanced. It doesn't meet the specifics of guidelines X, or it fails to align with corporate initiative Y, or Z dollars is too costly in today's figures. The pressures are seldom about how a particular initiative is out of sync with the generalities of zeitgeist, flies in the face of the workplace culture we are espousing, or might not generate the anticipated value over the lifespan of the project. Take Blueprint 2020 as a concrete example, many people have taken issues with its specifics but few can argue that it was not a step in the right general direction.
The same thinking applies to the formation of public policy. Look at all the concern about the implications of self-driving cars -- epitomized by the discussions about what algorithms should decide to do in a 'who to kill' situation where loss of life is inevitable. Public discourse on this issue tends to over emphasize the issue of deaths due to autonomous vehicles in absolute terms (i.e. taking an inside view) rather than as a percentage of the overall mortality rate for traffic accidents (there were 1.25 million road traffic deaths globally in 2013). While concerns about autonomous vehicles causing accidents is real, perhaps it ought not to factor so heavily into how we understand the issue. Taking an outside view rather than an inside view on this issue might alter the balance of the discussion and reshape the public discourse. The inside view generates asymmetric scrutiny on autonomous vehicles, shapes public opinion and thus limits the government's ability to make 'progressive' (outside view driven) policy.
Willingness to change your view in the face of new information
Second, the 'willingness to change your view in the face of new information' is conceptually very straightforward but occurs rarely in practice in large permission-based cultures. These cultures tend to make sense of new information by contextualizing it within the current frame or rule set, they do not easily re-frame or change the rule set in the face of new information. This is precisely why the Treasury Board Policy Suite is something that needed to be reset -- it finally became apparent that so much of it was stale -- rather than something that adapted and changed over time as the context changed. The problem here is well known and again directly connected to the notion of asymmetric scrutiny. How many people are responsible for ensuring compliance with the rule set across the organization, all of whom are unable to change the rules in the face of new information and forced to apply them as they are written. This dogmatism is the very definition of the problem asymmetric scrutiny and illustrates why the problem is systemic and slows innovative forces within our institutions.
Again the same thinking applies to the formation of policy options. Being slow to move from wherever public discourse is currently anchored slows down our policy response and often means we end up behind the eight-ball, bringing policy solutions that are largely about mitigation rather than prevention. This can especially be the case when highly specialized knowledge (technical or scientific) is slow to move into the mainstream. The impact of tobacco on public health in the 1990s was a good example of this as is (perhaps) the impact of sugar today. All in all this makes 'progressive' policy incredibly difficult to pursue.
How do we do culture better?
First, we can change hiring practices so that it better privileges candidates who approach problems from an outside rather than an insider view; candidates who are willing to change their view in light of new information.
Second, we can set mandatory review dates for all of the guidelines and directives in the Treasury Board Policy Suite to ensure that the policy framework is evergreen and invest the necessary resources to prune it and keep it healthy so that it never needs to be 'reset' again.
How do we do policy better?
First, we can introduce more specific and purposeful policy making techniques (e.g. foresight) that privilege the outside view by design.
Second, we can popularize highly technical and/or scientific knowledge by using plain language to introduce complex ideas and raise issues with the general public (e.g. public education) to move public opinion.