 | by Nick Charney |        |
A while back I presented a model demonstrating what I consider to be the future of public policy (See: Blending Sentiment, Data Analytics, Design Thinking, and Behavioural Economics). Kent later observed that the model could in fact describe the more encompassing idea of governance writ large (See: Building Distributed Capacity). At first I agreed with his observation but it's something I've been quietly reflecting on a lot lately and the more I think about it, the more I get the sense that what I've put forward is more precisely a formula that informs governance. Or perhaps more rightly, could inform a particular way of "doing" governance, because governance is – as Kent himself recently noted (See: People Act, Technology Helps) – what people do.
Recapping Copernicus
If you didn't catch the original post (again, see: Blending Sentiment, Data Analytics, Design Thinking, and Behavioural Economics) here's the TL;DR recap of the formula:
(Public Sentiment + Data Analytics) / (Design Thinking + Behavioural Economics) = Future of Evidence Based Policy
It's a back-to-basics model that argues that the sum of what the public wants (sentiment) and what the evidence suggests is possible (data) is best achieved through policy interventions that are highly contextualized and can be empirically tested, tweaked, and maximized (design thinking + behavioural economics) while simultaneously creating new data to support or refute it and facing real-time and constantly shifting public scrutiny.
Naming Copernicus
I chose to name the formula Copernicus for the following reasons:
- it speaks to the fact that the formula represents a significant reorientation in the field of policy development and execution;
- it infers the amount of effort that will be required to overcome the inertia that is inherent in current frame of reference; and
- it conveys the sense that once the formula becomes the new frame of reference the old frame is no longer tenable.
Copernicus is a means
It's a frame that helps you lean into the hard work of figuring out the variables. What do people want? What does the evidence suggest is possible?
It's a frame that helps you lean even further into the harder work of structuring the execution. What policy levers are most likely to work? How do you design the interaction? How do you build adaptability into the prototype?
It's a frame that helps decision makers gather rich information points and brings them to a series of decision points.
Copernicus is not an end
What I'm trying to get at is the fact that the formula isn't a panacea of simplification but a lens through which to better understand complexity. It doesn't tell you how to weigh the variables against one another, or what choice(s) to make, but rather it helps identify that which you ought to consider when doing so.
To be honest, I was planning on writing a series of posts elaborating each of the formula's elements but every time I sit down to do so I get lost in the complexity of each of them. In short, I'm still learning, thinking them through, running them up against real world examples. I still plan on doing so, but I need to dedicate more time to think it all through.
To this end, I'm considering convening a small discussion to test the model against recent policy choices made by different organizations (e.g. Canada Post' decision to end home delivery) to see precisely how it could help me both understand and explain a policy choice if I was in the position to make one. If this is a thought exercise that you are interested in participating in, drop me a line, I'd be happy to run through it with you as a thought exercise.
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