More thoughts on the Copernicus formula

by Nick Charney

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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.

You may have noticed that I sense "once the formula comes the new frame" and not "if the formula becomes the new frame"; I did so subconsciously, noticed, paused, reflected, and kept it as is because my gut feeling is that it is only a matter of time before the formula's elements become as ubiquitous as the social media that we used to talk about in similar veins.

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.

Building Distributed Capacity

By Kent Aitken

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Last week Nick laid out a model that blends public sentiment, data analytics, design thinking, and behavioural economics as the future of evidence-based policy (see: basically, that was the title). The opportunity cost of inaction, here, is far greater than the immediate financial investments required. The only disagreement I can muster is that I'd actually call it the future of governance, writ large.

But, we're in an era of intense scrutiny. Governments are no longer entirely opaque entities, and spending can be held not just to account but to undue pressure. And that pressure is greatest when spending doesn't lead to immediate and obvious public benefits, which is the case for pursuing the future as described above.

However, there are examples of governments spending money on complex investments - those that are long-term, hard-to-measure, and with widely distributed benefits. It's largely because there are strong communities that envision the long term that are bellowing for these investments, creating crucial pressure and accountability.


And these investments line up with the model Nick proposed. For public sentiment, the U.K. is building capacity through organizations like Sciencewise, dedicated to helping government consult with citizens on science and technology policy. For design thinking, there are a handful of examples, established to help policy makers apply techniques in their work. In the Behavioural Economics field, the U.K. are again the leaders with the Behavioural Insights Unit, and the U.S. appointed Cass Sunstein to a key role to make progress there. For Data Analytics? I welcome examples. But there is good news in the technology space, however, as on Monday a bill was proposed in the U.S. that would codify the national Chief Technology Officer role and establish a Digital Government Office.


These are all wise investments, the success of which can only be measured in the long-term and at the macro scale. None of those investments solve an easily definable problem; rather, they create a distributed capacity, a system for more reliable problem-solving.



So where do we go from here?

At the highest level, it's a question of ensuring that we can make important investments in complex solutions. Where the counterfactual is the key question, and the opportunity cost of inaction far outweighs immediate financial costs. And with closely watching stakeholders than can be hard to convince.


More concretely? There's a group of brilliant and dedicated public servants pursuing capacity-building for design thinking close to home. This is both a discrete capacity and a way to improve virtually every decision-making process, so I think this will go a long way towards better results. Design thinking is properly merciless in testing and discarding sub-optimal solutions.


But data analytics, behavioural economics, and understanding public sentiment require their own skillsets. And I think (and have for some time) that the opportunity cost of not exploring capacity-building in these areas is too great to be ignored.

Blending Public Sentiment, Data Analytics, Design Thinking and Behavioural Economics

by Nick Charney

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The Thinker by Darwin Bell

Last year I wrote a lengthy piece that argued that understanding the future of evidence based policy meant understanding the confluence of big data and social media (See: Big Data, Social Media and the Long Tail of Public Policy). Today I want to further qualify my statements, and refine my conceptual model to reflect some of my more recent thinking.


Project Copernicus

To be fair the conceptual model – which I've decided to nickname Project Copernicus (See: Towards Copernicus if you don't get the reference) – is very much a moving target; and while it ebbs and flows as I come into contact with new (to me) thinking, it's very much about leaning into the hard stuff (See: Lean into it) and "building a better telescope" (See: Complexity is a Measurement Problem).


To recap quickly and push forward

At the outset of the aforementioned piece I offered up a TL;DR summation that was essentially:

Social Media + Big Data Analytics = Future of Public Policy

And feel that refining that statement is as good as a place to start as any; here's my latest thinking:

(Public Sentiment + Data Analytics) / (Design Thinking + Behavioural Economics) = Future of Evidence Based Policy

In a sense its a rather simple, 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.


I have a number of reasons for nuancing the model

• Public Sentiment is broader than social media and it is incumbent on policy makers to be as inclusive as possible when incorporating sentiment. Focusing on social media ignores issues of the digital divide and unduly privileges those with greater digital literacy. This may be one of the reasons that the Deputy Minister's Committee on Social Media and Policy Development was recast as the Deputy Minister's Committee on Policy Innovation; social media may be innovative but it doesn't necessarily follow that innovative ideas flow from social media.
• Data Analytics is broader than Big Data and includes both linked data and open data. These don't necessarily always fall into the category of big data on their own but will play an important role as more and more data sources start to rub up against each other. 
• Design Thinking combines empathy for the context of a problem, creativity in the generation of insights and solutions, and rationality to analyze and fit solutions to the particular context
• Behavioural Economics brings sentiment, analytics, and design to ground by emphasizing what people actually do when faced with a given situation (rather than what we think they ought to do)
• Evidence Based is an important qualifier and cannot be narrowly construed as relating to only one of the variables on the left side of the equation; evidence comes in many forms and it is up to policy makers and elected officials to determine how to weigh the different sources of evidence (variables in the equation above) against each other in a given set of circumstances.

On Savvy Policy Makers

Savvy policy makers (and for that matter, elected officials) are likely the ones able (and willing) to chart their policy directions against this type of model; the one's who can say with confidence:

"Here is what we've heard from the public, here is what the evidence supports, and here is the most policy intervention we have determined to be the most efficacious. However, it is one we will continue to refine over time, as it creates new data, and is forced to stand up to real world public scrutiny"

When was the last time you heard someone qualify a policy position with that kind of preamble?