Since CP Snow’s controversial lecture in 1959 and stretching back into the eighteenth century, we have been engaged in a debate that questions the relative importance that our society places on science and that which it places on arts and creativity.
Asked to explain the image that comes into our head when we hear the word ‘scientist’ and when we hear the word ‘artist’ or ‘designer’ perhaps does enough to explain just how far apart these disciplines can sit within our culture and consciousness. However, we are seeing more and more interest in the bringing together of arts and science - whether that be through the growing interest in STEAM, data artists or the growing movement of people like Heather Dewey Hagborg and many others, who are innovating at the intersection between art and science. As never before, we have a passion for dancing at the edge of - and into - other disciplines.
At Nesta’s recent LabWorks event, we began to explore how this inter-disciplinary dance is playing out within the theory and practice of Innovation Labs. Are we actively bringing together scientific method, art and creativity to support innovation? Or are we still in an age-old, Art vs Science standoff?
Because on the one hand, we see the rise in use of science-based approaches to public innovation; that is - systematic experimentation such as randomised control trials – which are seeking to understand how the world works, and rigorously testing hypotheses in controlled environments. The spectrum of that work is wide, but it shares some common characteristics of being systematic, logical with a quest for empirical evidence.
And on the other hand we have design-based methods; focused on creativity, origination, co-production, applying design principles and techniques such as creative ideation processes, and visualisation and modelling of prototypes; and asking not necessarily how the world works, but what is needed to solve a problem. What does not yet exist that should exist?
As Innovation Labs across the world multiply in number so too does the adoption of these different methods - and so it becomes quite important to apply a critical gaze. When under the microscope viewed together, these design and science-led methods raise a number of intriguing questions, such as: where do good ideas actually come from? What counts as evidence, and what doesn’t? How do we judge the relative successes of these distinct approaches to achieve transformative over just incremental change?
Are we looking at a collective suite of tools and methods from which we may pick depending on context and objective - or are we looking at fundamentally opposing philosophies about the world, and therefore about how to support innovation? Regardless of approach, are we still left with the same challenges of diffusion and scale, or does one method set the conditions more favourably for scale than another?
And if working in this - indeed in any field - shows us that there is nothing as permanent as change, are the methods that are popular today, likely to be so popular tomorrow. And if not, what stands on their shoulders, and to what extent can we predict and shape those methods ourselves through experimentation and dialogue?
Having David Halpern and Christian Bason take to the stage at LabWorks to champion and critique their own approaches was a treat, and you can view the full video here. Perhaps predictably, while the debate threw into sharp relief the differences between the two approaches, it quickly showed not just how in need of each other they are, but how exciting that union can be.
Good ideas need to come from somewhere, and creating space for creativity, creative expression, sparks of inspiration, and insight to arise that generate new ideas is, I hope, an obvious point to make in favour of more design-led, creative innovation processes. The challenge of course, and one that Bason and others acknowledge, is that the creative urge does not always mean that designers and artists take time to explore existing ideas and evidence relating to the field in which they are exploring.
In other words, fresh, unfettered eyes can also sometimes be naïve eyes. A challenge that, technically at least, is easy to overcome, if there is sufficient will to learn from previous initiatives and interventions. Whether, on that journey of exploration, there is also enough will to replicate something that already exists and working successfully in another context, is a question I’ll silently answer - but consider one eyebrow raised.
But it would be a mistake to focus solely on design process. We know, not least from the Wellcome Trust’s work, that art affords us an opportunity to visualise possibilities and to start new kinds of conversations about things that matter. We know that art may help us heal, that it can amplify social change, that it can provoke, disturb and disrupt our thinking in countless different ways. In other words, this isn’t just about design methods, but giving legitimacy and value to the fact that art itself that can be a powerful catalyst for innovation. This lingering thought has certainly prompted me to think about experimenting more in this area within Nesta’s own Lab.
In the early stages of an innovation process, creativity, origination and participation are all pretty vital activities which create new perspectives, new thinking and new ideas. However, the more challenging point raised by Halpern in our LabWorks debate is the problem of ‘optimism bias’; namely, that we can become very strongly attached to ideas (particularly our own, which are always excellent).
But we know good ideas can easily masquerade as bad ones, or at the very least come laden with untested assumptions about their likely success. Passing the baton (or Bason, if you will) at this point from a design-led process that has created a working prototype, to a more controlled trial (or series of trials) that can test and produce evidence, seems an obvious point of integration for the two methods. But it’s a nuanced game.
A reflection on my work within Nesta’s own Innovation Lab, is that contrary evidence doesn’t always quell ‘optimism bias’; and that it’s not always straightforward whether that’s a good or a bad thing when it comes to the pursuit of radical, highly challenging innovation. There are a range of things to consider, such as: timing (ok, it’s not working right now but it needs more time), context (ok, it’s not working right now but we can see the context is changing), and iteration (ok, it’s not working but if we make these changes it might). These are all judgement calls to be made. And you may well find yourself a year down the line realising you should have listened to what the initial results of your trial were telling you and stopped work eleven months ago. Or you may not. Life, and evidence, can change.
A crude assessment of scientific interventions over creative, design and user led innovation methods could easily come to the conclusion that science methods are about experimenting on people, and design-led methods are focused on innovating and inquiring with people. And I’m sure we could easily find people who conduct RCT’s and people who practice participatory research methods that would both fiercely defend their own methods, and probably engage in quite a lengthy, epistemological debate about the nature of objectivity.
We’ll skim over that and reflect on the response from Halpern, who strongly argues the need for empirical evidence; highlighting the necessity to understand revealed, rather than stated preferences when considering what to pay most attention to in an innovation process. Essentially, that people can’t always be trusted to accurately report their own behaviours or intentions.
This point that we very often do not enact what we espouse feels true to me; as it will to anyone with an ounce of self-awareness. But so too does Bason’s thoughts on the necessity - particularly for leaders - to engage in processes that allow for subjective, emotional engagement in a particular issue; the kind of personal inquiry often inherent within design or user-led approaches.
Many Labs are supporting innovations that they want to see implemented at a scale that will have an overwhelmingly positive impact on a particular challenge or problem. Therefore, as we heard in the debate, Labs almost always need to influence decision makers in order to achieve that scale. And I was left with the reinforced view that this won’t be achieved through the generation of evidence alone any more than it will be through creating opportunities for leaders and other actors to gain insights that stir their hearts and minds alone. But of course, both and more besides.
This points towards the critical need for Labs to be using more imaginative methods and processes that enable us to combine and simultaneously value very different things – a skill much in demand in this increasingly complex and chaotic world.
The image above is reproduced with kind permission of Nathalie Miebach who takes weather data from massive storms and turns it into complex sculptures that embody the forces of nature and time. You can see more of her extraordinary work here.