This week we hosted the first Innovation Growth Lab conference, with over 230 participants from 27 countries. Here’s my opening talk.

‘With this event we hope that new ground is being broken - in understanding the sources of growth and prosperity, and understanding what governments and others can do to support them. A community is taking shape which aims to make policy more empirical, more experimental and perhaps truer to the spirit of the innovation and entrepreneurship it aims to help. At its heart is a very simple idea: that we should ask more often, and more insistently of anyone responsible for any new or existing programme: ‘how can we know that it really works?’

Three very different individuals - George Gilder, the Buddha and Michael Faraday - showed just how important that question can be. George Gilder was one of the most influential writers on entrepreneurship in Silicon Valley back in the 80s. He wrote that successful entrepreneurs needed ‘to spend a lot of time in the grit and grease and garbage of their business…’ and that they had to be ‘meek enough and shrewd enough to endure the humbling of self that comes in the process of profound learning from others.’ Anyone involved in backing entrepreneurs will recognise the point. It’s wonderful to have passion, but if entrepreneurs become too fixated on their idea or their product then they are highly likely to fail, while those willing to adapt, learn, and be surprised are much more likely to succeed.

Two thousand years before Gilder, the Buddha used a famous sermon, the Kalama Sutta, to set out ideas equally fundamental to what we are talking about today: ‘Do not go upon what has been acquired by repeated hearing’ he advised his listeners, ‘nor upon tradition; rumour; what is in a scripture; surmise; an axiom; or specious reasoning; or a bias towards a notion that has been pondered over; or another’s seeming ability; or on the consideration, “The monk is our teacher.”’ He was warning his listeners, in other words, to be sceptical, and to recognise that the road to insight is paved with doubt.

This idea – that knowledge depends on doubt and disconfirmation – is also of course the foundation of modern world and of modern science. The great scientist Michael Faraday put it very well: ‘ We ever make the wish the father to the thought: we receive as friendly that which agrees with, we resist with dislike that which opposes us; whereas the very reverse is required by every dictate of common sense.'

All three in their different ways emphasised just how much openness, humility, and the willingness to be proven wrong, is essential to wisdom and understanding.

We need to seek out disconfirmation, rather than just confirmation, to arrive at truths.

That takes us to economics and economic policy. This idea of progress coming from disconfirmation should be obvious in economics, as a mature social science. Indeed it should be particularly obvious because a similar idea lies at the core of a market economy which is in many respects a discovery process that tries out multiple ideas and methods and rejects the great majority.

Yet these simple insights are surprisingly unusual in economic and industrial policy. A big survey we commissioned on the state of knowledge in innovation policy – the Manchester Compendium – summarised what was known in nearly 20 major reports, but also showed how little policy had been designed in ways that would generate useful knowledge. There were plausible accounts of what might work but surprisingly little definitive knowledge, because the methods of implementation had not built in serious learning, let alone formal use of control groups.

A similar pattern can be found in much writing about economics and industrial policy – which have plenty of marvellous argument, logic and historical analogy. But they very rarely attempt to answer the question which I think is crucial for us today: how can we know if it really works? Or: how would we know if you were wrong?

That was one of the prompts for setting up the Innovation Growth Lab. To generate more useful knowledge, founded on rigorous experimental design, and to create a space for testing promising ideas, that would be open to surprise, and offer the prospect of saving a vast amount of otherwise wasted time and money, including some of the tens of billions spent by governments trying to prompt economies to be more innovative.

Our hope is that we are going with the grain and that this will be one of the big trends happening in public policy.

We’re all too aware that there are contradictory trends: we can see plenty of ‘post truth politics’ around, and leaders who are oblivious to facts, evidence or consistency. But there are also grounds for optimism with ever wider recognition of the value of rigour, data, facts and experimentation.

Over the last five years we’ve tried to create a new institutional ecology here in the UK – we host the Alliance for Useful Evidence, backed by the ESRC and Big Lottery Fund, targeting politicians, civil servants practitioners, and linking over 2,000 individuals and organisations to better understand evidence in policy and frontline practice. We advocated, and then saw established, a network of What Works centres – 10 plus now here, and parallels like What Works Cities in the US.

The centres all exist to make the available facts more visible, so as to reduce the space for deception, delusion and ill-conceived actions. They all promote the principle that anyone in power – like ministers - has every right to ignore the evidence (since it may well be wrong) but that they have no right to be ignorant of it. They are all focused as much on the use of evidence and demand, as well as supply. And all try to promote a more sophisticated understanding of research tools, what randomised control trials (RCTs) can and can’t do, or the role of natural experiments, surveys, data, or the subtle skills of doing experiments in government – handling the politics, the media, and the ethics.

At their best, experiments are surprising. They tell us non-obvious truths. A good example is the myriad of programmes that try to influence children either by attracting them – for example, to science or entrepreneurship – or by repelling them – for example, from crime and drugs.

Remarkably, once studied with rigorous methods, many of these turn out to have precisely the opposite effect to what they intended, often discouraging children from things like science, and attracting them to the very criminality and drugs they’re being discouraged from. But it’s only through rigorous experiment that we find out.

This sort of approach marks a big shift away from the deductive view of policy, intuition, to a more iterative, practice based, empirical one. It’s an approach well suited to the fields of innovation and entrepreneurship which in the past were dominated only by anecdote and inspirational stories, but are increasingly served by large data sets, substantially thanks to years of fantastic work by Kauffman and others.

Sometimes the evidence confirms our hunches. But often it proves them wrong – whether in relation to patents boxes, entrepreneurship programmes in universities, or tax breaks in enterprise zones. As Philip Tetlock showed in his classic work on predictions, the most expert people can be the least successful at predicting the future, mainly because they become too confident in their own ability and so seek out confirming information. The best predictors – and perhaps analysts more generally - are good at listening to new information, careful not to be guided too much by grand theories.

Our job here today is to discuss how we make these simple insights part of the mainstream. So that the default is to test ideas, not just implement them. To seek disconfirmation as well as confirmation. To learn and not just repeat ideas because they are plausible, or fashionable or advocated by someone with a Nobel Prize.

And our hope is that we can create a community of people involved in doing this in practice, learning from each other’s successes and failures faster, smarter and with more impact.’