The Art of Innovation (Part 1)

There’s currently a very interesting exhibition on at the Science Museum in London called “The Art of Innovation”. It’s about the links and dialogue between the (natural) sciences and the arts.

Given my interest in the dialogue between the social sciences and the arts (have a scroll through some of the other posts in this blog if you’re new), the first part of the exhibition was a little wide of the mark. The artistic side of the first few exhibits included architecture, dress-making and textiles.

However the real showstopper for me was a piece designed by Theodore Olivier in 1830 (see below) in the section on “Meaningful Matter”. These were built to convey complex 3-dimensional ideas to a wide audience. The coloured strings could be adjusted using weighted beads in the frame, meaning that different surface shapes could be modelled according to the mathematical equation at question, revealing a number of surfaces and shapes simultaneously. These frames and models were eventually produced in significant numbers and sold across the world. The one in the picture below was made by French company Fabre de Lagrange.

Exploring surfaces and mathematical equations in 3 dimensions

However, as ideas progressed, these models fell out of favour with the mathematical and scientific communities. After the First World War, the models were displayed in museums as curios where they found a new audience; avant grade artists. The string models represented not the abstract thinking of the original mathematical equations, but the ability to explore shape and form in a new way.

Among those who drew inspiration from these models were Barbara Hepworth and Henry Moore. Hepworth modelled her sculpture on the mathematical models that she had seen at Oxford (see below).

Barbara Hepworth Sculpture inspired by mathematical equations
Barbara Hepworth sculpture inspired by mathematical equations

As a member of the St Ives set, it was therefore no surprise to see Hepworth joined on the opposite wall by a set of sketches by Henry Moore.

Sketches by Henry Moore
Henry Moore sketches inspired by equations

Moore’s sketches drew inspiration from the string recreations of maths equations that he encountered on display at the Science Museum when he was a student in the 1920s. Moore immediately recognised the structural and artistic possibilities the models presented, noting that “it wasn’t the scientific study of these models but the ability to look through the strings as with a bird cage and to see one form within another which excited me”. The sketches on display in the exhibition eventually inspired several wooden string figures of his own.

There are similarities with some of the work I’ve been doing with zentangling interaction patterns, as you can see in my previous posts. I’ve also built 3 dimensional string models using the colours of strings to represent economic, legal and “other” aspects of each interaction, overlapping these to build complex patterns. Some of the questions I’ve been pondering are how models like these could help us explore complex patterns of interaction in the context of the social sciences? How could we visualise economic and legal aspects of interactions using colour to build up overarching patterns of interaction?

Stay tuned for more from the exhibition in Part 2….

The Science of Economics? What Works, and How Much…

We do seem to be talking more about economics – what it should do and look like. But there is still a whiff of revolution about calls for the discipline to be more evidence-based and, well, scientific. This article, by Philip Aldrick in the Times yesterday, argues for more careful scientific approaches, and this is worth noting. Of course, in the natural sciences, this would be taken as read. Drugs need to be extensively trialled before they are sold and used to treat disease in humans. But for some reason, in the social sciences, theory and ideology have the ability to shape policy just as much as evidence.

Aldrick’s piece cites two studies launched by Nesta, a UK Innovation think tank, roughly seven years ago. The first was a retrospective review of the effectiveness of business clusters; do small businesses do better when they are closely located and can share location and labour advantages? The second was a randomised controlled trial on whether tax relief for small creative companies worked. The results of the studies were not their most important findings however.

For the sake of finishing a story, the first study proved relatively inconclusive, and could not find any clear correlation between clusters and growth. The second study found that tax and financial incentives were helpful in the short term for small creative businesses, but after 12 months any advantage had faded.

So, what was the main impact? The reason these two studies are remarkable are for their illustration of research methods. While retrospective reviews – generally the majority of most empirical work in the social sciences – can only look for correlation, randomised controlled trials (RCTs) can go deeper, further, and can identify causative factors. In other words, we can target specific factors and identify why things happen. This is important because it means we can be more scientific about what works, how it works, and why. And this means we can begin to base policy on evidence rather than theory. RCTs also offer a way of measuring the extent of policy impacts. By having a test group and a control group, we can gauge the extent to which a policy really makes a difference. And that means we can evaluate whether a policy is financially and economically viable. So, RCTs offer a way of seeing not only what works, but how much.

Why is this news? Similar to other recent posts on here, there is increasing discussion of economics and how the discipline can be improved in the mainstream media. Aldrick’s argument is that economics – both the research and the formulation of policy – can and should be more scientific in its approach. And to this end he calls for more RCTs and longer term studies testing causation before policy is enacted. The government has launched the Business Basics Fund with Nesta to carry out trials investigating, among other things, productivity. UK productivity lags behind that of other countries, attributed generally to poor management practices. But how can management practices be altered to improve productivity?

Questions like this lend themselves readily to RCTs where different techniques can be trialled in comparison with a control group. Nevertheless, there are questions of macroeconomics that are not suitable for trials. We cannot test interest rates or tariffs, for example, against control groups. And this remains a problem for the larger questions tackled in macroeconomics, where theory remains a significant influencer of policy.

Calls for greater use of careful empirical data in shaping economic, legal and social science policy is not new though. Economic sociology, economic sociology of law, and sociolegal approaches have long stressed the need for analysis and understanding to be based firmly in the real world, on real data, and about real people. Increasing access to big data and AI could enhance this. As Aldrick states, “Economics is a social science. Why not make it more scientific?”