Solution focused social science

A recent article by Duncan Watts in Nature prompted me to structure some thoughts that I had about the challenges in creating and predicting changes in society.

Watts, a physicist by initial training but now a sociologist, asks whether social science should be more solution oriented. As I found recently when attempting to build a simulation model of the diffusion of low energy/carbon innovations, there are many different social science theories that could be applied to how people influence each other and make decisions about whether to adopt a new innovation or not. My intention had been to build on existing approaches but the two primary models used in the simulation literature for representing how influence occurs both failed the face value test of sufficiently describing how influence occurs by missing out key features*. Watts goes further and argues that the two primary conceptual models discussed in the social science literature for understanding innovation diffusion make incompatible assumptions and so cannot both be right. And yet, both continue to be used in research studies.

During the recent Brexit debate in the UK, a leading politician declared that we have had enough of experts telling us what to think because they are usually wrong. He was particularly referring to economists at this point but the bald criticism of all experts was somewhat concerning. We do though know through Tetlock’s work that those ‘experts’ that appear on TV are only marginally better (if at all) than a random prediction. But again, these are mainly economics and politics experts rather than, say, physics or medical experts. The challenge as Watts describes it is that in social science (including economics and psychology), there are many different theories for how any particular aspect of social or economic behaviour occurs and experts usually have their own preference. By contrast, in the physical sciences there is usually broad agreement about the majority of issues particularly those that have been studies for sometime.

Being a mathematician by training I found (and continue to find) it strange that, despite the difficulty, social scientists think that it is OK that they don’t have a clear view on many social issues – and don’t appear to strive to obtain one. There are challenges of course; unlike physical sciences, it is difficult to carry out controlled trials and hard to evaluate the effect of specific social or economic interventions since other factors cannot be held constant and the timescale for social change can be long. Human beings are not as predictable as atoms and the economy and society are complex systems in which effects may be non-linear and emergent but in the real world it is not helpful to have a wide range of competing theories to choose from. Tony Greenham at the RSA argues that economics is not like dentistry (see John Maynard Keynes) and it is impossible to have just a single view of how the economy works rather that it is useful to allow a dialogue between the different approaches to occur. Policy makers may with good reason feel confused about the right approach to take to achieve social change because they have to take action. Ultimately, dialogue has to converge to a decision as to which action to take.

Consequently, Watts argues that some of the social science effort which currently goes into trying to further build theories of how specific aspects of how a social system works would be more usefully focused on solving specific real world issues. I recall sitting in a meeting at a research institute that was trying to decide its research priorities and doodling a 2×2 matrix (I am a management consultant by trade after all!). The two axes were whether the project took the academic field further and whether the work would be immediately applicable. I only discovered sometime later that Donald Stokes got there some 20 years before me in his book Pasteur’s Quadrant. Pasteur’s quadrant is the cell in the 2×2 matrix which both contributes to the academic field AND is also practically useful. This is where Watts’ article suggests more social science effort should be focused though it also discusses the challenges in finding and executing work that fits this brief showing that the shift is not trivial.

In the absence of greater consensus on key issues in the social sciences, I think that Watts’ suggestion of shifting more effort into focusing on specific solutions is a good way forward. And surely better than just refining or adding to the multitude of partial theories which already exist.

Watts makes the arguments much more clearly than I can here and so I encourage you to read his paper.

*Some modellers might argue here that the models have been fitted to real world data and so they have been validated. However, given the number of parameters in each model it is possible to fit both to historic data and so both would appear to be good models. This does not help though when you then want to use the model to assess interventions into the system since you do not really know the underlying mechanism and hence the effect of different interventions.

Mathematician as Organisational Effectiveness consultant

As I resume working as an independent consultant helping organisations become more effective, I was reflecting on my background and training as a mathematician and how this helps me and my clients.

When people think about maths they almost always think about numbers. And yes, it is really useful to be able to manipulate and analyse data well. Indeed, in modern organisations it is increasingly an essential skill not just to be able to look at data but to do your own analysis on it, to search for your own solutions. As an OE consultant it is critical to be able to gather and analyse data (sometimes in quite sophisticated ways) to ensure that you are making the right diagnosis of the business issues. Having a natural affinity for numbers and the tools to analyse them is a real bonus. What we do when we analyse data is look for patterns that help us understand what is happening in the real world. At its heart much of what mathematicians all over the world do is pattern finding, searching through data to find the nuggets of insight.

Maths is also about the ability to build models to explain and predict. As a mathematician I am always looking to understand which factors really matter (whether it be in numbers or not), how they relate to each other and what happens as a result. My brain seems to automatically build these interlinked representations (or models) of what is happening in any situation very rapidly. In complex modern organisations, this is essential as we need to understand how changing one thing might affect others, and also make sure that all the changes we are making are aligned with each other and not pulling in different directions (a more common occurrence than one might hope).

The ideas of complexity science such as adaptation, emergence, and agent based modelling have a lot to offer here. Complexity science was initially developed at the Santa Fe Institute where a group or researchers came together in recognition that academia had become too siloed and that the real problems in the world needed a cross-disciplinary approach to solving them. We still see this silo effect when organisations are trying to improve their situation – everyone (HR, IT, marketing etc etc) has their own solution that they know think will make the difference but very few people are able to really consider the whole system (both within and outside the organisation) and how the parts of it affect each other. This is how mathematicians think naturally.

Maths is also a language. It is used by all the other sciences (physical and social) to define their problems in a rigorous way. Mathematicians have to have the ability to understand different situations and people, and to translate what they are saying into a clear definition of the problem and then find the appropriate tools to solve it. They also then need to be able to translate their solutions back into the language of the people who had the initial problem. This is so critical in business situations – people from different functions talk their own language and whilst they are in the same room talking to each other they often really have no idea what everyone else is really saying. As mathematicians we have to help with this translation and be precise about what is being said otherwise the wrong problem will be solved.

Finally, using maths also requires you to consider not just the numbers but also how people process data, how they think about it and how they make decisions as result. This leads you to consider the process of decision making in organisations and how to help people make better decisions. The rapidly expanding cognitive and decision science fields have much to offer the OE practitioner here.

I reflect then that the four main abilities that being a mathematician allows me to bring to my role as an OE consultant are being naturally comfortable and capable with numbers and statistics, the ability to create mental maps or models of situations which reflect the interrelated nature of different factors or levers, the skill of a linguist to translate between people speaking different (business) languages, and the ability to step back and think about how other people are thinking (and feeling).

Of course, to use a mathematical term, these are necessary but not sufficient skills for being a good OE consultant (years of experience helps as does my NLP training and coaching skills). However, I believe that they are necessary and am pleased to have them.

>What the financial world can learn from a nuclear reactor

>An interesting article in the Financial Times by Tim Harford drawing comparisons between the complex, tightly coupled systems of the operation of a nuclear power plant and that of the world banking systems (and by illustration domino toppling competitions). He concludes that it is not possible to make such systems safe by trying to control the behaviour of individual elements but that the solution lies in isolating the impact that the failure of one element can have on the rest of the system.

>The power of convergence

>A new model for scientific research known as “convergence” offers the potential for revolutionary advances in biomedicine and other areas of science, according to a white paper issued today by 12 leading MIT researchers. The white paper says that the United States should capitalize on the trend of convergence — which involves the merger of life, physical and engineering sciences — to foster the innovation necessary to meet the growing demand for accessible, affordable health care. 

Over the weekend I have been reading Mitchell Waldrop’s excellent book Complexity: the emerging science at the edge of order and chaos which tells the story of the establishment of the Santa Fe Institute in the early to mid 1980s. It is the story of how a few brilliant minds recognised that the future of science was in the study of complexity and that it required a multi-disciplinary approach from physics to biology to maths to computer science to economics (and more). One of the big constraints these great minds saw was how funding and academic progress was only available to people who stayed within the confines of existing disciplines. The second constraint was how much science was reductionist – drilling down in greater and greater detail and specialism rather than understanding how different fields could work together. Sound familiar? Judging by the convergence article many of these barriers still exist and people are still fighting to break through them.

>The Five Habits of Highly Effective Hives

>For millions of years, the scouts on honey bee swarms have faced the task of selecting proper homes. Evolution by natural selection has structured these insect search committees so that they make the best possible decisions. What works well for bee swarms can also work well for human groups. In Harvard Business Review, Thomas Seeley helps us learn from the bees five guidelines for achieving a high collective IQ.