Brian Arthur introduced a new and important idea in his book The nature of technology: that of ‘combinatorial evolution’. The argument, put perhaps overly briefly, is essentially this: a ‘technology’, an aeroplane say, can be thought of as a system, and then we see that it is made up of a number of subsystems; and these can be arranged in a hierarchy. Thus the plane has engines, engines have turbo blades etc. The control system must sit at a high level in the hierarchy and then at lower levels we will find computers. The key idea is that most innovation comes at lower levels in the hierarchy, and through combinations of these innovations – hence combinatorial evolution. The computer may have been invented to do calculations but, as with aeroplanes, now figures as the lynchpin of sophisticated control systems.
This provides a basis for exploring research priorities. Arthur is in the main concerned with hard technologies and with specifics, like aeroplanes. However, he does remark that the economy ‘is an expression of technologies’ and that technological change implies structural change. Then: ‘…economic theory does not usually enter [here]………. it is inhabited by historians’. We can learn something here about dynamics, about economics and about interdisciplinarity! Continue reading
Systems thinking (see earlier entry) drives us to interdisciplinarity: we need to know everything about a system of interest and that means anything and everything that any relevant discipline can contribute. For almost any social science system of interest, there will be available knowledge at least from economics, geography, history, sociology, politics plus enabling disciplines such as mathematics, statistics, computer science and philosophy; and many more. Even this statement points up the paucity of disciplinary approaches. We should recognise that the professional disciplines such as medicine already have a systems focus and so in one obvious sense are interdisciplinary. But in the medicine case, the demand for in-depth knowledge has generated a host of specialisms which again produce silos and a different kind of interdisciplinary challenge. Continue reading
There are starting points that we can take from ‘systems thinking’ and theory development (‘evolvere theoria et intellectum’). Add ‘methods’ (including data – ‘Nullius in verba’) to this and this becomes the STM first step.
- S: define the system of interest, dealing with the various dimensions of scale etc
- T: what kinds of theory, or understanding, can be brought to bear?
- M: what kinds of methods are available to operationalise the theory, to build a model?
This is essentially analytical: how does the system of interest work? How has it evolved? What is its future? This approach will almost certainly force an interdisciplinary perspective and within that, force some choices. For example, statistics or mathematics? Econometrics or mathematical economics? We should also flag a connection to Brian Arthur’s (see ‘combinatorial evolution’ entry) ideas on the evolution of technology – applied to research. He would argue that our system of interest in practice can be broken down into a hierarchy of subsystems, and that innovation is likely to come from lower levels in the hierarchy. This was, in his case, technological innovation but it seems to me that this is applicable to research as well. Continue reading
We need data; but we need to encapsulate our understanding of cities in theories; and then we need to represent these theories in models. From ‘Nullius in verba’ to ‘Evolvere theoria et intellectum’ as a subsidiary motto: develop theory and understanding.
Can we ever have theory in the way that physicists have theory? Of course not, in that we have too many uncertainties at the micro-scale – the behaviours of individuals and organisations and this creates uncertainties at broader scales. We can’t calculate ‘constants’ (parameters) to umpteen decimal places and our data points do not fit precisely onto smooth lines or curves. But if we ask the right questions, we can develop theories in relation to those questions and then, from a quantitative perspective – and there are others! – we might say that a typical error level (or measure of uncertainty – is around 10% say. This is much better than not having the theory, particularly if it enables us to predict, at least for the short run. Continue reading
‘Nullius in verba’ is the motto of the Royal Society. It can be roughly translated as ‘Don’t take anybody’s word for it’ with the implication, ‘verify through experiments’. Urban researchers – and social scientists more broadly – live in their laboratory and the data is created minute by minute. Our experiments are the interpretations of that data and the testing of theories and models – models as representations of theories. In the case of models, data is used for calibration and there has to be enough ‘left over’ for testing; or the calibrated model can be tested against a full, usually future, data set. We now live in an age of ‘big data’: the ‘minute by minute’ can be taken literally. Continue reading