Modelling

Modelling, is a practice that has become a vital part of research and development in almost every sphere of modern life.

Climate models, incorporating very complex equations of atmospheric and sea surface processes are used for weather and climate forecasting. Some of the largest computers in the world are used to run weather forecasting models. Running such models to forecast long-term climate change and its impacts is even more computationally demanding, yet hugely important for national and international policy-making.

In order to predict the behaviour of nuclear power reactors, nuclear waste storage facilities and high-energy physics experiments, very complex models incorporating the latest nuclear physics theory are used. The safety of such installations depends in part on the accuracy of these models, and the models are an integral part of their monitoring and regulation.

In developing large engineering projects, it is standard practice to build a theoretical model of the proposed equipment in order to predict its behaviour and to set the design parameters to obtain optimal results. This avoids the need to make many expensive prototypes, and is used for everything from car engines to aircraft wings to the hulls of ocean racing yachts.

Mathematical models are also nowadays widely used in the social sciences, for example in economics, management, psychology, political sciences and sociology. An entire ‘science’ of modelling complexity has grown, often beckoned by a policy community bewildered by the variety and connectedness of change in the social world. But the over-reliance on mathematical modelling has attracted criticism from those who lament the sacrifices incurred in pursuit of mathematical rigor, in particular the neglect of other approaches, including the use of simulation and various qualitative and foresight methodologies.

Those who rely on models to understand complex processes, and for prediction, optimisation and many kinds of decision- and policy-making, increasingly wish to know how much they can trust the model outputs.

Uncertainty and inaccuracy in the outputs arises from numerous sources, including error in initial conditions, error in model parameters, imperfect science in the model equations, approximate solutions to model equations and errors in model structure or logic. The nature and magnitudes of these contributory uncertainties are often very difficult to estimate, but it is vital to do so. For example, different models produce very different predictions of the magnitude of global warming effects, with no credible error bounds. Sceptics can therefore continue to ignore them and pressure groups will seize upon the most pessimistic predictions.

In choosing this as its theme for 2007/08 the IAS enabled researchers from a wide range of Science and Social Sciences departments to engage in dialogue exploring the similarities and differences in modelling techniques across different subject disciplines, and in particular to critically address the shortcomings and uncertainties inherent in the mathematical modelling approach to understanding complex processes. It also allowed Arts and Humanities scholars to explore the role of conceptual models in shaping disciplinary understanding.