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System Dynamics Applied to Operations and Policy Decisions

Published online by Cambridge University Press:  02 May 2012

J.C.R. Hunt ,
Y. Timoshkina ,
P. J. Baudains  and
S.R. Bishop
J.C.R. Hunt
Affiliation:
Department of Earth Sciences, University College London, Gower Street, London, WC1E 6BT, UK. Email: jcrh@cpom.ucl.ac.uk Department of Mathematics, University College London, Gower Street, London, WC1E 6BT, UK
Y. Timoshkina
Affiliation:
Department of Mathematics, University College London, Gower Street, London, WC1E 6BT, UK
P. J. Baudains
Affiliation:
Department of Mathematics, University College London, Gower Street, London, WC1E 6BT, UK
S.R. Bishop
Affiliation:
Department of Mathematics, University College London, Gower Street, London, WC1E 6BT, UK
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Abstract

This paper reviews how concepts and techniques of system dynamics are being applied in new ways to analyse the operations and formation of artificial and societal systems and then to make decisions about them. The ideas and modelling methods to describe natural and technological systems are mostly reductionist (or ‘bottom-up’) and based on general scientific principles, with ad-hoc elements for any particular system. But very complex and large systems involving science, technology and society, whose complete descriptions and predictions are impossible, can still be designed, controlled and managed using the methods of system dynamics, where they are focused on the outputs of the system in relation to the input data available, and relevant external influences. For many complex systems with uncertain behaviour, their models typically combine concepts and methods of bottom-up system dynamics with statistical modelling of past or analogous data and optimization of outputs. System dynamics that has been generalized by advances in mathematical, scientific and technological research over the past 50 years, together with new approaches to the use of data and ICT, has led to powerful qualitative verbal and schematic concepts as well as improved quantitative methods, both of which have been shown to be of great assistance to decisions, notably about different types of uncertainty and erratic behaviour. This approach complements traditional decision-making methods, by introducing greater clarity about the process, as well as providing new techniques and general concepts for initial analysis, system description – using data in non-traditional ways – and finally analysis and prediction of the outcomes, especially in critical situations where system behaviour cannot be analysed by traditional decision-making methods. The scientific and international acceptance of system methods can make decision-making less implicit, and with fewer cultural assumptions. Topical examples of systems and decision-making are given.

Type
Brains and Robots
Information
European Review , Volume 20 , Issue 3 , 02 May 2012 , pp. 324 - 342
Copyright
Copyright © Academia Europaea 2012

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References

1.Epstein, J. M. and Axtell, R. (1996) Growing Artificial Societies: Social Science from the Bottom Up (Washington, DC: Brookings Institution Press).CrossRefGoogle Scholar
2.Gardner, M. (1970) Mathematical games: the fantastic combinations of John Conway's new solitaire game ‘Life’. Scientific American, 223, pp. 120–123.CrossRefGoogle Scholar
3.Foden, G. (2009) Turbulence (London: Faber and Faber).Google Scholar
4.Bondi, C. (1991) New Applications of Mathematics (London: Penguin).Google Scholar
5.Poincare, H. (1905) The principles of mathematical physics. Reprinted from The Monist, 15(1).Google Scholar
6.Porter, T. (2004) Karl Pearson: The Scientific Life in a Statistical Age (Princeton: Princeton University Press).Google Scholar
7.Sutherland, I. (ed.) (1993) Collected Papers of Lewis Fry Richardson: Quantitative Psychology and Studies of Conflict (Cambridge: Cambridge University Press).Google Scholar
8.Smuts, J. (1926) Holism and Evolution (London: Macmillan and Co).Google Scholar
9.Weaver, W. (1948) Science and complexity. American Scientist, 36(536).Google ScholarPubMed
10.Von Bertalanffy, L. (1968) General System Theory: Foundations, Development, Applications (New York: Braziller).Google Scholar
11.Wilson, A. (2000) Complex Spatial Systems: The Modelling Foundations of Urban and Regional Analysis (Harlow: Prentice Hall).Google Scholar
12.Prigogine, I. and Stengers, I. (1984) Order out of Chaos: Man's new Dialogue with Nature (London: Heinemann).Google Scholar
13. Gray, R. and Robinson, P. (2009) Stability of random brain networks with excitatory and inhibitory connections. Neurocomputing, 72(7–9), pp. 1849–1858.CrossRefGoogle Scholar
14.Mitchell, M. (2009) Complexity: A Guided Tour (New York: Oxford University Press).CrossRefGoogle Scholar
15.Johnson, N. (2009) Simply Complexity: A Clear Guide to Complexity Theory (Oxford: One World).Google Scholar
16.Favre, A. (1995) Chaos and Determinism: Turbulence as a Paradigm for Complex Systems Converging Toward Final States (Baltimore: Johns Hopkins University Press).CrossRefGoogle Scholar
17.Parry, M. and Carter, T. (1998) Climate Impact and Adaptation Assessment: A Guide to the IPCC Approach (London: Earthscan Publications Ltd).Google Scholar
18.Bienhocker, E. (2006) The Origin of Wealth (London: Random House).Google Scholar
19.Ferguson, N. M., Donnelly, C. A. and Anderson, R. M. (2001) The foot-and-mouth epidemic in Great Britain: pattern of spread and impact of interventions. Science, 292(5519), pp. 1155–1160.CrossRefGoogle ScholarPubMed
20.Holmes, J. (2009) Exploring in Security: Towards an Attachment-informed Psychoanalytic Psychotherapy (Hove: Routledge).CrossRefGoogle Scholar
21.Thompson, J. and Bishop, S. R. (eds) (1994) Nonlinearity and Chaos in Engineering Dynamics (Chichester: Wiley).Google Scholar
22.Vernadsky, V. (1998 [1926]) The Biosphere (New York: Copernicus Springer-Verlag).CrossRefGoogle Scholar
23.Hansen, J. (1997) Mouvements granulaires relatifs dans une poudre fluidisée. Proceedings of Conference on Turbulence and Determinism (Grenoble).Google Scholar
24.Hunt, J. C. R., Abell, C. J., Peterka, J. A. and Woo, H. G. C. (1978) Kinematical studies of the flow around free or surface-mounted obstacles: applying topology to flow visualization. Journal of Fluid Mechanics, 86(1), pp. 179–200.CrossRefGoogle Scholar
25.Steadman, P. (1983) Architectural Morphology (London: Pion).Google Scholar
26.Barrat, A., Barthelemy, M., Pastor-Satorras, R. and Vespignani, A. (2004) The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11), p. 3747.CrossRefGoogle ScholarPubMed
27.Reza, M. I. H. and Abdullah, S. A. (2010) Ecological connectivity framework in the state of Selangor, Peninsular Malaysia: A potential conservation strategy in the rapid changing tropics. Journal of Ecology and the Natural Environment, 2(5), pp. 73–83.Google Scholar
28.Healy, P. and Palepu, K. (2003) The fall of Enron. Journal of Economic Perspectives, 17(2), pp. 3–26.CrossRefGoogle Scholar
29.J. Bentham (1787) The Panopticon. Writings, Retrieved 17 July 2009, from http://cartome.org/panopticon2.htm.Google Scholar
30.England, J., Agarwal, J. and Blockley, D. (2008) The vulnerability of structures to unforeseen events. Computers and structures, 86(10), 1042–1051.CrossRefGoogle Scholar
31.X. Quan and N. Birnbaum (2002) Computer simulation of impact and collapse of New York World Trade Center North Tower. 20th International Symposium on Ballistics, Orlando, Florida, USA.Google Scholar
32.Greeves, C. Z., Pope, V. D., Stratton, R. A. and Martin, G. M. (2006) Representation of Northern Hemisphere winter storm tracks in climate models. Climate Dynamics, 28(7–8), pp. 683–702.CrossRefGoogle Scholar
33.Mitchell, J. F. B., Davis, R. A., Ingram, W. J. and Senior, C. A. (1995) On surface temperature, greenhouse gases, and aerosols: models and observations. Journal of Climate, 8, pp. 2364–2386.2.0.CO;2>CrossRefGoogle Scholar
34.Angeloudis, P. and Fisk, D. (2006) Large subway systems as complex networks. Physica A, 367, pp. 553–558.CrossRefGoogle Scholar
35.Stuyt, L. (2006) Design and performance of materials for subsurface drainage systems in agriculture. Agricultural Water Management, 86(1–2), pp. 50–59.CrossRefGoogle Scholar
36.White, G. F. and Haas, J. E. (1975) Assessment of Research on Natural Hazards (Cambridge, MA: MIT Press).Google Scholar
37.Helbing, D. (2008) Managing Complexity: Insights, Concepts, Applications (Berlin: Springer).CrossRefGoogle Scholar
38.Hunt, J. C. R., Bohnenstengel, S. I., Belcher, S. E. and Timoshkina, Y. (in press) Implications of Climate change for expanding cities worldwide. Urban Design & Planning Journal, forthcoming.Google Scholar
39.Smith, J. R. (1983) Advocating owner-occupation in the inner city: some lessons from American experience for U.K. low-cost home ownership programmes. Journal of Environmental Planning and Management, 26(1), pp. 40–43.Google Scholar
40.Mujis, D., Harris, A., Chapman, C., Stoll, L. and Russ, J. (2005) Improving schools in socio-economically disadvantaged areas - a review of research evidence. In: P. Clarke (ed.) Improving Schools in Difficulty (London, UK; New York, USA: Continuum International Publishing Group).Google Scholar
41.Schelling, T. C. (1971) Dynamics models of segregation. Journal of Mathematical Sociology, 1(2), pp. 143–186.CrossRefGoogle Scholar
42.Hunt, J. C. R., Eames, I. and Westerweel, J. (2006) Mechanics of inhomogeneous turbulence and interfacial layers. Journal of Fluid Mechanics, 554, pp. 499–519.CrossRefGoogle Scholar
43.Stelling, P. and Tucker, D. (2007) Floods, Faults, and Fire: Geological Field Trips in Washington State and Southwest British Columbia (Boulder, Colorado: Geological Society of America).CrossRefGoogle Scholar
44.Richardson, L. F. (1960) Statistics of Deadly Quarrels (Pittsburgh: Boxwood Press).Google Scholar
45.Perfahl, H., Byrne, M., Chen, T., Estrella, V., Alarcon, T., Lapin, A., Gatenby, R., Gillies, R., Lloyd, C., Maini, P., Reuss, M. and Owen, M. (2011) Multiscale modelling of vascular tumour growth in 3D: the roles of domain size and boundary conditions. PLoS ONE, 6(4), p. e14790.CrossRefGoogle ScholarPubMed
46.J. C. R Hunt (2006) Communicating big themes in applied mathematics. Mathematical modelling-education, engineering and economics. Proceedings of ICTMA12, London.Google Scholar
47.Gladwell, M. (2000) The Tipping Point: How Little Things can Make a Big Difference (New York: Little, Brown).Google Scholar
48.Jaeger, C., Horn, G. and Lux, T. (2009) From financial crisis to sustainability. Joint Report to German Federal Ministry of Environment (Potsdam Institut für Klimatologie).Google Scholar
49.Saunders, P. T. (1980) Introduction to Catastrophe Theory (Cambridge: Cambridge University Press).CrossRefGoogle Scholar
50.Zeeman, E. (1987) On the Psychology of a Hijacker. Analysing Conflict and its Resolution: Some Mathematical Contributions. P. Bennett (ed.) (Oxford: Oxford University Press).Google Scholar
51.D. Helbing, A. Johansson and H. Z. Al-Abideen (2007) The dynamics of crowd disasters: an empirical study. Physics Review E, 75, p. 046109; doi:10.1103/PhysRevE.75.046109.CrossRefGoogle Scholar
52.Lighthill, M. and Whitham, G. (1955) On kinematic waves. II: A theory of traffic flow on long crowded roads. Proceedings of the Royal Society A, 229(1178), pp. 317–345.Google Scholar
53.Layard, R. (2011) Happiness: Lessons from a New Science, 2nd edn (London: Penguin).Google Scholar