Articles
-
2010-2012
Columns for World Finance magazine
-
October 2011, Adbusters.org
Economitos
Portuguese translation of extract from Economyths
-
March 2011, Books magazine
De l'impossibilité de prévoir
French translation of a Literary Review of Canada review of Megadisasters: The Science of Predicting the Next Catastrophe by Florin Diacu.
-
February 2011, Foresight
Review: This Time Is Different
Review of This Time Is Different: Eight Centuries of Financial Folly by Carmen M. Reinhart and Kenneth S. Rogoff
-
January 2010, Literary Review of Canada
Blind oracles
Researchers have developed models to predict everything from earthquakes to pandemics. The trouble is, they don't work. A review of Megadisasters: The Science of Predicting the Next Catastrophe by Florin Diacu.
-
July 2009, Adbusters
Post-Pythagorean economics
Modern economics is based on a Pythagorean paradigm. Article first published in 2006, reprinted 2009.
-
April 2009, Foresight: The International Journal of Applied Forecasting
Adam Gordon's Future Savvy
Review of Adam Gordon's book Future Savvy: Identifying Trends to Make Better Decisions, Manage Uncertainty, and Profit from Change.
Selected research papers
-
Systems Biology Approaches to Cancer Drug Development. C. Snell, D. Orrell, E. Fernandez, C. Chassagnole and D. Fell
In A. Cesario, F. Marcus (eds.), Cancer Systems Biology, Bioinformatics and Medicine, Springer, 2011. -
Commentary: In some ways the situation is even worse. D. Orrell
Foresight, 19, 16-17, 2010Responds to Adam Gordon's article in issue 19 of Foresight.
-
Using predictive mathematical models to optimise the scheduling of anti-cancer drugs. D. Orrell and E. Fernandez.
Innovations in Pharmaceutical Technology, 59-62, June 2010Predicting the best schedules for drug combination treatments.
-
Systems economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach. D. Orrell and P. McSharry.
International Journal of Forecasting, 25, 734-43, 2009 (abstract)Special issue on "Decision Making and Planning Under Low Levels of Predictability." Discusses the problems faced in predicting complex systems ranging from the human body to the economy, and how some of the methodologies of systems biology can be applied to economics.
-
Reply to commentaries. D. Orrell and P. McSharry.
Foresight, 14, pg. 39, 2009Reply to commentaries by Roy Batchelor, Paul Goodwin and Robert Fildes on "A systems approach to forecasting".
-
A systems approach to forecasting. D. Orrell and P. McSharry.
Foresight, 14, 25-30, 2009Discusses new predictive tools for complex systems such as the economy.
-
Dual feedback loops in GAL regulon suppress cellular heterogeneity in yeast. S. Ramsey, J.J. Smith, D. Orrell, M. Marelli, T.W. Petersen, P. de Atauri, H. Bolouri, J.D. Aitchison.
Nature Genetics, 38, 1082-1087, 2006 (abstract)Presents experimental results which explore the role of feedback loops in a genetic network.
-
Feedback control of stochastic noise in the yeast galactose utilization pathway. D. Orrell, S. Ramsey, M. Marelli, J.J. Smith, T.W. Petersen, P. de Atauri, J.D. Aitchison, H. Bolouri.
Physica D, 217, 64-76, 2006Gives a technique for determining the sources of noise in a genetic network – i.e. the reactions which contribute most to fluctuations in individual proteins – and applies it to the galactose utilization pathway in yeast.
-
A method to estimate stochastic noise in large genetic regulatory networks. D. Orrell, S. Ramsey, P. de Atauri, and H. Bolouri.
Bioinformatics, 21, 208-217, 2005.Describes a fast way to estimate fluctuations in genetic networks, without doing the detailed stochastic simulations. Based on the same techniques used to analyse error growth in weather models.
-
Estimating error growth and shadow behavior in nonlinear dynamical systems. D. Orrell
Int. J. Bifurcat. Chaos., 15 (10), 3265-3280, 2005.Analyses the growth of prediction errors. Includes applications to biology and weather forecasting.
-
Filtering chaos: A technique to estimate dynamical and observational noise in nonlinear systems. D. Orrell
Int. J. Bifurcat. Chaos.,15 (1), 99-107, 2005.Prediction error is due to observational error, and model error. This paper shows how the model drift can be used to separate the two.
-
Ensemble forecasting in a system with model error. D. Orrell
J. Atmos. Sci., 62 (5), 1652-1659, 2005Shows how ensemble forecasts are adversely affected by model error in a simple system, and discusses the implications for weather forecasts. See also this unpublished earlier version which includes a test for ensemble error.
-
Dizzy: Stochastic simulation of large-scale genetic regulatory networks. S. Ramsey, D. Orrell, and H. Bolouri.
J. Bioinformatics Comput. Biol., 3 (2), 1-21, 2005A computational tool developed at the Institute for Systems Biology.
-
Control of internal and external noise in genetic regulatory networks. D. Orrell and H. Bolouri.
J. Theor. Biol., 230, 301-312, 2004.Uses techniques from nonlinear dynamics to show how feedback loops and other features can reduce stochastic fluctuations in genetic networks.
-
Evolution of "design principles" in biology and engineering. P. de Atauri, D. Orrell, S. Ramsey, H. Bolouri.
IEE Syst. Biol., 1, 28-40, 2004.Presents a detailed mathematical model of the galactose utilization pathway in yeast, and discusses the roles of various network features.
-
Model error and predictability over different timescales in the Lorenz '96 systems. D. Orrell.
J. Atmos. Sci., 60, 2219-2228, 2003.Explores the connection between short, medium and long-range predictions for a "toy" weather model.
-
The spectral bifurcation diagram: Visualizing bifurcations in high-dimensional systems. D. Orrell and L. Smith.
Int. J. Bifurcat. Chaos, 13, 3015-3027, 2003.A method to visualize the dynamics of nonlinear systems using harmonics.
-
Role of the metric in forecast error growth: how chaotic is the weather? D. Orrell.
Tellus, 54A, 350-362, 2002.Shows that the apparent sensitivity to initial condition of weather models is largely an artefact of the measuring technique.
-
Model error in weather forecasting. D. Orrell, L. Smith, J. Barkmeijer, and T. Palmer.
Nonlinear Proc. Geoph., 9, 357-371, 2001.Argues that weather forecast error is due mostly to model error, rather than the butterfly effect. See also No more butterfly effect, a transcript of a 2003 radio show by the Australian Broadcasting Corporation on the role of chaos in weather forecasting.
-
Modelling nonlinear dynamical systems: chaos, error, and uncertainty. D. Orrell.
D. Phil. Thesis, Oxford University, 2001.
More
-
Gaia Theory
A brief history of the theory as developed by James Lovelock and others
-
Post-Pythagorean science and economics
Our Pythagorean heritage, and how it is being overturned by the new sciences
-
Steppenpuppy
A play