by David Orrell
Protein: A protein is a molecule consisting of a string of amino acids folded into a particular shape. Proteins make up the structure of cells and carry out most of an organism’s functions.
Random walk: If a drunk stumbles around, taking steps of fixed length in random directions, then the path he traces out is a random walk. The random walk theory is used in physics to describe the random motion of small particles, in finance to describe the random fluctuations of an asset’s value, and by the police to test drunk drivers. See also stochastic process.
RATIONAL NUMBERS: These are numbers that can be expressed as a fraction, such as ID="365"> . All other numbers (i.e., most of them) are called irrational.
Reductionism: This is the belief, championed by Descartes, that the behaviour of systems can be understood by reducing them to their simplest parts. An example is the selfish gene theory. See also holism.
RMS (ROOT MEAN SQUARE): For a set of N numbers {x, x, ID="368"> , x}, the RMS 12Nis given by the square root of the sum of squares: x2+ x122+ ID="369"> + xN2 . The RMS has many applications. For the case N = 2, it can be viewed in terms of a diagonal distance, from the theorem of Pythagoras. If the different numbers represent errors in N separate variables, the RMS is often used as a measure of the total error.
RNA (RIBONUCLEIC ACID): Created by transcription of DNA, RNA plays a key role in the cell, especially as an intermediary between genes and proteins.
SCEPTIC: One who questions and critically examines whatever passes for knowledge, and acknowledges doubt and uncertainty. A global-warming sceptic should therefore be someone who believes that we cannot predict climate change, rather than (its current usage) someone who believes it will not happen.
SELFISH GENE THEORY: This is the reductionist theory, first proposed by Richard Dawkin (1976), that evolution is the net product of independent, individual genes trying to maximize their reproductive success. The theory is therefore a kind of efficient market theory for the genome (see EMH). Supporters of the theory emphasize that it does not imply that genes are endowed with their own motives—only that they can be viewed as acting as if they are.
SENSITIVITY TO INITIAL CONDITION: A mathematical model has this property if small changes to the initial value of the variables (i.e., the initial condition) result in very different forecasts. See also butterfly effect.
SENSITIVITY TO PARAMETERIZATION: A mathematical model has this property if small changes to the parameterizations used (for example, the value of certain parameters) result in very different forecasts. Climate change predictions are generally insensitive to small changes in initial condition but sensitive to parameterization.
SHADOW ORBIT: This refers to a trajectory of a dynamical system that stays within a specified distance—the shadow radius—of a sequence of observations. A model’s accuracy over a certain time period can be assessed by searching for a trajectory, starting from a perturbed initial condition, which shadows within as small a shadow radius as possible (while allowing for uncertainty in the observations). The smaller the shadow radius, the better the model is at matching the data. The expected radius can be compared with estimates from the model drift. Together, they provide two independent methods to assess model error.
STANDARD DEVIATION: This is a measure of the width of a normal distribution. It’s also used as a measure of volatility of some fluctuating quantity, where the implicit assumption is that the deviations from the mean follow a normal distribution. The square of the standard deviation is known as the variance.
STOCHASTIC: A process is stochastic if it is random. If two players bet repeatedly on the flip of a coin, then the score will represent a stochastic process, with some similarity to the fluctuations of a financial asset.
TRAJECTORY: A trajectory is the solution to a dynamical system, starting from a particular initial condition, which traces out the values of the variables as a function of time. It’s sometimes also called an orbit (as in shadow orbit).
UNCOMPUTABLE: This term is used here to refer to a physical, biological, or other real-world system that cannot be accurately modelled using equations. Some features of uncomputable systems may be compared with the emergent properties of complex mathematical systems. An example is the formation or dissipation of clouds, for which no accurate equations exist.
VALIDATION: An argument is considered valid if it is free from any obvious logical flaws. By analogy, a mathematical model is valid if there isn’t anything obviously wrong with it. The word is confusingly used in areas such as climate science; people say, for example, that a model has been “validated” because its results are consistent with a set of observations. This “validity” is then used to imply that the model is an accurate representation of the underlying system. However, the model equations can always be tuned to fit observed data, and no matter what tests are applied, there is no guarantee that a model will continue to work in the future, or that it even bears any close resemblance to the function of the system (Oreskes et al. 1994). Note that even highly imperfect models can still be useful for gaining insights and proposing testable hypotheses.
VARIABLE: A variable is any number in an equation that is allowed to change. In the example of the falling stone in Chapter 2, time, position, and velocity are all variables. See also parameter.
VOLATILITY: In economics, volatility refers to the size of fluctuations in an asset’s value around its mean. It’s often used as a proxy for risk. Volatility can be measured by the standard deviation, though this assumes that the deviations follow a normal distribution.
BIBLIOGRAPHY
Abraham, C. (2005). Race. The Globe and Mail, June 18, 2005.
Abraham, R. (1994). Chaos, Gaia, Eros: A chaos pioneer uncovers the three great streams of history. New York: HarperCollins.
Acar, M., Becskei, A. and van Oudenaarden, A. (2005). Enhancement of cellular memory by reducing stochastic transitions. Nature, 435, 228–232.
ACIA (2004). Impacts of a warming arctic: Arctic climate impact assessment.
Cambridge: Cambridge University Press.
Ackerman, J. (2001). Chance in the house of fate: A natural history of heredity. Boston: Houghton Mifflin.
Albright, R. (2002). What can past technology forecasts tell us about the future? Technological Forecasting and Social Change, 69, 443–64.
Allen, M. (2005). A novel view of global warming. Nature, 433, 198.
Allen, M., Kettleborough, J., and Stainforth, D. (2002). Model error in weather and climate forecasting. From the proceedings of the 2002 ECMWF Predictability Seminar, European Centre for Medium-range Weather Forecasting, Reading, U.K., pp. 275–94.
Alter, D., and Eny, K. (2005). The relationship between the supply of fast-food chains and cardiovascular outcomes. Canadian Journal of Public Health, 96 (3), 173–77.
Anonymous (1999). We woz wrong. The Economist, December 16, 1999.
Anonymous (1999a). Rethinking thinking. The Economist, December 16, 1999. Anonymous (1999b). It never rains. The Economist, September 16, 1999.
Anonymous (2000). Who owns your genes? The Economist, June 29, 2000. Anonymous (2000a). Climate change may be down to farming. Guardian, November 4, 2000.
Anonymous (2001). The “skeptical environmentalist.” The Economist, September 6, 2001.
Anonymous (2001a). Say “R”. The Economist, November 29, 2001.
Anonymous (2002). The race to computerise biology. The Economist, December 12, 2002.
Anonymous (2003). Hot potato revisited. The Economist, November 6, 2003.
Anonymous (2004). Up close, and personal. The Economist, October 16, 2004.
Anonymous (2005). Are you being served? The Economist, April 21, 2005.
Anonymous (2005a). Amazon destruction accelerating in Brazil. Reuters, May 19, 2005.
Anonymous (2005b). Divining the future. The Economist, January 15, 2005. Anonymous (2005c). Q&A with Laurie Garrett. Foreign Affairs, 84 (July/August).
Anonymous (2005d). Hotting up. The Economist, February 3, 2005.
>
Anonymous (2005e). WHO: Impossible to predict bird flu deaths. The Associated Press, September 30, 2005. [http://abcnews.go.com/Health/wireStory?id=1172896 ] Anonymous (2005f). Weather risk: Natural hedge. The Economist, September 29, 2005.
Anonymous (2005g). Personalised drugs ‘decades away.’ BBC News Online, September 21, 2005.
Heinemann.
Aristotle (1981). The Politics. Translated by T. J. Saunders and revised by T. A. Sinclair. London: Penguin Classics.
Arrhenius, S. (1896). On the influence of carbonic acid in the air upon the temperature of the ground. Philosophical Magazine, 41, 237.
Asimov, I. (1951). Foundation. New York: Avon.
Bachelier, L. (1900). Théorie de la spéculation. Annales Scientifiques de l’Ecole Normale Supérieure, 17, 21–86. [English translation by P. Cootner (1964), pp. 17–78.] Baker, D., DeLong, J. B., and Krugman, P. R. (2005). Asset returns and economic growth. Brookings Papers on Economic Activity, 1, 289–315.
Barabási, A. L., and Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–12.
Barabási, A. L., and Oltvai, Z. N. (2004). Network biology: Understanding the cell’s functional organization. Nature Reviews Genetics, 5, 101–13.
Barnett, J., and Adger, W. M. (2003). Climate dangers and atoll countries.
Climatic Change, 61, 321.
Baron-Cohen, S. (2003). The essential difference: Male and female brains and the truth about autism. New York: Basic Books.
Bass, T. A. (1999). The predictors. New York: Henry Holt.
Batra, R. (1987). The great depression of 1990. New York: Simon and Schuster. Bazell, R. (2005). Movies help doctors discover autistic minds: Cutting-edge research at Yale may help with early detection. NBC Nightly News, February 24, 2005.
Bernstein, P. L. (1998). Against the gods: The remarkable story of risk. Toronto: John Wiley.
Bjerknes, V. (1904). Das Problem der Wettervorhersage, betrachtet vom Stadpunkte der Mechanik und der Physik. (Weather forecasting as a problem in mechanics and physics). Meteorologische Zeitschrift, 21, 1–7.
Bjerknes, V. (1911). Dynamic meteorology and hydrography, Part 2:
Kinematics. New York: Gibson Bros.
Blum, W. (2002). Folgenloser Flügelschlag. Bild der wissenschaft, June 2005, p. 46.
Bogen, J. E. (1975). Some educational aspects of hemisphere specialization. UCLA Educator, 17, 24–32.
Bohm, D. (1974). On the subjectivity and objectivity of knowledge. In John Lewis (ed.), Beyond chance and necessity. London: Garnerstone Press.
Bollerslev, T. P. (1986). Generalized autoregressive conditional heteroskedas-ticity. Journal of Econometrics, 31, 307–27.
Bosart, L. F. (2003). Whither the weather analysis and forecasting process?
Weather Forecasting, 18, 520–29.
Bragg, M. (1999). On giants’ shoulders. New York: Wiley.
Brookes, M. (2004). Extreme measures: The dark visions and bright ideas of Francis Galton. New York: Bloomsbury.
Brooks, R. (2002). Flesh and machines: How robots will change us. New York: Pantheon.
Brumfiel, G. (2004). Newton’s religious screeds get online airing. Nature, 430, 819.
Buchanan, M. (2000). Ubiquity. London: Weidenfeld and Nicolson.
Buizza, R., Barkmeijer, J., Palmer, T. N., and Richardson, D. S. (2000).
Current status and future developments of the ECMWF Ensemble Prediction System. Meteorological Applications, 7, 163–75.
Byers, M. (2005). On thinning ice. London Review of Books, 27, January 6, 2005.
Byrne, F. (2005). Lunch with the FT: Make it snappy. Financial Times, January 28, 2005.
Calder, N. (2003). Magic universe: The Oxford guide to modern science.
Oxford: Oxford University Press.
Campbell, D. and Lee, R. (2003). “Carnage by computer”: The blackboard economics of the 2001 foot and mouth epidemic. Social & Legal Studies, 12, 425-459.
Capra, F. (1983). The tao of physics. Boston: Shambhala.
Capra, F. (1996). The web of life: A new scientific understanding of living systems. New York: Anchor Books.
Capra, F. (2002). The hidden connections. New York: Anchor Books.
Chagnon, S., ed. (2000). El Niño 1997–1998: The climate event of the century. Oxford: Oxford University Press.
Charlson, R., Lovelock, J., Andreae, M., and Warren, S. (1987). Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature, 326, 655–61.
Cheung, Y.-W., Chinn, M. and Garcia Pascual, A. (2005). Empirical exchange rate models of the nineties: are any fit to survive? Journal of International Money & Finance, 24, 1150–1175.
Chomsky, N. (1988). Language and the problems of knowledge. Cambridge, MA: MIT Press.
Christianson, J.R. (2000). On Tycho’s island: Tycho Brahe and his assistants 1570–1601. Cambridge: Cambridge University Press.
Churchill, W. (1932). Fifty years hence. Popular Mechanics (March), 390.
Cohn, S. (1997). An introduction to estimation theory. Journal of the Meteorological Society of Japan, 75, 257–88.
Connor, J. A. (2004). Kepler’s witch : An astronomer’s discovery of cosmic order amid religious war, political intrigue, and the heresy trial of his mother. New York: HarperCollins.
Cootner, P. (1964). The random character of stock market prices. Cambridge, MA: MIT Press.
Cornford, F. M. 1969 (1923). Greek religious thought. New York: AMS Press. Coulson, S., and Williams, R. F. (2005). Hemispheric asymmetries and joke comprehension. Neuropsychologia, 43, 128–41.
Coupland, D. (2002). Souvenir of Canada. Vancouver: Douglas and McIntyre.
Cowles, A. (1933). Can stock market forecasters forecast? Econometrica, 12, 206–14. Cox, J. D. (2002). Storm watchers: The turbulent history of weather prediction from Franklin’s kite to El Niño. Hoboken, NJ: John Wiley.
Crichton, M. (2002). Prey. New York: HarperCollins.
Crichton, M. (2004). State of Fear. New York: HarperCollins.
Cullen, M.R. (2005). Serum osteopontin levels—is it time to screen asbestos-exposed workers for pleural mesothelioma? New England Journal of Medicine, 353, 1564–73.
Daly, H.E. and Cobb, J.B. Jr. (1989). For the common good. Boston: Beacon Press.
Danchin, A. (2002). The delphic boat: What genomes tell us. Cambridge, MA: Harvard University Press.
Danforth, C., and Yorke, J. A. (2005). Making forecasts for chaotic physical processes. Physical Review Letters, 96, 144102.
Darwin, C. (1905). The voyage of a naturalist round the world in H.M.S.
“Beagle”. New York: Routledge.
Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press.
De Atauri, P., Orrell, D., Ramsey, S., and Bolouri, H. (2004). Evolution of “design principles” in biology and engineering. IEE Systems Biology, 1, 28–40. Deacon, R. (1968). John Dee: Scientist, geographer, astrologer and agent to Elizabeth I. London: Frederick Muller.
Dennis, C. (2004). The most important sexual organ. Nature, 427, 390.
Descartes, R. (1960). Discourse on Method. Translation by Arthur Wollaston. Harmondsworth, UK: Penguin.
Diamond, J. (2005). Collapse. New York: Penguin.
Downton, R. A., and Bell, R. S. (1988). The impact of analysis differences on a medium-range forecast. Meteorological Magazine, 117, 279–85.
Draper, J. W. (1874). History of the conflict between religion and science. New York: D. Appleton and Company.
Drexler, E. (1986). Engines of creation. New York: Anchor Books.
Duncan, D. E. (2003). Reversing bad truths. Discover, 24, 20.
Dunphy, S. (2004). The inflation conundrum. Seattle Times, February 22, 2004.
Ebert, E. E., Damrath, U., Wergen, W., and Baldwin, M. E. (2003). The WGNE assessment of short-term quantitative precipitation forecasts.
Bulletin of the American Meteorological Soci
ety, 84, 481–92.
Edelman, G. (1987). Neural Darwinism: The theory of neuronal group selection. New York: Basic Books.
Edwards, B. (1999). Drawing on the right side of the brain. London:
HarperCollins.
Edwards, P. N. (2000). A brief history of atmospheric general circulation modeling. In David A. Randall (ed.), General circulation model development. San Diego: Academic Press.
Ehrlich, P. R. (1968). The population bomb. New York: Ballantine Books.
Ehrlich, P. R. (2000). Human natures: Genes, cultures, and the human prospect. Washington, DC: Island Press.
Ehrlich, P. R., and Ehrlich, A. (2004). One with Nineveh: Politics, consumption and the human future. Washington, DC: Island Press.
Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686–88.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica, 50, 987–1008.
Errico, R. M., Langland, R., and Baumhefner, D. P. (2002). The workshop in atmospheric predictability. Bulletin of the American Meteorological Society, 83, 1341–43.
Eto, M., Watanabe, K., and Makino, I. (1989). Increased frequencies of apolipoprotein E2 and E4 alleles in patients with ischemic heart disease. Clinical Genetics, 36, 183–88.
Eubank, S., et al. (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429, 180–84.
Fama, E. F. (1965). Random walks in stock-market prices. Selected Papers, 16. Chicago: University of Chicago, Graduate School of Business.
Ferguson, G. W. (1954). Signs and symbols in Christian art. New York:
Oxford University Press.
Field, G. C. (1956). The philosophy of Plato. Oxford: Oxford University Press.
Fischetti, M. (2001). Drowning New Orleans. Scientific American, October 2001.
Foley, S. (2005). Roche is boosted by avian flu fears. Independent, October 20, 2005.
Frank, K. T., Petrie, B., Choi, J. S., and Leggett, W. C. (2005). Trophic cascades in a formerly cod-dominated ecosystem. Science, 10, 1621–23.