Abstract
In the course of integrative problem solving (IPS), in the broad sense, investigators encounter a plethora of models representing aspects of the real-world that seem relevant to the solution of the problem at hand. These models (mathematical or otherwise, analytical or computational) are characterized by a varying degree of complexity and fundamentality. One cannot be perfectly sure which model is the best one for the situation, in the same way that one cannot know absolutely in a metaphysical sense. This was, in fact, Plato’s philosophical perspective: ultimate reality (or pure forms; see Section 2.2.4) is too perfect to be knowable by humans.
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Notes
- 1.
Plato’s cave is probably the most famous cave in the history of philosophical thought.
- 2.
One wonders what the postmodern perspective would be concerning Item b. As noted earlier, by poignantly ignoring knowledge obtained in the past, the postmodern world has turned into sort of a black hole.
- 3.
More details are given in Section 8.3.2.
- 4.
See, also, Section 9.4.1.
- 5.
This perspective is closely linked to Occam’s razor, Section 8.2.3 that follows.
- 6.
Also known as Ockham’s razor.
- 7.
Many authors believe that the exact meaning of the term “reasonable” was not made sufficiently clear by Harold Jeffreys.
- 8.
To take a layperson’s example, let \( {\chi_{p_{j}}} \)and S q represent, respectively, the statements “The man will not get pregnant,” and “The man was taking birth control pills,” respectively. The probability of \( {\chi_{p_{j}}} \) is already very high, and the addition of S q will not increase the probability any further.
- 9.
For the probabilities of Eq. (8.8) to make sense in the integrative model-choice setting of Eq. (8.7), the corresponding entities \( ({\chi_{{p}_j}} ,\ S_{q}) \), must be substantively relevant; i.e., the relevance of the entities is based on natural causation rather than mere correlations. Otherwise said, technical model efficiency takes a back seat to physical model fidelity, in which case substantive relevance is an essential component of stochastic confirmation (conditions for a dataset to provide sound evidence for, or confirm, a model assertion, prediction, etc.). Also, when using S q to derive conditional probabilities in Eq. (8.8) the investigator should keep in mind the metalanguage issues discussed in Section 1.2.3.4 and elsewhere in the book.
- 10.
It should be noticed that, instead of the convariance another depedence function could be also used, like the space–time variogram or structure function.
- 11.
I.e., to “leave all else to the gods”; Odes, Book I, Ode IX: 9 by Quintus Horatius Flaccus (65-8 BC).
- 12.
This was General Colin Powell’s doctrine, as the readers may recall.
- 13.
Congregation for the Propagation of the Faith.
- 14.
The works of these feminist theorists, who are active mainly in academic environments, must be sharply distinguished from endeavors with very different objectives, such as the scientific study of feminine conditions (social, biological, etc.) and the women’s emancipation from male domination. Instead, radical feminists place ideological belief in the progress of women above truth, justice, equality, fairness, and scientific methodology, and they often claim to speak on behalf of all women.
- 15.
I.e., “one hand washes the other” or “the favor for the favor.” Attributed to Petronius.
- 16.
Brought to power by uninspired voters, some argue.
- 17.
- 18.
The glory of sons is their fathers. Typical is the case of the “star” professor whose career is built by his papa professor’s network (papa knows best). The “star” conveniently obtains his doctorate in papa’s institute and co-authors several of papa’s publications. Papa’s network takes care of everything, and the “star” does not need to produce any original research worth the large amounts of funding he receives.
- 19.
“We used to have actresses trying to become stars; now we have stars trying to become actresses.”
- 20.
- 21.
“There is no easy way from the Earth to the stars” (Seneca the Younger).
References
Agin, D. (2007). Junk science. New York: St Martin’s Press.
Balzac, Honoré de (1901). The works of Honoré de Balzac, vol. I (trans: Ellen Marriage). Philadelphia, PA: Avil Publishing Co.
Baudrillard, J. (1994). Simulacra and simulation. Ann Arbor, MI: The University of Michigan Press.
Berger, J. O., & Jefferys, W. H. (1991). The application of robust Bayesian analysis to hypothesis testing and Occam’s razor. Technical Report #91-04. Purdue, IN: Department of Statistics, Purdue University.
Berkun, S. (2007). The myths of innovation. Sebastopol, CA: O’Reilly Media, Inc.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.
Boer, G. J. (2004). Long time-scale potential predictability in an ensemble of coupled climate models. Climate Dynamics, 23, 29–44.
Bondi, L. (1999). Stages on journeys: some remarks about human geography and psychotherapeutic practice. The Professional Geographer, 51, 11–24.
Bothamley, J. (2002). Dictionary of theories. Detroit, MI: Visible Ink Press.
Bower, G. H., & Hilgard, E. R. (1981). Theories of learning. Englewood Cliffs, NJ: Prentice-Hall.
Briggs, D. J., Collins, S., Elliott, P., Fischer, P., Kingham, S., Lebret, E., et al. (1997). Mapping urban air pollution GIS: a regression-based approach. International Journal of Geographical Information Science, 11, 699–718.
Bunge, M. (1996). In praise of intolerance to charlatanism in academia. In Bennett, P. R. Gross, N. Levitt, & M. W. Lewis (Eds.), The flight from science and reason (pp. 96–115). Baltimore, MD: John Hopkins University Press.
Burnham, K., & Anderson, D. (2002). Model selection and multimodel inference. New York: Springer.
Charlton, B. G. (2009). Are you an honest scientist? Truthfulness in science should be an iron law, not a vague aspiration. Medical Hypotheses, 73, 633–635.
Cherkassky, V., & Mulier, F. (1998). Learning from data. New York: Wiley.
Christakos, G. (2005). Recent methodological developments in geophysical assimilation modelling. Reviews of Geophysics, 43, 1–10.
Christakos, G., Olea, R. A., Serre, M. L., Yu, H.-L., & Wang, L.-L. (2005). Interdisciplinary public health reasoning and epidemic modelling: the case of black death. New York: Springer-Verlag.
Delingpole, J. (2009) Climategate: the final nail in the coffin of ‘Anthropogenic Global Warming’? Telegraph Blogs, UK, 20 Nov 2009. doi:http://blogs.telegraph.co.uk/news/jamesdelingpole/100017393/.
Domingos, P. (1998). Occam’s two razors: the sharp and the blunt. In: Proceedings of the fourth international conference on knowledge discovery and data mining, pp. 37–43. New York: AAAI Press.
Domingos, P. (1999). The role of Occam’s razor in knowledge discovery. Data Mining and Knowledge Discovery, 3, 409–425.
Ein-Dor, P., & Feldmesser, J. (1987). Attributes of the performance of central processing units: a relative performance prediction model. Communications of the ACM, 30, 308–317.
Eller, C. (1995). Living in the lap of the goddess. Boston, MA: Beacon Press.
Eller, C. (2002). The myth of matriarchal prehistory. Boston, MA: Beacon Press.
Feynman, R. P. (1998). The meaning of it all. Reading, MA: Perseus Books.
Gilbert, M. R., & Masucci, M. (2005). Moving beyond ‘Gender and GIS’ to a feminist perspective on information technologies: the impact of welfare reform on women’s IT needs. In Lise Nelson & Joni Seager (Eds.), A companion to feminist geography (pp. 305–321). Oxford, UK: Blackwell.
Goleman, D. (1996). Vital lies simple truths. New York: Simon & Schuster.
Gregory, P. (2005). Bayesian logical data analysis for the physical sciences. Cambridge, UK: Cambridge University Press.
Grünwald, P., Myung, I. J., & Pitt, M. (Eds.). (2005). Advances in minimum description length: theory and applications. Cambridge, MA: MIT Press.
Gryparis, A., Coull, B. A., Schwartz, J., Helen, H., & Suh, H. H. (2007). Semiparametric latent variable regression models for spatiotemporal modelling of mobile source particles in the greater Boston area. Applied Statistics, 56(2), 183–209.
Haller, M. (2008). European integration as an elite process: the failure of a dream? London, UK: Routledge.
Hayles, N. K. (1992). Gender encoding in fluid mechanics: masculine channels and feminine flows. Differences: A Journal of Feminist Cultural Studies, 4(2), 16–44.
Hileman, B. (2007). An NIH director steps aside. Chemical & Engineering News. 85(35), 10, 27 Aug Issue.
Horrobin, D. F. (2001). Something rotten at the core of science? Trends in Pharmacological Sciences, 22(2), 51–52.
Ioannidis, J. P. A. (2005). Contradicted and initially stronger effects in highly cited clinical research. Journal of the American Medical Association, 294, 218–228.
Jefferys, W. H., & Berger, J. O. (1991). Sharpening Ockham’s razor on a Bayesian strop. Technical Report #91–44C. Purdue, IN: Department of Statistics, Purdue University.
Jeffreys, H. (1939). Theory of probability (3 (1961)th ed.). Oxford, UK: Clarendon Press.
Kass, R. E., & Raftery, A. E. (1993). Bayes factors and model uncertainty. Tech. Report No.254. Seattle, WA: Department of Statistics, University of Washington.
Katsouyanni, K., Samet, J. M., Anderson, H. R., Atkinson, R., Le Tertre, A., Medina, S., Samoli, E., Touloumi, G., Burnett, R. T., Krewski, D., Dominici, F., Peng, R. D., Schwartz, J., & Zanobetti, A. (2009). Air pollution and health: A European and North American approach (APHENA). Research Report 142. Boston, MA: Health Effects Institute.
Klee, R. (1997). Introduction to the philosophy of science. New York: Oxford University Press.
Kwan, M.-P. (2002). Feminist visualization: re-envisioning GIS as a method in feminist geographic research. Annals of the Association of American Geographers, 92, 645–61.
Kwan, M.-P. (2007). Affecting geospatial technologies: toward a feminist politics of emotion. The Professional Geographer, 59(1), 22–34.
Lad, F. (2006). Objective Bayesian statistics … Do you buy it? Should we sell it? Bayesian Analysis, 1(3), 441–444.
Latif, M., Collins, M., Pohlmann, H., & Keenlyside, N. (2006). A review of predictability studies of Atlantic sector climate on decadal time scales. Journal of Climate, 19, 5971–5987.
Lide, D. R. (Ed.). (2009). CRC handbook of chemistry and physics. Boca Raton, FL: CRC Press.
Martin, T. W. (2007). Scientific literacy and the habit of discourse. SEED Magazine, 12, 76–77.
Martin, M. A., & Roberts, S. (2008). A regression approach for estimating multiday adverse health effects of PM10 when daily PM10 data are unavailable. American Journal of Epidemiology, 167(12), 1511–1517.
Maziak, W. (2009). The triumph of the null hypothesis: epidemiology in an age of change. International Journal of Epidemiology, 38, 393–402.
Misner, C. W., Thorne, K. S., & John Archibald Wheeler, J. A. (1973). Gravitation. New York: W.H. Freeman.
Motulsky, H., & Christopoulos, A. (2004). Fitting models to biological data using linear and nonlinear regression. New York: Oxford University Press.
Neuman, S. P. (2003). Maximum likelihood Bayesian averaging of uncertain model predictions. Stochastic Environmental Research and Risk Assessment, 17(5), 291–305.
Nightingale, A. (2003). A feminist in the forest: situated knowledges and mixing methods in natural resource management. ACME, 2(1), 77–90.
Peng, R. D., Dominici, F., & Louis, T. (2006). Model choice in multi-site time series studies of air pollution and mortality. Journal of Royal Statistical Society, Series A, 169, 179–203.
Reiser, F. (2008). The spate of bogus research papers from Korean scientists. Skeptical Inquirer, 32(4), 5–6.
Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge, UK: Cambridge University Press.
Rothwell, P. M., & Martyn, C. N. (2000). Reproducibility of peer review in clinical neuroscience: is agreement between reviewers any greater than would be expected by chance alone? Brain, 123, 1964–1969.
Salmon, F. (2009). A formula for disaster. WIRED Mar 2009: 74–79 and 112.
Samet, J. M., Dominici, F., Zeger, S. L., Schwartz, J., & Dockery, D. W. (2000a). National morbidity, mortality, and air pollution study. Part I: methods and methodologic issues. Research Report 94(I). Cambridge, MA: Health Effects Institute.
Samet, J. M., Zeger, S. L., Dominici, F., Curriero F., Coursac I., Dockery D. W., Schwartz J., & Zanobetti A. (2000b). National morbidity, mortality, and air pollution study. Part II: morbidity and mortality from air pollution in the United States. Research Report 94(II). Cambridge, MA: Health Effects Institute.
Smolin, L. (2006). The trouble with physics: the rise of string theory, the fall of a science and what comes next. Boston, MA: Houghton Mifflin.
Sober, E. (2008). Evidence and evolution. New York: Cambridge University Press.
Sokal, A. (2008). Beyond the Hoax: science, philosophy and culture. Oxford, UK: Oxford University Press.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal Royal Statistics Society, Series B, 64, 583–639.
Suskind, R. (2004). Faith, certainty and the Presidency of George W. Bush, The New York Times Magazine, 17 Oct 2004.
Taleb, N. N. (2008a). The Black Swan. London, UK: Penguin Books.
Taleb, N. N. (2008b). The fourth quadrant: A map of the limits of statistics. Edge-The Reality Club. doi:http://www.edge.org/3rd_culture/taleb08/taleb08_index.html.
Taylor, E. F., & Wheeler, J. A. (1992). Spacetime physics: introduction to special relativity. New York: W. H. Freeman & Co.
The Editors of The Lancet. (2010). Retraction-Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. Lancet, 375(9713), 445.
van der Helm, P. A. (2000). Simplicity versus likelihood in visual perception: from surprisals to precisals. Psychological Bulletin, 126(5), 770–800.
Warner, C. M. (2007). The best system money can buy: corruption in the European Union. Ithaca, NY: Cornell University Press.
Webb, G. (1996). Further experimental evidence against the utility of Occam’s razor. Journal of Artificial Intelligence Research, 4, 397–417.
Wernick, M. N., & Aarsvold, J. N. (2004). Emission tomography. San Diego, CA: Elsevier Academic.
White, B. (2000). EPA urged to improve its scientific research. Washington Post, Thursday, 15 June 2000.
Wilde, O. (1905). De Profundis. London, UK: Methuen and Co.
Winter, C. L., & Nychka, D. (2009). Forecasting skill of model averages. Stochastic Environmental Research and Risk Assessment. doi:10.1007/s00477-009-0350-y.
Yanosky, J. D., Paciorek, C., Schwartz, J., Laden, F., Puett, R., & Suh, H. (2008). Spatio-temporal modeling of chronic PM10 exposure for the Nurses’ Health Study. Atmospheric Environment, 42, 4047–4062.
Yanosky, J. D., Paciorek, C. J., & Suh, H. H. (2009). Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the northeastern and midwestern United States. Environmental Health Perspectives, 117(4), 522–529.
Zizek, S. (2002). Welcome to the desert of the real. New York: Verso.
Zizek, S. (2009). To each according to his greed. Harper’s Magazine, 319(1913), 15–18.
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Christakos, G. (2010). On Model-Choice. In: Integrative Problem-Solving in a Time of Decadence. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9890-0_8
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