Abstraction in ecology: reductionism and holism as complementary heuristics
Tóm tắt
In addition to their core explanatory and predictive assumptions, scientific models include simplifying assumptions, which function as idealizations, approximations, and abstractions. There are methods to investigate whether simplifying assumptions bias the results of models, such as robustness analyses. However, the equally important issue – the focus of this paper – has received less attention, namely, what are the methodological and epistemic strengths and limitations associated with different simplifying assumptions. I concentrate on one type of simplifying assumption, the use of mega parameters as abstractions in ecological models. First, I argue that there are two kinds of mega parameters qua abstractions, sufficient parameters and aggregative parameters, which have gone unnoticed in the literature. The two are associated with different heuristics, holism and reductionism, which many view as incompatible. Second, I will provide a different analysis of abstractions and the associated heuristics than previous authors. Reductionism and holism and the accompanying abstractions have different methodological and epistemic functions, strengths, and limitations, and the heuristics should be viewed as providing complementary research perspectives of cognitively limited beings. This is then, third, used as a premise to argue for epistemic and methodological pluralism in theoretical ecology. Finally, the presented taxonomy of abstractions is used to comment on the current debate whether mechanistic accounts of explanation are compatible with the use of abstractions. This debate has suffered from an abstract discussion of abstractions. With a better taxonomy of abstractions the debate can be resolved.
Tài liệu tham khảo
Beatty, J. (1995). The evolutionary contingency thesis. In G. Wolters & J. Lennox (Eds.), Concepts, theories, and rationality in the biological sciences (pp. 45–81). Konstanz: Universitätsverlag Konstanz.
Boone, W., & Piccini, G. (2016). The cognitive neuroscience revolution. Synthese, 193(5), 1509–1534. https://doi.org/10.1007/s11229-015-0783-4.
Cohen, J. (1985). Can fitness be aggregated? American Naturalist, 125(5), 716–729. https://doi.org/10.1086/284374.
Fodor, J. (1974). Special sciences (or: The disunity of science as a working hypothesis). Synthese, 28(2), 97–115. https://doi.org/10.1007/BF00485230.
Gardner, R., Cale, W., & O’Neill, R. (1982). Robust analysis of aggregation error. Ecology, 63(6), 1771–1779. https://doi.org/10.2307/1940119.
Grimm, V. (1999). Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future? Ecological Modelling, 115(2-3), 129–148. https://doi.org/10.1016/S0304-3800(98)00188-4.
Haug, M. (2011). Abstraction and explanatory relevance; or, why do the special sciences exist? Philosophy of Science, 78(5), 1143–1155. https://doi.org/10.1086/662257.
Holling, C. (1959). The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Canadian Etymologist, 91(05), 293–320. https://doi.org/10.4039/Ent91293-5.
Horgan, T. (1993). From supervenience to superdupervenience: Meeting the demands of a material world. Mind, 102(408), 555–586. https://doi.org/10.1093/mind/102.408.555.
Iwasa, Y., Andreasen, V., & Levin, S. (1987). Aggregation in model ecosystems I. Perfect aggregation. Ecological Modelling, 37(3-4), 287–302. https://doi.org/10.1016/0304-3800(87)90030-5.
Iwasa, Y., Levin, S., & Andreasen, V. (1989). Aggregation in model ecosystems II. Approximate aggregation. Journal of Mathematics Applied in Medicine & Biology, 6(1), 1–23. https://doi.org/10.1093/imammb/6.1.1-a.
Jamniczky, H. (2005). Biological pluralism and homology. Philosophy of Science, 72(5), 687–698. https://doi.org/10.1086/508108.
Jeltsch, F., Müller, M., Grimm, V., Wissel, C., & Brandl, R. (1997). Pattern formation triggered by rare events: Lessons from the spatial spread of rabies. Proceeding of the Royal Society of London. Series B: Biological Sciences, 264, 495–503.
Kaplan, M. (2011). Explanation and description in computational neuroscience. Synthese, 183(3), 339–373. https://doi.org/10.1007/s11229-011-9970-0.
Kim, J. (2006). Emergence: Core ideas and issues. Synthese, 151(3), 547–559. https://doi.org/10.1007/s11229-006-9025-0.
Kitcher, P. (1984). 1953 and all that. A tale of two sciences. Philosophical Review, 93(3), 335–373. https://doi.org/10.2307/2184541.
Korpimäki, E., & Norrdahl, K. (1989). Predation of Tengmalm’s owls: Numerical responses, functional responses and dampening impact on population fluctuations of microtines. Oikos, 54(2), 154–164. https://doi.org/10.2307/3565261.
Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2010). Economic modelling as robustness analysis. British Journal for Philosophy of Science, 61(3), 541–567. https://doi.org/10.1093/bjps/axp049.
Lane, P., Lauff, G., & Levins, R. (1976). The feasibility of using a holistic approach in ecosystem analysis. In S. Levin (Ed.), Ecosystem analysis and prediction: Proceedings of a SIAM-SIMS conference held at Alta, Utah, July 1–5, 1974 (pp. 111–128). Philadelphia: Society for Industrial and Applied Mathematics.
Levin, S. (1991). The problem of relevant detail. In S. Busenberg & M. Martelli (Eds.), Differential equations in biology, epidemiology and ecology (pp. 9–15). New York: Springer. https://doi.org/10.1007/978-3-642-45692-3_1.
Levin, S. (1992). The problem of pattern and scale in ecology. Ecology, 73(6), 1943–1967. https://doi.org/10.2307/1941447.
Levins, R. (1966). The strategy of model building in population biology. American Scientists, 54, 421–431.
Levins, R. (1998). The internal and external in explanatory theories. Science as Culture, 7(4), 557–582. https://doi.org/10.1080/09505439809526525.
Levins, R. (2006). Strategies of abstraction. Biology and Philosophy, 21, 741–755.
Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80(2), 241–261. https://doi.org/10.1086/670300.
Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25. https://doi.org/10.1086/392759.
Miłkowski, M. (2016). Explanatory completeness and idealization in large brain simulations: A mechanistic perspective. Synthese, 193(5), 1457–1478. https://doi.org/10.1007/s11229-015-0731-3.
Murray, J., Stanley, E., & Brown, D. (1986). On the spatial spread of rabies among foxes. Proceeding of the Royal Society of London Series B: Biological Sciences, 299, 111–150.
Musgrave, A. (1981). ‘Unreal assumptions’ in economic theory: The f-twist untwisted. Kyklos, 34(3), 377–387. https://doi.org/10.1111/j.1467-6435.1981.tb01195.x.
Mäki, U. (2000). Kinds of assumptions and their truth: Shaking an untwisted f-twist. Kyklos, 53(3), 317–336. https://doi.org/10.1111/1467-6435.00123.
Nathan, M. (2012). The varieties of molecular explanation. Philosophy of Science, 79(2), 233–254. https://doi.org/10.1086/664791.
Odenbaugh, J. (2005). Idealized, inaccurate but successful: A pragmatic approach to evaluating models in theoretical ecology. Biology and Philosophy, 20(2-3), 231–255. https://doi.org/10.1007/s10539-004-0478-6.
Paine, R. (1966). Food wed complexity and species diversity. American Naturalist, 100(910), 65–75. https://doi.org/10.1086/282400.
Persson, L., & Diehl, S. (1990). Mechanistic individual-based approaches in the population/community ecology of fish. Annales Zoologi Fennica, 27, 165–182.
Putnam, H. (1975). Mind, language, and reality. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511625251.
Raerinne, J. (2011). Causal and mechanistic explanations in ecology. Acta Biotheoretica, 59(3-4), 251–271. https://doi.org/10.1007/s10441-010-9122-9.
Raerinne, J. (2013a). Explanatory, predictive, and heuristic roles of allometries and scaling relationships. Bioscience, 63(3), 191–198. https://doi.org/10.1525/bio.2013.63.3.7.
Raerinne, J. (2013b). Robustness and sensitivity of biological models. Philosophical Studies, 166(2), 285–303. https://doi.org/10.1007/s11098-012-0040-3.
Raerinne, J. (2017). Explanations of exceptions in biology: Corrective asymmetry vs. autonomy. Synthese. https://doi.org/10.1007/s11229-016-1195-9.
Raerinne, J., & Baedke, J. (2015). Exclusions, explanations, and exceptions: On the causal and lawlike status of the competitive exclusion principle. Philosophy and Theory in Biology, 7, 1–17.
Rastetter, E., King, A., Cosby, B., Hornberger, G., O’Neill, R., & Hobbie, J. (1992). Aggregating fine-scale ecological knowledge to model coarser-scale attributes to ecosystems. Ecological Applications, 2(1), 55–70. https://doi.org/10.2307/1941889.
Reisman, K., & Forber, P. (2005). Manipulation and the causes of evolution. Philosophy of Science, 72(5), 1113–1123. https://doi.org/10.1086/508120.
Schoener, T. (1986). Mechanistic approaches to community ecology: A new reductionism? American Zoologist, 26, 86–106.
Thulke, H.-H., Grimm, V., Müller, M., Staubach, C., Tischendorf, L., Wissel, C., & Jeltsch, F. (1999). From pattern to practice: A scaling-down strategy for spatially explicit modelling illustrated by the spread and control of rabies. Ecological Modelling, 117(2-3), 179–202. https://doi.org/10.1016/S0304-3800(98)00198-7.
Tilman, D. (1977). Resource competition between planktonic algae: An experimental and theoretical approach. Ecology, 58(2), 338–348. https://doi.org/10.2307/1935608.
Tilman, D. (1980). A graphical-mechanistic approach to competition and predation. American Naturalist, 116(3), 362–393. https://doi.org/10.1086/283633.
Tilman, D. (1986). A consumer-recourse approach to community ecology. American Zoologist, 26(1), 5–22. https://doi.org/10.1093/icb/26.1.5.
Tilman, D. (1990). Constraints and tradeoffs: Toward a predictive theory of competition and succession. Oikos, 58(1), 3–15. https://doi.org/10.2307/3565355.
Vellend, M. (2010). Conceptual synthesis in community ecology. QUARTERLY REVIEW OF BIOLOGY, 82, 183–206.
Weisberg, M., & Reisman, K. (2008). The robust Volterra principle. Philosophy of Science, 75(1), 106–131. https://doi.org/10.1086/588395.
Weslake, B. (2010). Explanatory depth. Philosophy of Science, 77(2), 273–294. https://doi.org/10.1086/651316.
Wimsatt, W. (1980). Randomness and perceived-randomness in evolutionary biology. Synthese, 43(2), 287–339. https://doi.org/10.1007/BF00413929.
Wimsatt, W. (1986). Forms of aggregativity. In A. Donogan, A. Perovich, & M. Wedin (Eds.), Human nature and natural knowledge (pp. 259–291). Reidel: Dordrecht. https://doi.org/10.1007/978-94-009-5349-9_14.
Winther, R. (2006). On the dangers of making scientific models ontologically independent: Taking Richard Levin’s warning seriously. Biology and Philosophy, 21, 703–724.
Ylikoski, P., & Kuorikoski, J. (2010). Dissecting explanatory power. Philosophical Studies, 148(2), 201–219. https://doi.org/10.1007/s11098-008-9324-z.