The aim of this paper is to develop ideas about robustness analyses. I introduce a form of robustness analysis that I call sufficient parameter robustness, which has been neglected in the literature. I claim that sufficient parameter robustness is different from derivational robustness, the focus of previous research. My purpose is not only to suggest a new taxonomy of robustness, but also to argue that previous authors have concentrated on a narrow sense of robustness analysis, which they have inadequately distinguished from other investigations of models such as sensitivity analysis.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Sensitivity analysis is a term with different meanings. My definition is not intended to correspond to the activity in which the modeler investigates how the uncertainty in the input of a model could affect the uncertainty of its output, despite the fact that the latter is sometimes referred to as sensitivity analysis. Moreover, by sensitivity analysis I do not refer to perturbation analysis, which is a method to solve equations.
According to the latitudinal diversity gradient, the number of species within a taxonomic group tends to increase with decreasing latitudes, i.e., diversity increases towards the tropics and decreases towards the poles.
Broken up or violated is meant in the sense that there will be exceptions to the model’s predictions and/or the model exhibits different dynamic behavior(s) over some range of parameter values.
The population growth of certain biological systems is discrete rather than continuous over time, and hence the reason we use a discrete model to model their population growth is adopted for substantial reasons rather than tractability reasons. One and the same assumption or “mathematical structure” can thus be used either as a substantial or tractability assumption, and its status as either depends on the contextual and pragmatic aspects of modeling. In other words, the status of a modeling assumption cannot be evaluated in isolation. At the same time, when the contextual and pragmatic aspects of modeling are fixed, the status of modeling assumptions can be compared as to whether they introduced mainly to “facilitate modeling” or whether they represent the “core aspects” of a model or a set of similar models.
I do not claim that the presented taxonomy of modeling assumptions is exhaustive. In fact, with a more detailed taxonomy of modeling assumptions (Musgrave 1981; Mäki 2000) we could arrive at a more detailed taxonomy of robustness analyses of models. Moreover, Kuorikoski et al. (2010) distinguish a third type of modeling assumption, namely, Galilean assumptions. Galilean assumptions serve to isolate certain entities, factors, and so on from the involvement of everything else (Mäki 1992, 1994). Rather than thinking of Galilean assumptions as an independent kind of modeling assumption, we should understand these as stating that the aim of scientific theorizing and modeling is isolation, whereas tractability and substantial assumptions are devices for accomplishing isolations.
Lotka–Volterra models have been criticized as being poorly tested, giving inaccurate predictions, and lacking explanatory power (Smith 1952; Pielou 1981; Hall 1988; Shrader-Frechette 1990). Even if we neglect the fact that models have functions other than furnishing explanations and giving predictions (Pielou 1981; Odenbaugh 2005), the above is not damaging to my position. The criticism is typically directed at the Lotka–Volterra predation model. The Lotka–Volterra competition model, which I use as an example, is on firmer ground with regard to the above. The Lotka–Volterra competition model’s representational accuracy and its ability to save the phenomena are not at issue here, however. It suffices to show that the equations in the Lotka–Volterra competition model represent a modular system of invariant equations; the fact is that the Lotka–Volterra competition model remains invariant during variations in the values of its “independent” variables (N 1 or N 2), at least for certain parameter values. This shows that the model gives us at least potentially valid causal or mechanistic explanations, that is, the Lotka–Volterra competition model is an explanatory model rather than a phenomenological model devoid of any explanatory power.
In fact, Koch (1974a) demonstrated that the co-existence of two competitors with one resource in a seasonally varying environment is a robust result of competition models, which included substantial modeling assumptions different from the classical competition model.
Some of the assumptions mentioned are Galilean assumptions. However, the point is that these assumptions were incorporated into the model to make it more tractable.
The only recent paper I know of that discusses sufficient parameters in detail is Winther (2006). His ideas have developed from a different angle than mine.
The above is, in crude terms, the explanation that Hutchinson (1961) gave to his paradox of the plankton referred to above.
Nothing in the above presupposes that for genuine or true explanations the identification of the actual cause or mechanism has become redundant, owing to sufficient parameter robustness of the results of models. Abstract causal surrogates—sufficient parameters—should not be mistaken for the actual causes and mechanisms of phenomena. To do otherwise is to commit oneself to a reification fallacy. What has just been said is one of the main motivations behind mechanistic or resource-based competition models as well (Tilman 1980; Pacala and Tilman 1994; Leibold 1995).
Armstrong, R. A., & McGehee, R. (1980). Competitive exclusion. American Naturalist, 115, 151–170.
Calcott, B. (2011). Wimsatt and the robustness family: Review of Wimsatt’s re-engineering philosophy for limited beings. Biology and Philosophy, 26, 281–293.
Chesson, P. L. (2000). Mechanisms of maintenance of species diversity. Annual Review of Ecology and Systematics, 31, 343–366.
Chesson, P. L., & Case, T. J. (1986). Overview: Nonequilibrium community theories: Chance, variability, history, and coexistence. In J. Diamond & T. J. Case (Eds.), Community Ecology (pp. 229–239). New York: Harper & Row.
Chesson, P. L., & Huntly, N. (1997). The roles of harsh and fluctuating conditions in the dynamics of ecological conditions. American Naturalist, 150, 519–553.
Connor, E. F., & McCoy, E. D. (1979). The statistics and biology of the species–area relationship. American Naturalist, 113, 791–833.
Gibbard, A., & Varian, H. R. (1978). Economic models. Journal of Philosophy, 75, 664–677.
Hall, C. A. (1988). An assessment of several of the historically most influential theoretical models used in ecology and of the data provided in their support. Ecological Modelling, 43, 5–31.
Hutchinson, G. E. (1961). The paradox of the plankton. American Naturalist, 95, 137–145.
Koch, A. L. (1974a). Coexistence resulting from an alteration of density dependent and density independent growth. Journal of Theoretical Biology, 44, 373–386.
Koch, A. L. (1974b). Competitive coexistence of two predators utilizing the same prey under constant environmental conditions. Journal of Theoretical Biology, 44, 387–395.
Kuorikoski, J., & Lehtinen, A. (2009). Incredible world, credible results. Erkenntnis, 70, 119–131.
Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2010). Economic modelling as robustness analysis. British Journal for Philosophy of Science, 61, 541–567.
Lane, P. A., Lauff, G. H., & Levins, R. (1976). The feasibility of using a holistic approach in ecosystem analysis. In S. A. 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.
Lawton, J. H. (1996). Patterns in ecology. Oikos, 75, 145–147.
Lawton, J. H. (1999). Are there general laws in ecology? Oikos, 84, 177–192.
Leibold, M. A. (1995). The Niche concept revisited: Mechanistic models and community context. Ecology, 76, 1371–1382.
Levin, S. A. (1991). The problem of relevant detail. In S. N. Busenberg & M. Martelli (Eds.), Differential Equations Models in Biology, Epidemiology and Ecology: Proceedings Held in Clarement, California, January 13–16, 1990 (pp. 9–15). New York: Springer.
Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73, 1943–1967.
Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54, 421–431.
Levins, R. (1993). A response to Orzack and Sober: Formal analysis and the fluidity of science. Quarterly Review of Biology, 68, 547–555.
Levins, R. (1998). The internal and external in explanatory theories. Science as Culture, 7, 557–582.
Levins, R. (2006). Strategies of abstraction. Biology and Philosophy, 21, 741–755.
Lloyd, E. A. (2010). Confirmation and robustness of climate models. Philosophy of Science, 77, 971–984.
Mäki, U. (1992). On the method of isolation in economics. In C. Dilworth (Ed.), Idealization IV: Intelligibility in science. Poznan studies in the philosophy of sciences and the humanities (Vol. 26, pp. 317–351). Amsterdam: Rodopi.
Mäki, U. (1994). Isolation, idealization and truth in economics. In B. Hamminga & N. B. De Marchi (Eds.), Idealization in economics. Poznan studies in the philosophy of sciences and the humanities (Vol. 38, pp. 147–168). Amsterdam: Rodopi.
Mäki, U. (2000). Kinds of assumptions and their truth: Shaking an untwisted f-twist. Kyklos, 53, 317–336.
May, R. M. (1974). Biological populations with nonoverlapping generations: Stable points, stable cycles, and chaos. Science, 186, 645–647.
May, R. M. (1976). Simple mathematical models with very complicated dynamics. Nature, 261, 459–467.
Musgrave, A. (1981). Unreal assumptions’ in economic theory: The F-twist untwisted. Kyklos, 34, 377–387.
Odenbaugh, J. (2003). Complex systems, trade-offs, and theoretical population biology: Richard Levin’s “strategy of model building in population biology” revisited. Philosophy of Science, 70, 1496–1507.
Odenbaugh, J. (2005). Idealized, inaccurate but successful: A pragmatic approach to evaluating models in theoretical ecology. Biology and Philosophy, 20, 231–235.
Odenbaugh, J. (2006). The strategy of the strategy of model building in population biology. Biology and Philosophy, 21, 607–621.
Orzack, S. H., & Sober, E. (1993). A critical assessment of Levins’s the strategy of model building in population biology (1966). Quarterly Review of Biology, 68, 533–546.
Pacala, S. W., & Tilman, D. (1994). Limiting similarity in mechanistic and spatial model of plant competition in heterogeneous environment. American Naturalist, 143, 362–393.
Peters, R. H. (1991). A critique of ecology. Cambridge: Cambridge University Press.
Pielou, E. C. (1981). The usefulness of ecological models: A stock-taking. Quarterly Review of Biology, 56, 17–31.
Preston, F. W. (1962a). The canonical distribution of commonness and rarity: Part I. Ecology, 43, 185–215.
Preston, F. W. (1962b). The Canonical distribution of commonness and rarity: Part II. Ecology, 43, 410–432.
Raerinne, J. P. (2011). Causal and mechanistic explanations in ecology. Acta Biotheoretica, 59, 251–271.
Rastetter, E. B., King, A. W., Cosby, B. J., Hornberger, G. M., O’Neill, R. V., & Hobbie, J. E. (1992). Aggregating fine-scale ecological knowledge to model coarser-scale attributes to ecosystems. Ecological Applications, 2, 55–70.
Schoener, T. W. (1982). Mathematical ecology and its place among the sciences. Science, 178, 389–391.
Shrader-Frechette, K. (1990). Interspecific competition, evolutionary epistemology, and ecology. In N. Rescher (Ed.), Evolution, cognition, and realism (pp. 47–61). Lanham: University Press of America.
Simberloff, D. S. (1976). Experimental zoogeography of islands: Effects of island size. Ecology, 57, 629–648.
Smith, F. E. (1952). Experimental methods in population dynamics: A critique. Ecology, 33, 441–450.
Strobeck, C. (1973). N Species Competition. Ecology, 54, 650–654.
Tilman, D. (1980). A graphical-mechanistic approach to competition and predation. American Naturalist, 116, 362–393.
Vellend, M. (2010). Conceptual synhtesis in community ecology. Quarterly Review of Biology, 82, 183–206.
Weisberg, M. (2006a). Forty years of “the strategy”: Levins on model building and idealization. Biology and Philosophy, 21, 623–645.
Weisberg, M. (2006b). Robustness analysis. Philosophy of Science, 73, 730–742.
Weisberg, M., & Reisman, K. (2008). The Robust Volterra principle. Philosophy of Science, 75, 106–131.
Wimsatt, W. C. (1979). Reduction and reductionism. In P. D. Asquith & H. E. Kyburg (Eds.), Current research in philosophy of science—proceedings of the P.S.A. Critical research problems conference (pp. 352–337). East Lansing: Philosophy of Science Association.
Wimsatt, W. C. (1980). Randomness and perceived-randomness in evolutionary biology. Synthese, 43, 287–339.
Wimsatt, W. C. (1981). Robustness, reliability, and overdetermination. In M. B. Brewer & B. Collins (Eds.), Scientific inquiry and the social sciences—A volume in Honor of Donald T. Campbell (pp. 124–163)., Honor of Donald T. Campbell San Francisco: Jossey-Bass.
Winther, R. G. (2006). On the dangers of making scientific models ontologically independent: Taking Richard Levin’s warning seriously. Biology and Philosophy, 21, 703–724.
Woodward, J. (2002). What is a mechanism? A counterfactual account. Philosophy of Science, 69, S366–S377.
Woodward, J. (2006). Some varieties of robustness. Journal of Economic Methodology, 13, 219–240.
Ylikoski, P., & Kuorikoski, J. (2010). Dissecting explanatory power. Philosophical Studies, 148, 201–219.
A version of this paper was presented at the Philosophy of Science Group seminar 17 November 2008 at the University of Helsinki. This research was supported financially by the Finnish Cultural Foundation and the Academy of Finland as a part of the project Modeling Mechanisms (project number 112 2818). I am grateful to the members of the Philosophy of Science Group in Helsinki, especially Aki Lehtinen and Petri Ylikoski, and an anonymous referee for this journal who provided helpful comments on earlier drafts of this paper.
About this article
Cite this article
Raerinne, J. Robustness and sensitivity of biological models. Philos Stud 166, 285–303 (2013). https://doi.org/10.1007/s11098-012-0040-3
- Scientific models
- Sufficient parameters