Abstract
Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Robustness analysis is often used by scientific realists to argue for particular claims being true or certain entities as existing and bears a passing resemblance to the “no miracle argument”. However, generally, robustness analysis is different than the no miracles argument. Robustness concerns the following conditional probability, Pr(O/M 1&M 2&···&M n ), and the no miracles argument concerns the following conditional probability, Pr(M/O 1&O 2&···&O n ). where O i are predictions and M i are models (or theories).
It is not clear that these types of assumptions are that different. For example, a causal assumption that some factor C causes E assumes certain intervening factors are absent. But we leave worries like these to the side.
Suppose Pr(O/C&A 1) ≠ Pr(O/C&A 2) where O is some prediction, C is the common “substantial” core, and the A i s are different idealized assumptions. It follows that [Pr(O)Pr(C&A 1/O)]/Pr(C&A 1) ≠ [Pr(O)Pr(C&A 2/O)]/Pr(C&A 2). Thus, Pr(C&A 1) ≠ Pr(C&A 2). However, this is consistent with Pr(A 1/A 2) ≠ Pr(A 1).
Our views should not be construed as skepticism regarding causal claims. For example, we have reason to believe the Volterra Principle is a true causal claim when we can intervene in a predator–prey system and produce the intended effect. For example, all things being equal, if we instantiate the antecedent and the consequent is observed that is reason to believe the Volterra Principle describes a causal relationship. However, if we do this, robustness analysis plays no role in confirmation.
A common response to this sort of argument is that we employ a notion of approximate truth and suggest that say A j is closer to the truth than A i . However, as far as we are aware, no such account of approximate truth is successful. And in any case, many of the false assumptions in question are not even close to truth on any understanding of approximate truth.
Julian Reiss has recently made this point forcefully for economics (Reiss 2008 118–119).
That is, given his prior probability with regard to those idealizations was zero, then regardless of the likelihoods, the posterior probability must be zero.
There is also a tradition in the semantic view of theories for regarding models as providing theoretical “definitions” or set-theoretical predicates. Thus, syntactically or semantically, we can separate the model from the model’s application.
This implies that we also reject the view that models state capacity claims (Cartwright 1999) or make claims about credible worlds (Sugden 2000, 2009). For reasons to reject these views see Cartwright (2009), Gruene-Yanoff (2009), Alexandrova (2008), Alexandrova and Northcott (2009), and Odenbaugh (2006).
See Al Roth, the father of design economics (among many others, of course) on the complex mixture of methods that goes into successful applied economics (Roth 2002).
“Natural experiments” are important in both economics and biology. Here one finds to situations such that some purported causal factor is present in the former and not the latter and where everything else is roughly the same. The problem with these so-called experiments is that rarely are they even roughly the same. Still, they can be important.
References
Alexandrova A (2006) Connecting rational choice models to the real world. Phil Soc Sci 36(2):173–192
Alexandrova A (2008) Making models count. Phil Sci 75:383–404
Alexandrova A, Northcott R (2009) Progress in economics: lessons from the Spectrum Auctions. In: Kincaid H, Ross D (eds) Oxford handbook of philosophy of economics. Oxford University Press, Oxford, pp 306–336
Aydinonat NE (2008) The invisible hand in economics: how economists explain unintended social consequences. Routledge, London
Brandon R (1990) Adaptation and environment. Princeton University Press, Princeton
Cartwright N (1989) Nature’s capacities and their measurement. Oxford University Press, Oxford
Cartwright N (1991) Replicability, reproducibility, and robustness—comments on Harry Collins. Hist Polit Econ 23:143–155
Cartwright N (1999) The dappled world. Cambridge University Press, Cambridge
Cartwright N (2009) If no capacities, then no credible worlds: but can models reveal capacities? Erkenntnis 70:45–58
Da Costa N, French S (2003) Science and partial truth: a unitary approach to models and reasoning in science. Oxford University Press, New York
Forber P (forthcoming) Confirmation and explaining how possible. Stud Hist Phil Sci C Stud Hist Phil Biol Biomed Sci
Giere RN (1988) Explaining science. University of Chicago Press, Chicago
Gruene-Yanoff Till (2009) Learning from minimal economic models. Erkenntnis 70:81–99
Hamminga B (1983) Neoclassical theory structure and theory development. Springer, Berlin
Hausman DM (1992) The Inexact and separate science of economics. Cambridge University Press, Cambridge
Hindriks FA (2006) Tractability assumptions and the Musgrave–Mäki typology. J Econ Methodol 13:401–423
Horn H (1975) Markovian properties of forest succession. In: Cody ML, Diamond J (eds) Ecology and evolution of communities). Belknap Press, Cambridge, pp 196–211
Humphreys P (2002) Computational models. Philosophy of Science Supplement: Proceedings of the 2000 Biennial Meeting of the PSA. Part II: Symposia Papers, 69/3: S1–S11
Klemperer P (1999) Auction theory: a guide to the literature. J Econ Surv 13(3):227–286
Klemperer P (2002) What really matters in auction design. J Econ Lit 16(1):169–189
Kuorikoski J, Lehtinen A, Marchionni C (2010) Economic modelling as robustness analysis. Brit J Phil Sci 23:5–7. doi:10.1093/bjps/axp049
Levins R (1968) Evolution in changing environments: some theoretical explorations. Princeton University Press, Princeton
Lloyd E (1994) The structure and confirmation of evolutionary theory. Princeton University Press, Princeton
Maki U (1992) “On the method of isolation in economics. Poznan Stud Phil Sci Humanit 26:319–354
Maki U (1998) Aspects of realism about economics. Theoria 13:301–319
McMullin E (1985) Galilean idealization. Stud Hist Philos Sci 16(3):247–273
Morgenbesser S (1956) Theories and schematas in social sciences. PhD Dissertation, University of Pennsylvania
Musgrave A (1981) Unreal assumptions in economic theory: the F-twist untwisted. Kyklos 34:377–387
Odenbaugh J (2005) Idealized, inaccurate, and successful: a pragmatic approach to evaluating models in theoretical ecology. Biol Philos 20:231–255
Odenbaugh J (2006) Struggling with the sciences of ecology. Biol Philos 21:395–409
Orzack SH, Sober E (1993) A critical assessment of Levins’s The Strategy of Model Building in Population Biology (1966). Q Rev Biol 68:533–546
Reiss J (2008) Error in economics: the methodology of evidence based economics. Routledge, London
Roth A (2002) The economist as engineer: game theory, experimental economics and computation as tools of design economics. Econometrica 70(4):1341–1378
Salmon W (1984) Scientific explanation and the causal structure of the world. Princeton University Press, Princeton
Sugden R (2000) Credible worlds: the status of theoretical models in economics. J Econ Methodol 7(1):1–31
Sugden R (2009) Credible worlds, capacities and mechanisms. Erkenntnis 70:3–27
Van Fraassen BC (1980) The scientific image. Oxford University Press, Oxford
Weisberg M (2006) Robustness analysis. Phil Sci 73:730–742
Weisberg M, Reisman K (2008) The robust Volterra principle. Phil Sci 75:106–131
Wimsatt WC (1981) Robustness, reliability and overdetermination. In: Brewer MB, Collins BE (eds) Scientific inquiry and the social sciences. Jossey-Bass, San Francisco, pp 124–163
Wimsatt W (1987) False models as a means to truer theories. In: Nitecki M, Hoffman A (eds) Neutral models in biology. Oxford University Press, London, pp 23–55
Woodward J (2006) Some varieties of robustness. J Econ Methodol 13(2):219–240
Acknowledgments
Authors would like to thank Michael Weisberg, Robert Northcott, Nancy Cartwright, Uskali Maki and two anonymous referees for useful comments on the paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
The authors are jointly and equally responsible for the content of the paper.
Rights and permissions
About this article
Cite this article
Odenbaugh, J., Alexandrova, A. Buyer beware: robustness analyses in economics and biology. Biol Philos 26, 757–771 (2011). https://doi.org/10.1007/s10539-011-9278-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10539-011-9278-y