Abstract
Our best sciences are frequently held to be one way, perhaps the optimal way, to learn about the world’s higher-level ontology and structure. I first argue that which scientific theory is “best” depends in part on our goals or purposes. As a result, it is theoretically possible to have two scientific theories of the same domain, where each theory is best for some (scientifically plausible) goal, but where the two theories posit incompatible ontologies. That is, it is possible for us to have goal-dependent pluralism in our scientific ontologies. This ontological pluralism arises simply from our inability to directly know the world’s objects, rather than any particular claims about our cognitive limits, values, or social structures. I then present two case studies in which this possibility actually occurs—one based on simulations and theoretical analyses of constructed causal systems, and one from actual scientific investigations into the proper ontology for ocean regions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Of course, the “true” commitments of a scientific theory often cannot be determined solely from the mathematical or linguistic expression of the theory, but instead require further analysis. None of what I say in this paper will turn on this particular difficulty, though.
Kitcher (2001) makes a similar argument, but remains open to the possibility that there could be a single, unifying ontology that works best for all goals and purposes.
Thanks to an anonymous reviewer for emphasizing the many different sources of goals, and so pluralism, for Dupré.
I thus concede that my argument may not have much influence on those who believe in a hard-line eliminativism that says every “higher-level” object is necessarily just a mereological sum. I briefly return to this issue in the Conclusion.
I will also focus more narrowly on scientific contexts, largely because the arguments in the next section require a level of specificity that often does not arise in everyday or commonsense reasoning. Both Dupré (1993) and Horst (2007) argue that (their forms of) ontological pluralism arise even for everyday concepts.
To be clear: I think that many of the arguments in this paper apply equally well to both physics and “higher-level” sciences. It is sometimes argued in present-day analytic metaphysics and ontology, however, that investigations into the “fundamental” or “foundational” constituents of nature can proceed through other means (e.g., Sider 2011). I aim to remain relatively agnostic about those debates, and so prudence leads me to not discuss physics in any significant detail.
There are many long-standing philosophical objections to inferring the existence of \(A\)s from the diverse and repeated successes of a theory that uses \(A\)s in some essential manner (e.g., Laudan 1981; van Fraassen 1980). If one embraces those objections, however, then one has already given up on the purely epistemic goal, and so will need no convincing that science can have multiple, ontology-relevant goals.
A different proposal would be to argue that the truth will be best according to some weighted combination of the different evaluation dimensions. This would yield a single evaluation function, and so the danger of pluralism would not rear its head. I do not know of any concrete proposals of this form, however, nor is it clear what grounds could be provided to motivate any particular weighting. At the least, it seems that any proposed weighting would likely exhibit significant context- and domain-dependence. Nonetheless, it remains a possibility that could be explored. Thanks to an anonymous referee for emphasizing this point.
And of course, for each type of prediction, there are many different aspects of \(T\) that we might want to predict, such as its precise value, expected deviation from some norm, variance over time, and so forth. I briefly return to this issue below.
Assuming that various technical conditions hold, such as (i) we have an ideal intervention; (ii) there are no common causes of \(C\) and \(T\); etc.
One might object that the relevant standard should be given observations of R, not some other C. However, we can allow for C to be the trivial functions of R (i.e., C = R), and so include this possibility.
As noted earlier, we can actually have multiple \(\mathbf{C}_{\mathbf{Obs}}\) and \(\mathbf{C}_{\mathbf{Int}}\) sets depending on exactly what we want to predict about \(T\). In the analysis described below, for example, Fancsali (2013) considered multiple criteria for \(\mathbf{C}_{\mathbf{Int}}\), including “predicting post-intervention magnitude of \(T\)” and “predicting post-intervention change in \(T\).” The emergence of incompatible ontologies did not depend on the particular aspect of \(T\) that one wanted to predict.
For example, if we know only that \(C\) and \(T\) are correlated, then we do not know whether \(C \rightarrow T\) or \(C \leftarrow T\) (or perhaps a common cause of the two). We know something about the causal structure—namely, there is some causal connection—but still have some uncertainty.
At least, if we make some general assumptions about how causal relations manifest in observed data. I do not engage here with the debate about the legitimacy of those assumptions (see, e.g., Cartwright 2002, 2007; Glymour 1999), as this example only requires that we can sometimes learn causal structure without experiments. Even opponents of the general project of causal structure learning from observations grant that the assumptions are sometimes acceptable.
Interventions directly on ocean indices are not technically feasible at the current time.
Again, I aim to bracket off the question of exactly what this ontological status is, and in particular, whether these things must be understood to be real objects (in some sense). All I require here is that these things deserve some type or degree of status.
References
Cartwright, N. (1999). The dappled world: A study of the boundaries of science. Cambridge: Cambridge University Press.
Cartwright, N. (2002). Against modularity, the causal Markov condition, and any link between the two: Comments on Hausman and Woodward. The British Journal for the Philosophy of Science, 53, 411–453.
Cartwright, N. (2007). Hunting causes and using them: Approaches in philosophy and economics. Cambridge: Cambridge University Press.
Chakravartty, A. (2013). On the prospects of naturalized metaphysics. In D. Ross, J. Ladyman, & H. Kincaid (Eds.), Scientific metaphysics. Oxford: Oxford University Press.
Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507–554.
Chu, T., & Glymour, C. (2008). Search for additive time series causal models. Journal of Machine Learning Research, 9, 967–991.
Danks, D. (2014). Unifying the mind: Cognitive representations as graphical models. Cambridge: The MIT Press.
Dennett, D. C. (1987). The intentional stance. Cambridge: The MIT Press.
Dupré, J. (1993). The disorder of things: Metaphysical foundations of the disunity of science. Cambridge: Harvard University Press.
Dupré, J. (2012). Processes of life: Essays in the philosophy of biology. Oxford: Oxford University Press.
Fancsali, S. E. (2013). Constructing variables that support causal inference. (Doctoral dissertation). Pittsburgh: Carnegie Mellon University, Department of Philosophy.
Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1–58.
Giere, R. N. (1999). Science without laws. Chicago: University of Chicago Press.
Glantz, M. H., Katz, R. W., & Nicholls, N. (Eds.). (1991). Teleconnections linking worldwide climate anomalies. Cambridge: Cambridge University Press.
Glymour, C. (1999). Rabbit hunting. Synthese, 121, 55–78.
Glymour, C. (2007). When is a brain like the planet? Philosophy of Science, 74, 330–347.
Hausman, D. M. (1998). Causal asymmetries. Cambridge: Cambridge University Press.
Horst, S. (2007). Beyond reduction: Philosophy of mind and post-reductionist philosophy of science. New York: Oxford University Press.
Horst, S. (2011). Laws, mind, and free will. Cambridge: The MIT Press.
Kellert, S. H., Longino, H. E., & Waters, C. K. (Eds.). (2006). Scientific pluralism. Minneapolis: University of Minnesota Press.
Kitcher, P. (2001). Science, truth, and democracy. New York: Oxford University Press.
Laudan, L. (1981). A confutation of convergent realism. Philosophy of Science, 48(1), 19–49.
Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.
Putnam, H. (1975). Philosophy and our mental life. Mind, language, and reality: Philosophical papers (pp. 291–303). Cambridge: Cambridge University Press.
Ropelewski, C. F., & Halpert, M. S. (1996). Quantifying southern oscillation-precipitation relationships. Journal of Climate, 9, 1043–1059.
Sider, T. (2011). Writing the book of the world. Oxford: Oxford University Press.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., et al. (Eds.). (2007). Climate change 2007: The physical science basis: Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.
Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search (1st ed.). Berlin: Springer.
Steinbach, M., Tan, P.-N., Kumar, V., Klooster, S., & Potter, C. (2003). Discovery of climate indices using clustering. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 446–455). New York: ACM.
van Fraassen, B. C. (1980). The scientific image. Oxford: Oxford University Press.
Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.
Wellen, S., & Danks, D. (2014). Learning with a purpose: The influence of goals. In P. Bello, M. Guarini, M. McShane & B. Scassellati (Eds.), Proceedings of the 36th annual conference of the cognitive science society (pp. 1766–1771). Austin, TX: Cognitive Science Society.
Wilson, M. (2006). Wandering significance: An essay on conceptual behavior. Oxford: Oxford University Press.
Wilson, R. A. (1996). Promiscuous realism. British Journal for the Philosophy of Science, 47, 303–316.
Wilson, R. A. (2008). Material constitution and the Many-Many Problem. Canadian Journal of Philosophy, 38(2), 201–218.
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.
Acknowledgments
Thanks to participants at the 2013 Ontology & Methodology conference at Virginia Tech, as well as three anonymous reviewers for this journal, for their valuable comments, feedback, and criticisms. This work was partially supported by a James S. McDonnell Foundation Scholar Award.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Danks, D. Goal-dependence in (scientific) ontology. Synthese 192, 3601–3616 (2015). https://doi.org/10.1007/s11229-014-0649-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-014-0649-1