Abstract
We ask how to use machine learning to expand observability, which presently depends on human learning that informs conceivability. The issue is engaged by considering the question of correspondence between conceived observability counterfactuals and observable, yet so far unobserved or unconceived, states of affairs. A possible answer lies in importing out of reference frame content which could provide means for conceiving further observability counterfactuals. They allow us to define high-fidelity observability, increasing the level of correspondence in question. To achieve high-fidelity observability, we propose to use generative machine learning models as the providers of the out of reference frame content. From an applied point of view, such a role of generative machine learning models shows an emerging dimension of human-machine cooperation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bueno O (1997) Empirical adequacy: A partial structures approach. Stud History Philos Sci Part A 28(4):585–610
Campbell D, Copeland J, Deng Z-R (2017) The Inconceivable Popularity of Conceivability Arguments. Philosophical Q 67(267):223–240
Chakravartty A (2017) Reflections on new thinking about scientific realism. Synthese 194(9):3379–3392
Chalmers D, J (2002) Does conceivability entail possibility? In: Gendler T, Hawthorne J (eds) Conceivability and Possibility. Oxford University Press, New York, pp 145–200
Chong S, Tino P, Yao X (2008) Measuring Generalization Performance in Coevolutionary Learning. IEEE Trans Evol Comput 12(4):479–505
Chong S, Tino P, Yao X (2009) Relationship Between Generalization and Diversity in Coevolutionary Learning. IEEE Trans Comput Intell AI Games 1(3):214–232
Chong S, Tino P, Ku D, Yao X (2012) Improving Generalization Performance in Co-Evolutionary Learning. IEEE Trans Evol Comput 16(1):70–85
Darwen P, Yao X (1995) On Evolving Robust Strategies for Iterated Prisoner’s Dilemma. In: Yao X (ed) Progress in Evolutionary Computation. AI ’93 and AI ’94 Workshops on Evolutionary Computation Melbourne, Victoria, Australia, November 16, 1993 Armidale, NSW, Australia, November 21–22, 1994 Selected Papers. Springer, Berlin
Dawkins R, Krebs J (1979) R. Arms races between and within species. Proceedings of the Royal Society of London. Series B, Biological Sciences, 205(1161), 489–511
Dicken P (2007) Constructive Empiricism and the Metaphysics of Modality. Br J Philos Sci 58(3):605–612
Dicken P (2009) Constructive Empiricism and the Vices of Voluntarism. Int J Philosophical Stud 17:189–201
Fiocco M, O (2020) The epistemic idleness of conceivability. In: Bueno O, Shalkowski SA (eds) The Routledge Handbook of Modality. Routledge, London
Gendler T, Hawthorne J (2002) Conceivability and Possibility. Oxford University Press, New York
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative Adversarial Networks. In Proceedings of 27th Advances in Neural Information Processing Systems (NIPS), December 8–13 Montreal, Canada
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. The MIT Press, Cambridge, MA
Hill C, S (1997) Imaginability, Conceivability, Possibility and the Mind-Body Problem. Philos Stud 87(1):61–85
Hitchcock C, Woodward J (2003) Explanatory Generalizations, Part II: Plumbing Explanatory Depth. Noûs 37(2), 181–199
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366
Humphreys P (2004) Extending ourselves: computational science, empiricism, and scientific method. Oxford University Press, New York, NY
Kung P (2010) Imagining as a Guide to Possibility. Philos Phenomenol Res 81(3):620–663
Ladyman J (2000) What’s Really Wrong with Constructive Empiricism? Van Fraassen and the Metaphysics of Modality. Br J Philos Sci 51(4):837–856
Ladyman J (2004) Constructive Empiricism and Modal Metaphysics: A Reply to Monton and van Fraassen. Br J Philos Sci 55(4):755–765
Leonelli S (2020) Scientific Research and Big Data. The Stanford Encyclopedia of Philosophy (Summer 2020 Edition). In E. N. Zalta (ed.) https://plato.stanford.edu/archives/sum2020/entries/science-big-data/
Monton B, van Fraassen BC (2003) Constructive Empiricism and Modal Nominalism. The British Journal for the Philosophy of Science, 54(3), 405–422
Muller FA (2005) The Deep Black Sea: Observability and Modality Afloat. Br J Philos Sci 56(1):61–99
Olsson C, Bhupatiraju S, Brown T, Odena A, Goodfellow I (2018) Skill Rating for Generative Models, arXiv:1808.04888v1 [stat.ML].
Putnam H (1975) Mathematics, Matter and Method (Philosophical Papers, Vol. 1). Cambridge University Press, Cambridge
Rescher N (2020) Knowledge at the Boundaries. Springer, Cham
Reutlinger A (2016) Is There a Monist Theory of Causal and Non-Causal Explanations? The Counterfactual Theory of Scientific Explanation. Philos Sci 83(5):733–745
Reutlinger A (2018) Extending the Counterfactual Theory of Explanation. In: Reutlinger A, Saatsi J (eds) Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations. Oxford University Press, New York, pp 74–95
Saatsi J, Pexton M (2012) Reassessing Woodward’s Account of Explanation: Regularities, Counterfactuals, and Noncausal Explanations. Philos Sci 80(5):613–624
Spelda P, Stritecky V (2021) What Can Artificial Intelligence Do for Scientific Realism? Axiomathes 31, 85–104
Tidman P (1994) Conceivability as a Test for Possibility. Am Philos Q 31(4):297–309
Van Valen L (1973) A New Evolutionary Law. Evolutionary Theory 1:1–30
Wang C, Xu C, Yao X, Tao D (2018) Evolutionary Generative Adversarial Networks, arXiv:1803.00657v1 [cs.LG].
Wilks Y (2017) Will There Be Superintelligence and Would It Hate Us? AI Magazine 38(4):65–70
Woodward J (2000) Explanation and invariance in the special sciences. Br J Philos Sci 51(2):197–254
Woodward J (2003) Making Things Happen: A Theory of Causal Explanation. Oxford University Press, New York
Woodward J (2018) Some Varieties of Non-Causal Explanation. In: Reutlinger A, Saatsi J (eds) Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations. Oxford University Press, New York, pp 117–140
Yablo S (1993) Is Conceivability a Guide to Possibility? Philos Phenomenol Res 53(1):1–42
Yao X, Darwen P (1995) An Experimental Study of N-Person Iterated Prisoner’s Dilemma Games. In: Yao X (ed) Progress in Evolutionary Computation. AI ’93 and AI ’94 Workshops on Evolutionary Computation Melbourne, Victoria, Australia, November 16, 1993 Armidale, NSW, Australia, November 21–22, 1994 Selected Papers. Springer, Berlin
Yao X (1997) Automatic Acquisition of Strategies by Co-evolutionary Learning. In Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA ’97)
Acknowledgements
This output was supported by the NPO Systemic Risk Institute LX22NPO5101.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Spelda, P., Stritecky, V. Expanding Observability via Human-Machine Cooperation. Axiomathes 32 (Suppl 3), 819–832 (2022). https://doi.org/10.1007/s10516-022-09636-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10516-022-09636-0