When philosophy (of science) meets formal methods: a citation analysis of early approaches between research fields

  1. Bonino, Guido
  2. Maffezioli, Paolo
  3. Petrovich, Eugenio
  4. Tripodi, Paolo
Revista:
Synthese

ISSN: 0039-7857,  1573-0964

Año de publicación: 2022

Volumen: 200

Número: 2

Tipo: Artículo

DOI: 10.1007/S11229-022-03484-6 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Synthese

Resumen

The article investigates what happens when philosophy (of science) meets and begins to establish connections with two formal research methods such as game theory and network science. We use citation analysis to identify, among the articles published in Synthese and Philosophy of Science between 1985 and 2021, those that cite the specialistic literature in game theory and network science. Then, we investigate the structure of the two corpora thus identified by bibliographic coupling and divide them into clusters of related papers by automatic community detection. Lastly, we construct by the same bibliometric techniques a reference map of philosophy, on which we overlay our corpora to map the diffusion of game theory and network science in the various sub-areas of recent philosophy. Three main results derive from this study. (i) Philosophers are interested not only in using and investigating game theory as a formal method belonging to applied mathematics and sharing many relevant features with social choice theory, but also in considering its applications in more empirically oriented disciplines such as social psychology, cognitive science, or biology. (ii) Philosophers focus on networks in two research contexts and in two different ways: in the debate on causality and scientific explanation, they consider the results of network science; in social epistemology, they employ network science as a formal tool. (iii) In the reference map, logic—whose use in philosophy dates back to a much earlier period—is distributed in a more uniform way than recently encountered disciplines such as game theory and network science. We conclude by discussing some methodological limitations of our bibliometric approach, especially with reference to the problem of field delineation.

€ Ver financiación

Información de financiación

Financiadores

Referencias bibliográficas

  • Agresti, A. (2007). An introduction to categorical data analysis. John Wiley & Sons.
  • Aumann, R. (1976). Agreeing to disagree. The Annals of Statistics, 4(6), 1236–1239.
  • Axelrod, R. (1984). The evolution of cooperation. Basic Books.
  • Bala, V., & Goyal, S. (1998). Learning from neighbours. Review of Economic Studies, 65(3), 595–621. https://doi.org/10.1111/1467-937X.00059
  • Barabási, A.-L. (2014). Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Basic Books.
  • Barabási, A.-L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and Its Applications, 311(3–4), 590–614. Doi: https://doi.org/10.1016/S0378-4371(02)00736-7.
  • Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509
  • Barry, B. (1965). Political argument. Routledge.
  • Bastian, M., Heyman, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154.
  • Batagelj, V., & Mrvar, A. (2004). Pajek. In M. Jünger & P. Mutzel (Eds.), Graph drawing software (pp. 77–103). Springer.
  • Betti, A., & van den Berg, H. (2014). Modelling the history of ideas. British Journal for the History of Philosophy, 22(4), 812–835.
  • Betti, A. & van der Berg, H. (2016). Towards a computational history of ideas. In Wieneke, L., Jones, C., Düring, M., Armaselu, F. & Leboutte, R. (Eds.), CEUR Workshop Proceedings.
  • Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
  • Bonino, G., Maffezioli, P., & Tripodi, P. (2020). Logic in analytic philosophy: A quantitative analysis. Synthese. https://doi.org/10.1007/s11229-020-02770-5
  • Börner, K., Chen, C., & Boyack, K. W. (2005). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255. https://doi.org/10.1002/aris.1440370106
  • Bornmann, L., Wray, K. B., & Haunschild, R. (2020). Citation concept analysis (CCA): A new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by exemplary case studies including classic books by Thomas S. Kuhn and Karl R. Popper. Scientometrics, 122(2), 1051–1074. https://doi.org/10.1007/s11192-019-03326-2
  • Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404. https://doi.org/10.1002/asi.21419
  • Braithwaite, R. (1955). Theory of games as a tool for the moral philosopher. Cambridge University Press.
  • Buonomo, V., & Petrovich, E. (2018). Reconstructing late analytic philosophy. A quantitative approach. Philosophical Inquiries, 6(1), 152–182. Doi: https://doi.org/10.4454/philinq.v6i1.184.
  • de Bruin, B. (2005). Game theory in philosophy. Topoi, 24(2), 197–208.
  • Chen, C. (2013). Mapping scientific frontiers: The quest for knowledge visualization (2nd ed.). Springer.
  • Crupi, V., & Tentori, K. (2016). Confirmation theory. In A. Hájek & C. Hitchcock (Eds.), Oxford handbook of philosophy and probability (pp. 650–665). Oxford University Press.
  • Davis, J. B. (2010). Individuals and identity in economics. Cambridge University Press.
  • Gauthier, D. (1986). Morals by agreement. Clarendon Press.
  • Hahn, U., Hansen, J. U., & Olsson, E. J. (2020). Truth tracking performance of social networks: How connectivity and clustering can make groups less competent. Synthese, 197(4), 1511–1541. https://doi.org/10.1007/s11229-018-01936-6
  • Hansonn, S. O. (2018). Formalization. In S. O. Hansonn & V. Hendricks (Eds.), Introduction to formal philosophy (pp. 3–61). Springer.
  • Harsanyi, J. (1955). Cardinal welfare, individualistic ethics, and interpersonal comparisons of utility. Journal of Political Economy, 63(4), 309–321.
  • Herfeld, C., & Doehne, M. (2019). The diffusion of scientific innovations: A role typology. Studies in History and Philosophy of Science Part A, 77, 64–80. https://doi.org/10.1016/j.shpsa.2017.12.001
  • Hidalgo, C. A. (2016). Disconnected, fragmented, or united? A trans-disciplinary review of network science. Applied Network Science, 1(6).
  • Horsten, L., & Pettigrew, R. (2011). Mathematical methods in philosophy. In L. Horsten & R. Pettigrew (Eds.), The continuum compendium in philosophical logic (pp. 14–26). Bloomsbury.
  • Huneman, P. (2010). Topological explanations and robustness in biological sciences. Synthese, 177(2), 213–245. https://doi.org/10.1007/s11229-010-9842-z
  • Hyland, K. (1999). Academic attribution: Citation and the construction of disciplinary knowledge. Applied Linguistics, 20(3), 341–367. https://doi.org/10.1093/applin/20.3.341
  • Jaccard, P. (1912). The distribution of the flora in the alpine zone. New Phytologist, 11, 37–50.
  • Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE, 9(6), e98679. https://doi.org/10.1371/journal.pone.0098679
  • Kessler, M. M. (1963). Bibliographic coupling extended in time: Ten case histories. Information Storage and Retrieval, 1(4), 169–187. https://doi.org/10.1016/0020-0271(63)90016-0
  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.
  • Lewis, D. (1969). Convention: A philosophical study. Harvard University Press.
  • Lewis, T. G. (2009). Network science: Theory and practice. John Wiley & Sons.
  • Leydesdorff, L. (2008). On the normalization and visualization of author co-citation data: Salton’s Cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77–85. https://doi.org/10.1002/asi.20732
  • Leydesdorff, L., & Amsterdamska, O. (1990). Dimensions of citation analysis. Science, Technology, and Human Values, 15(3), 305–335. https://doi.org/10.1177/016224399001500303
  • Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25. https://doi.org/10.1086/392759
  • Maltseva, D., & Batagelj, V. (2021). Journals publishing social network analysis. Scientometrics, 126(4), 3593–3620. https://doi.org/10.1007/s11192-021-03889-z
  • Marx, W., Bornmann, L., Barth, A., & Leydesdorff, L. (2014). Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS). Journal of the Association for Information Science and Technology, 65(4), 751–764. https://doi.org/10.1002/asi.23089
  • Merton, R. K. (1988). The Matthew effect in science, II: Cumulative advantage and the symbolism of intellectual property. Isis, 79(299), 606–623.
  • Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1–19. https://doi.org/10.1016/j.ejor.2015.04.002
  • Moretti, F. (2013). Distant reading. Verso.
  • Nash, J. (1951). Non-Cooperative Games. Annals of Mathematics Journal, 54, 286–295.
  • von Neumann, J., & Morgenstern, O. (1944). The theory of games and economic behavior. Princeton University Press.
  • Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. https://doi.org/10.1073/pnas.0601602103
  • Newman, M. E. J. (2018). Networks (2nd ed.). Oxford University Press.
  • Nicolaisen, J. (2007). Citation analysis. Annual Review of Information Science and Technology, 41(1), 609–641. https://doi.org/10.1002/aris.2007.1440410120
  • Noichl, M. (2019). Modeling the structure of recent philosophy. Synthese. https://doi.org/10.1007/s11229-019-02390-8
  • Perea, A. (2012). Epistemic game theory: Reasoning and choice. Cambridge University Press.
  • Pence, C., & Ramsey, G. (2018). How to do digital philosophy of science. Philosophy of Science, 85(5), 930–941. https://doi.org/10.1086/699697
  • Petrovich, E. (2020). Science mapping. ISKO Encyclopedia of Knowledge Organization. https://www.isko.org/cyclo/science_mapping.
  • Rathkopf, C. (2018). Network representation and complex systems. Synthese, 195(1), 55–78. https://doi.org/10.1007/s11229-015-0726-0
  • Rawls, J. (1971). A theory of justice. Harvard University Press.
  • Ross, D. (2019). Game Theory, The Stanford Encyclopedia of Philosophy.
  • Shafir, E., & Tversky, A. (1992). Thinking through uncertainty: Nonconsequential reasoning and choice. Cognitive Psychology, 24(4), 449–474. https://doi.org/10.1016/0010-0285(92)90015-T
  • Skyrms, B. (1996). Evolution of the social contract. Cambridge University Press.
  • Skyrms, B. (2004). The stag hunt and the evolution of social structure. Cambridge University Press.
  • Skyrms, B. (2010). Signals: Evolution, Learning, and Information. Oxford University Press.
  • Small, H. (2003). Paradigms, citations, and maps of science: A personal history. Journal of the American Society for Information Science and Technology, 54(5), 394–399. https://doi.org/10.1002/asi.10225
  • Thor, A., Marx, W., Leydesdorff, L., & Bornmann, L. (2016). Introducing CitedReferencesExplorer (CRExplorer): A program for reference publication year spectroscopy with cited references standardization. Journal of Informetrics, 10(2), 503–515. https://doi.org/10.1016/j.joi.2016.02.005
  • Traag, V., & Franssen, T. (2016). Revealing the quantitative-qualitative divide in sociology using bibliometric visualization. CWTS Blog. https://www.cwts.nl/blog?article=n-q2v294.
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683
  • van Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651. https://doi.org/10.1002/asi.21075
  • van Eck, N. J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. ISSI Newsletter, 7(3), 50–54.
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918
  • Weatherall, J. O., & O’Connor, C. (2020). Conformity in scientific networks. Synthese. https://doi.org/10.1007/s11229-019-02520-2
  • Weatherall, J. O., O’Connor, C., & Bruner, J. P. (2020). How to beat science and influence people: Policymakers and propaganda in epistemic networks. The British Journal for the Philosophy of Science, 71(4), 1157–1186. https://doi.org/10.1093/bjps/axy062
  • Weingart, S. B. (2015). Finding the history and philosophy of wcience. Erkenntnis, 80(1), 201–213. https://doi.org/10.1007/s10670-014-9621-1
  • Zitt, M., Lelu, A., Cadot, M., & Cabanac, G. (2019). Bibliometric Delineation of Scientific Fields. In W. Glänzel, H. F. Moed, U. Schmoch, & M. Thelwall (Eds.), Springer Handbook of Science and Technology Indicators (pp. 25–68). New York: Springer. Doi: https://doi.org/10.1007/978-3-030-02511-3_2.
  • Zollman, K. J. S. (2010). The epistemic benefit of transient diversity. Erkenntnis, 72(1), 17–35.
  • Zollman, K. J. S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587. https://doi.org/10.1086/525605