Theory-choice, transient diversity and the efficiency of scientific inquiry

  • AnneMarie Borg
  • Daniel Frey
  • Dunja ŠešeljaEmail author
  • Christian Straßer
Paper in Formal Methods and Exact Sciences
Part of the following topical collections:
  1. EPSA17: Selected papers from the biannual conference in Exeter


Recent studies of scientific interaction based on agent-based models (ABMs) suggest that a crucial factor conducive to efficient inquiry is what Zollman (2010) has dubbed ‘transient diversity’. It signifies a process in which a community engages in parallel exploration of rivaling theories lasting sufficiently long for the community to identify the best theory and to converge on it. But what exactly generates transient diversity? And is transient diversity a decisive factor when it comes to the efficiency of inquiry? In this paper we examine the impact of different conditions on the efficiency of inquiry, as well as the relation between diversity and efficiency. This includes certain diversity-generating mechanisms previously proposed in the literature (such as different social networks and cautious decision-making), as well as some factors that have so far been neglected (such as evaluations underlying theory-choice performed by scientists). This study is obtained via an argumentation-based ABM (Borg et al. 2017, 2018). Our results suggest that cautious decision-making does not always have a significant impact on the efficiency of inquiry while different evaluations underlying theory-choice and different social networks do. Moreover, we find a correlation between diversity and a successful performance of agents only under specific conditions, which indicates that transient diversity is sometimes not the primary factor responsible for efficiency. Altogether, when comparing our results to those obtained by structurally different ABMs based on Zollman’s work, the impact of specific factors on efficiency of inquiry, as well as the role of transient diversity in achieving efficiency, appear to be highly dependent on the underlying model.


Agent-based modeling Cautious decision-making Theory-choice Transient diversity Scientific inquiry Scientific interaction 



We are grateful to two anonymous reviewers for valuable comments on the previous draft of this paper.

The research by AnneMarie Borg and Christian Straßer is supported by a Sofja Kovalevskaja award of the Alexander von Humboldt Foundation and by the German Ministry for Education and Research.

The research of Dunja Šešelja is supported by the DFG (Research Grant HA 3000/9-1).

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  1. Alexander, J.M. (2013). Preferential attachment and the search for successful theories. Philosophy of Science, 80(5), 769–782.CrossRefGoogle Scholar
  2. Alexander, J.M., Himmelreich, J., Thompson, C. (2015). Epistemic landscapes, optimal search, and the division of cognitive labor. Philosophy of Science, 82(3), 424–453.CrossRefGoogle Scholar
  3. Borg, A.M., Frey, D., Šešelja, D., Straßer, C. (2017). Examining network effects in an argumentative agent-based model of scientific inquiry. In Baltag, A., Seligman, J., Yamada, T. (Eds.) Proceedings Logic, rationality, and interaction: 6th international workshop, LORI 2017, Sapporo, Japan, September 11-14, 2017 (pp. 391–406). Berlin: Springer Berlin Heidelberg.Google Scholar
  4. Borg, A.M., Frey, D., Šešelja, D., Straßer, C. (2018). Epistemic effects of scientific interaction: approaching the question with an argumentative agent-based model. Historical Social Research, 43(1), 285–309.Google Scholar
  5. Borg, A.M., Frey, D., Šešelja, D., Straßer, C. (2019). Using agent-based models to explain past scientific episodes: towards robust fndings. Forthcoming.Google Scholar
  6. Currie, A., & Avin, S. (2018). Method pluralism, method mismatch and method bias. Philosopher’s Imprint.Google Scholar
  7. Dung, P.M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial intelligence, 77, 321–358.CrossRefGoogle Scholar
  8. Frey, D., & Šešelja, D. (2018a). Robustness and idealization in agent-based models of scientific interaction. British Journal for the Philosophy of Science.
  9. Frey, D., & Šešelja, D. (2018b). What is the epistemic function of highly idealized agent-based models of scientific inquiry? Philosophy of the Social Sciences.
  10. Grim, P. (2009). Threshold phenomena in epistemic networks. In AAAI fall symposium: complex adaptive systems and the threshold effect (pp. 53–60).Google Scholar
  11. Grim, P., Singer, D.J., Fisher, S., Bramson, A., Berger, W.J., Reade, C., Flocken, C., Sales, A. (2013). Scientific networks on data landscapes: question difficulty, epistemic success, and convergence. Episteme, 10(4), 441–464.CrossRefGoogle Scholar
  12. Kelp, C., & Douven, I. (2012). Sustaining a rational disagreement. EPSA philosophy of science: Amsterdam 2009 101–110.Google Scholar
  13. Kuhn, T. (1962). Structure of scientific revolutions, 3rd edition. Chicago: The University of Chicago Press.Google Scholar
  14. Kummerfeld, E., & Zollman, K.J.S. (2016). Conservatism and the scientific state of nature. The British Journal for the Philosophy of Science, 67(4), 1057–1076.CrossRefGoogle Scholar
  15. Lakatos, I. (1978). The methodology of scientific research programmes. Philosophical papers. Volume I, Editors: John Worrall and Gregory Currie. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  16. Laudan, L. (1977). Progress and its problems: towards a theory of scientific growth. London: Routledge and Kegan Paul Ltd.Google Scholar
  17. Muldoon, R. (2017). Diversity, rationality and the division of cognitive labor. In Scientific collaboration and collective knowledge: New Essays. Oxford University Press.Google Scholar
  18. Nickles, T. (2006). Heuristic appraisal: context of discovery or justification? In Revisiting discovery and justification: Historical and philosophical perspectives on the context distinction (pp. 159–182).Google Scholar
  19. Pöyhönen, S. (2017). Value of cognitive diversity in science. Synthese, 194 (11), 4519–4540.CrossRefGoogle Scholar
  20. Pöyhönen, S., & Kuorikoski, J. (2016). Modeling epistemic communities. In Fricker, M., Graham, P.J., Henderson, D., Pedersen, N., Wyatt, J. (Eds.) The routledge handbook of social epistemology (forthcoming). Routledge.Google Scholar
  21. Šešelja, D. (2019). Some lessons from simulations of scientific disagreements, synthese (accepted for publication).Google Scholar
  22. Šešelja, D., & Straßer, C. (2013). Abstract argumentation and explanation applied to scientific debates. Synthese, 190, 2195–2217.CrossRefGoogle Scholar
  23. Šešelja, D., & Straßer, C. (2014a). Epistemic justification in the context of pursuit: a coherentist approach. Synthese, 191(13), 3111–3141.Google Scholar
  24. Šešelja, D., & Straßer, C. (2014b). Heuristic reevaluation of the bacterial hypothesis of peptic ulcer disease in the 1950s. Acta Biotheoretica, 62, 429–454.Google Scholar
  25. Šešelja, D., & Weber, E. (2012). Rationality and irrationality in the history of continental drift: was the hypothesis of continental drift worthy of pursuit? Studies in History and Philosophy of Science, 43, 147–159.CrossRefGoogle Scholar
  26. Thoma, J. (2015). The epistemic division of labor revisited. Philosophy of Science, 82(3), 454–472.CrossRefGoogle Scholar
  27. Weisberg, M. (2006). Robustness analysis. Philosophy of Science, 73(5), 730–742.CrossRefGoogle Scholar
  28. Weisberg, M., & Muldoon, R. (2009). Epistemic landscapes and the division of cognitive labor. Philosophy of Science, 76(2), 225–252.CrossRefGoogle Scholar
  29. Whitt, L.A. (1992). Indices of theory promise. Philosophy of Science, 59, 612–634.CrossRefGoogle Scholar
  30. Wilensky, U. (1999). Netlogo. ( In Center for connected learning and computer based modeling. Northwestern University.
  31. Zollman, K.J.S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.CrossRefGoogle Scholar
  32. Zollman, K.J.S. (2010). The epistemic benefit of transient diversity. Erkenntnis, 72(1), 17–35.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Institute for Philosophy IIRuhr-University BochumBochumGermany
  2. 2.Faculty of Economics and Social SciencesHeidelberg UniversityHeidelbergGermany
  3. 3.Munich Center for Mathematical PhilosophyLMU MunichMunichGermany

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