Advertisement

Synthese

pp 1–16 | Cite as

Some lessons from simulations of scientific disagreements

  • Dunja ŠešeljaEmail author
S.I.: Disagreement in Science
  • 47 Downloads

Abstract

This paper examines lessons obtained by means of simulations in the form of agent-based models (ABMs) about the norms that are to guide disagreeing scientists. I focus on two types of epistemic and methodological norms: (i) norms that guide one’s attitude towards one’s own theory, and (ii) norms that guide one’s attitude towards the opponent’s theory. Concerning (i) I look into ABMs that have been designed to examine the context of peer disagreement. Here I challenge the conclusion that the given ABMs provide a support for the so-called Steadfast Norm, according to which one is epistemically justified in remaining steadfast in their beliefs in face of disagreeing peers. I argue that the proposed models at best provide evidence for a weaker norm, which concerns methodological steadfastness. Concerning (ii) I look into ABMs aimed at examining epistemic effects of scientific interaction. Here I argue that the models provide diverging suggestions and that the link between each ABM and the type of represented inquiry is still missing. Moreover, I examine alternative strategies of arguing in favor of the benefits of scientific interaction, relevant for contemporary discussions on scientific pluralism.

Keywords

Agent-based models Scientific disagreement Rational endorsement Scientific interaction Epistemic toleration Scientific pluralism 

Notes

Acknowledgements

I would like to thank Andrea Robitzsch for valuable discussions on epistemic and methodological norms, which inspired parts of this paper. I am also grateful to two anonymous referees, to Borut Trpin and to the audience of the MAP MCMP (Minorities and Philosophy at the Munich Center for Mathematical Philosophy) seminar where I first presented this paper, for valuable comments. Research for this paper was funded by the DFG (Research Grant HA 3000/9-1).

References

  1. Arnold, E. (2006). The dark side of the force: When computer simulations lead us astray and “model think” narrows our imagination—Pre-conference draft for the models and simulation conference, Paris, June 12–14. Accessed on October 31, 2018. https://eckhartarnold.de/papers/2006_simulations/node10.html.
  2. Arnold, E. (2013). Simulation models of the evolution of cooperation as proofs of logical possibilities. How useful are they? Ethics and Politics, XV(2), 101–138.Google Scholar
  3. Barnes, T. J., & Sheppard, E. (2010). Nothing includes everything’: Towards engaged pluralism in Anglophone economic geography. Progress in Human Geography, 34(2), 193–214.CrossRefGoogle Scholar
  4. Beebe, J. R, Baghramian, M., O’C Drury, L., & Dellsen, F., (2018). Divergent perspectives on expert disagreement: Preliminary evidence from climate science, climate policy, astrophysics, and public opinion. arXiv preprint arXiv:1802.01889.
  5. Boero, R., & Squazzoni, F. (2005). Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. Journal of artificial societies and social simulation, 8, 4.Google Scholar
  6. Borg, A., Frey, D., Šešelja, D., & Straßer, C. (2017). An argumentative agent-based model of scientific inquiry. In S. Benferhat, K. Tabia, & M. Ali (Eds.), Proceedings of the advances in artificial intelligence: From theory to practice—30th international conference on industrial engineering and other applications of applied intelligent systems, IEA/AIE 2017, Arras, France, 27–30 June 2017. Part I (pp. 507–510). Cham: Springer.Google Scholar
  7. Borg, A., Frey, D., Šešelja, D., & Straßer, C. (2017). Examining network effects in an argumentative agent-based model of scientific inquiry. In A. Baltag, J. Seligman, & T. Yamada (Eds.), Proceedings of the logic, rationality, and interaction: 6th international workshop, LORI 2017, Sapporo, Japan, 11–14 September 2017 (pp. 391–406). Berlin: Springer.CrossRefGoogle Scholar
  8. Borg, A., 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
  9. Borg, A., Frey, D., Šešelja, D., & Straßer, C. (2019). Theory-choice, transient diversity and the efficiency of scientific inquiry. European Journal for Philosophy of Science.  https://doi.org/10.1007/s13194-019-0249-5.
  10. Casini, L. & Manzo, G. (2016). Agent-based models and causality: A methodological appraisal. In The IAS working paper series. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133332. Accessed 1 Dec 2018
  11. Chang, H. (2012). Is water H2O? Evidence, Pluralism and Realism. Berlin: Springer.CrossRefGoogle Scholar
  12. Christensen, D. (2010). Higher-order evidence. Philosophy and Phenomenological Research, 81(1), 185–215.CrossRefGoogle Scholar
  13. De Cruz, H., & De Smedt, J. (2013). The value of epistemic disagreement in scientific practice. The case of Homo oresiensis. Studies in History and Philosophy of Science Part A, 44(2), 169–177.CrossRefGoogle Scholar
  14. De Langhe, R. (2013). Peer disagreement under multiple epistemic systems. Synthese, 190, 2547–2556.CrossRefGoogle Scholar
  15. Douglas, H. E. (2009). Science, policy, and the value-free ideal. Pittsburgh: University of Pittsburgh Press.CrossRefGoogle Scholar
  16. Douven, I. (2010). Simulating peer disagreements. Studies in History and Philosophy of Science Part A, 41(2), 148–157.CrossRefGoogle Scholar
  17. 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
  18. Elga, A. (2007). Reflection and disagreement. Noûs, 41(3), 478–502.CrossRefGoogle Scholar
  19. Elgin, C. Z. (2010). Persistent disagreement. In R. Feldman & T. A. Warfield (Eds.), Diasagreement (pp. 53–68). Oxford: Oxford University Press.CrossRefGoogle Scholar
  20. Elliott, K. C., & Willmes, D. (2013). Cognitive attitudes and values in science. Philosophy of Science, 80(5), 807–817.CrossRefGoogle Scholar
  21. Feldman, R. (2005). Respecting the evidence. Philosophical Perspectives, 19(1), 95–119.CrossRefGoogle Scholar
  22. Feldman, R. (2006). Epistemological puzzles about disagreement. Epistemology futures (pp. 216–326). Oxford: Oxford University Press.Google Scholar
  23. Feldman, R. (2007). Reasonable religious disagreements. In L. M. Antony (Ed.), Philosophers without gods (pp. 194–214). Oxford: OUP.Google Scholar
  24. Fleisher, W. (2018a). How to endorse conciliationism.  https://doi.org/10.7282/t3-z234-rj23.
  25. Fleisher, W. (2018b). Rational endorsement. Philosophical Studies, 175(10), 2649–2675.CrossRefGoogle Scholar
  26. Frey, D., & Šešelja, D. (2018a). Robustness and idealization in agent-based models of scientific interaction. British Journal for the Philosophy of Science.  https://doi.org/10.1093/bjps/axy039.
  27. Frey, D., & Šešelja, D. (2018b). What is the epistemic function of highly idealized agent-based models of scientific inquiry? Philosophy of the Social Sciences.  https://doi.org/10.1177/0048393118767085.
  28. Goldman, A. (2010). Epistemic relativism and reasonable disagreement. In R. Feldman & T. Warfield (Eds.), Disagreement (pp. 187–215). Oxford: Oxford University Press.CrossRefGoogle Scholar
  29. Grim, P. (2009). Threshold phenomena in epistemic networks. In AAAI fall symposium: complex adaptive systems and the threshold effect (pp. 53–60).Google Scholar
  30. Grim, P., Singer, D. J., Fisher, S., Bramson, A., Berger, W. J., Reade, C., et al. (2013). Scientific networks on data landscapes: Question difficulty, epistemic success, and convergence. Episteme, 10(04), 441–464.CrossRefGoogle Scholar
  31. Harnagel, A. (2018). A mid-level approach to modeling scientific communities. Studies in History and Philosophy of Science.  https://doi.org/10.1016/j.shpsa.2018.12.010.
  32. Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5, 3.Google Scholar
  33. Hegselmann, R., & Krause, U. (2005). Opinion dynamics driven by various ways of averaging. Computational Economics, 25(4), 381–405.CrossRefGoogle Scholar
  34. Hegselmann, R., & Krause, U. (2006). Truth and cognitive division of labor: First steps towards a computer aided social epistemology. Journal of Artificial Societies and Social Simulation, 9(3), 10.Google Scholar
  35. Kelly, T. (2010a). Peer disagreement and higher-order evidence. In R. Feldman & T. A. Warfield (Eds.), Disagreement (pp. 111–174). Oxford: Oxford University Press.CrossRefGoogle Scholar
  36. Kelly, T. (2010a). Peer disagreement and higher order evidence. Social Epistemology: Essential Readings, 24, 183–217.Google Scholar
  37. Kelp, C., & Douven, I. (2012). Sustaining a rational disagreement. In H. de Regt, S. Hartmann & S. Okasha (Eds.), EPSA philosophy of science: Amsterdam 2009. The European philosophy of science association proceedings (Vol. 1). Dordrecht: Springer.Google Scholar
  38. Konigsberg, A. (2012). The problem with uniform solutions to peer disagreement. Theoria, 79, 96.CrossRefGoogle Scholar
  39. Kuhn, T. (1977). The essential tension: Selected studies in scientific tradition and change. Chicago: University of Chicago press.CrossRefGoogle Scholar
  40. Lacey, H. (2009). The interplay of scientific activity, worldviews and value outlooks. Science and Education, 18, 839–860.CrossRefGoogle Scholar
  41. Lacey, H. (2013). Rehabilitating neutrality. Philosophical studies, 163(1), 77–83.CrossRefGoogle Scholar
  42. Lacey, H. (2014). Science, respect for nature, and human well-being: Democratic values and the responsibilities of scientists today. Foundations of Science, 21, 1–17.Google Scholar
  43. Lacey, H. (2015). ‘Holding’ and ‘endorsing’ claims in the course of scientific activities. Studies in History and Philosophy of Science Part A, 53, 89–95.CrossRefGoogle Scholar
  44. Laudan, L. (1984). Science and values. Berkeley: University of California Press.Google Scholar
  45. Longino, H. (2002). The fate of knowledge. Princeton: Princeton University Press.Google Scholar
  46. Longino, H. E. (2013). Studying human behavior: How scientists investigate aggression and sexuality. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  47. Magnus, P. D. (2014). Science and rationality for one and all. Ergo.  https://doi.org/10.3998/ergo.12405314.0001.005.
  48. Mason, W., & Watts, D. J. (2012). Collaborative learning in networks. Proceedings of the National Academy of Sciences, 109(3), 764–769.CrossRefGoogle Scholar
  49. Mason, W. A., Jones, A., & Goldstone, R. L. (2008). Propagation of innovations in networked groups. Journal of Experimental Psychology: General, 137(3), 422.CrossRefGoogle Scholar
  50. Merdes, C. (2018). Strategy and the pursuit of truth. Synthese. https://doi.org/10.1007/s11229-018-HrB01985-xHrB.
  51. Nickles, T. (2006). Heuristic appraisal: Context of discovery or justification? In J. Schickore & F. Steinle (Eds.), Revisiting discovery and justification: Historical and philosophical perspectives on the context distinction (pp. 159–182). Amsterdam: Springer.CrossRefGoogle Scholar
  52. Rescher, N. (1988). Rationality: A philosophical inquiry into the nature and the rationale of reason. Oxford: Oxford University Press.Google Scholar
  53. Rolin, K. (2011). Diversity and dissent in the social sciences: The case of organization studies. Philosophy of the Social Sciences, 41(4), 470–494.CrossRefGoogle Scholar
  54. Rosenstock, S., O’Connor, C., & Bruner, J. (2017). In epistemic networks, is less really more? Philosophy of Science, 84(2), 234–252.CrossRefGoogle Scholar
  55. Šešelja, D. (2017). Scientific pluralism and inconsistency toleration. Humana. Mente Journal of Philosophical Studies, 32, 1–29.Google Scholar
  56. Šešelja, D. (2018). Exploring scientific inquiry via agent-based modeling (Forthcoming). http://philsci-archive.pitt.edu/15120/1/Exploratory_ABMs.pdf. Accessed 1 Dec 2018.
  57. Šešelja, D., Kosolosky, L., & Straßer, C. (2012). Rationality of scientific reasoning in the context of pursuit: Drawing appropriate distinctions. Philosophica, 86, 51–82.Google Scholar
  58. Šešelja, D., & Straßer, C. (2013). Abstract argumentation and explanation applied to scientific debates. Synthese, 190, 2195–2217.CrossRefGoogle Scholar
  59. Šešelja, D., & Straßer, C. (2014). Epistemic justification in the context of pursuit: A coherentist approach. Synthese, 191(13), 3111–3141.CrossRefGoogle Scholar
  60. 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
  61. Solomon, M. (2006). Groupthink versus the wisdom of crowds: The social epistemology of deliberation and dissent. The Southern Journal of Philosophy, 44, 28–42.CrossRefGoogle Scholar
  62. Straßer, C., Šešelja, D., & Wieland, J. W. (2015). With-standing tensions: Scientific disagreement and epistemic tolerance. In E. Ippoliti (Ed.), Heuristic reasoning. Studies in applied philosophy, epistemology and rational ethics (pp. 113–146). Berlin: Springer.Google Scholar
  63. Thicke, M. (2018). Evaluating formal models of science. Journal for General Philosophy of Science, 66, 371.Google Scholar
  64. Verreault-Julien, P. (2019). How could models possibly provide how-possibly explanations? Studies in History and Philosophy of Science Part A, 73, 22–33.CrossRefGoogle Scholar
  65. Whitt, L. A. (1990). Theory pursuit: Between discovery and acceptance. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 1, 467–483.Google Scholar
  66. Whitt, L. A. (1992). Indices of theory promise. Philosophy of Science, 59, 612–634.CrossRefGoogle Scholar
  67. Zollman, K. J. S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.CrossRefGoogle Scholar
  68. 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.Munich Center for Mathematical PhilosophyLMU MunichMunichGermany

Personalised recommendations