pp 1–16 | Cite as

Some lessons from simulations of scientific disagreements

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


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.


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



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).


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Munich Center for Mathematical PhilosophyLMU MunichMunichGermany

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