What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic (Craver, in: Kaiser, Scholz, Plenge, Hüttemann (eds) Explanation in the special sciences: the case of biology and history, Springer, Dordrecht, pp 27–52, 2014) or epistemic norms (Bechtel in Mental mechanisms: philosophical perspectives on cognitive neuroscience, Routledge, London, 2008). Still, mechanistic philosophers on both sides agree that there is no sharp distinction between the processes of discovery and explanation (Bechtel and Richardson in Discovering complexity. Decomposition and localization as strategies in scientific research, MIT Press, Cambridge, 2010; Craver and Darden in In search of mechanisms: discoveries across the life sciences, University of Chicago Press, Chicago, 2013). Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition (Haugeland, in: Having thought. Essays in the metaphysics of mind, Harvard University Press, Cambridge, pp 267–290, 1998) we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery.
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Sheredos (2016) argues that another epistemic norm of mechanistic inquiry is the instruction to achieve generality, e.g. by categorizing token entities and activities in the same mechanism into types. We do not discuss generality here, because we want to allow for the possibility that describing one-off mechanisms can make a phenomenon intelligible without achieving generality. Besides, Sheredos’ account seems to imply that generality is a variety of the norm of intelligibility, because following this norm makes the scope of an explanatory text intelligible.
Illari seems to distinguish between norms and constraints: “Each kind of constraint alone gives us some kind of useful set of norms for evaluating, and attempting to build, mechanistic explanations.” (ibid., p. 253, emphasis added) While she does not explicitly define “norms”, we suspect that Illari means what we call “normative constraints” while her constraints correspond to that we call “norms”. However, not much hinges on this terminological difference, since Illari does not put her distinction to work.
For a non-inferential account of microscopic observation see Hacking (1981).
The locus classicus of an inferential account is van Fraasen (1980).
To make this more concrete: What we have in mind here is that scientists should not make assumptions that are highly untenable given their explanatory interests, like, say postulating that the moon is made of cheese when trying to explain its surface structure.
For illustration, Dennett discusses Conway’s “Game of Life”, a cellular automaton based on a 2D-grid of ON and OFF cells. Once the initial configuration is set, one can start the game and watch how the grid evolves. The evolution is governed by an algorithm which specifies, at each step, for any individual cell whether it will be on or off next time around depending on the cell’s current status as well as that of its eight neighbors. As a result, players can see “figures” move across the grid. Strikingly, observers will soon be able to recognize certain “species” and predict their “behavior” without knowing the rules in the algorithm. For Dennett, this illustrates that for higher-level causal generalizations to hold, we do not need to know what lower-level principles govern higher-level regularities. .
Our talk of “higher” and “lower” levels here is compatible with the mechanistic commitment that mechanisms form local nested hierarchies (see also Craver 2007, p. 191f.).
A stronger way to read Haugeland is to claim that neither sense of “pattern” is metaphysically prior. For current purposes, however, we bracket the metaphysics of patterns and instead focus on the role that skills play for pattern recognition in scientific practice.
What we mean here is not that scientists involved in the pattern recognition practice already agree on the details of the mechanism which will be the product of the discovery process. Rather, we suggest they have a shared agenda to explain a specific phenomenon by identifying the entities and activities responsible for it. This does still allow for disagreement as to which entities and activities are involved (see also below).
As we will detail below, because there can be considerable uncertainty regarding the mechanism during discovery, clearly individuating the corresponding pattern recognition practice is often only possible in retrospect.
Epistemic activities are typically governed by rules (e.g. standards of a discipline), but as Chang points out, these rules need not be articulated.
Individual epistemic activities are carried out from a particular epistemic perspective (see Sect. 3.1). Yet, a pattern recognition practice may combine epistemic activities that adopt different epistemic perspectives.
We agree with Halina (2018) that the norms of accuracy and intelligibility can sometimes pull in different directions: “intelligibility may take priority in pedagogical contexts; while conveying information about the target mechanisms may become more important in those contexts where advanced researchers are attempting to understand and intervene on a target system.” (ibid., p. 221, see also Kaplan and Craver 2011, 609f. for a similar point). However, here we are only concerned with the latter contexts, i.e. contexts in which researchers perform epistemic activities to generate novel knowledge about entities and activities. Thus our point about the need for both ontic and epistemic norms still holds for these contexts.
While this peer disagreement continued for several years, the pattern recognition practice eventually converged a shared conceptual framework (“genetic code”, “information transfer” etc.) as well as experimental systems (e.g., E. coli in-vitro system) to investigate protein synthesis (cf. Rheinberger 1997, ch. 12). The now shared epistemic perspective is evident Watson’s (1965) textbook, which presents a new model of protein synthesis including mRNA, together with biochemical and molecular biological details about the mechanism (cf. Darden and Craver 2002, p. 17).
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We are indebted to Carl Craver, Uljana Feest, Joseph Rouse, and Beate Krickel for ample discussion on the subject and helpful feedback on earlier versions of this paper. We would also like to thank the participants of the Workshop “Patterns in Science” held at Berlin School of Mind and Brain in 2015, the SPSP conference 2016 at Rowan University, the GAP conference 2018 at University of Cologne, and discussants at the philosophy of science colloquium at Australian National University in 2019 as well as three anonymous reviewers.
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Kästner, L., Haueis, P. Discovering Patterns: On the Norms of Mechanistic Inquiry. Erkenn (2019). https://doi.org/10.1007/s10670-019-00174-7