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Scientific Discovery Reloaded

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Abstract

The way scientific discovery has been conceptualized has changed drastically in the last few decades: its relation to logic, inference, methods, and evolution has been deeply reloaded. The ‘philosophical matrix’ moulded by logical empiricism and analytical tradition has been challenged by the ‘friends of discovery’, who opened up the way to a rational investigation of discovery. This has produced not only new theories of discovery (like the deductive, cognitive, and evolutionary), but also new ways of practicing it in a rational and more systematic way. Ampliative rules, methods, heuristic procedures and even a logic of discovery have been investigated, extracted, reconstructed and refined. The outcome is a ‘scientific discovery revolution’: not only a new way of looking at discovery, but also a construction of tools that can guide us to discover something new. This is a very important contribution of philosophy of science to science, as it puts the former in a position not only to interpret what scientists do, but also to provide and improve tools that they can employ in their activity.

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Notes

  1. This theory, which of course stems from Freud, is a version of the out-of-mind view, as the person has no conscious awareness of, or control over, the links among ideas.

  2. This examination is not intended to be exhaustive: it analyses a few representative works of approaches that eased a new way of accounting for scientific discovery.

  3. A dedutive inference is obtained by using primitive rules whereby the content of their conclusion is literally included in their premises. A stock example is modus ponens (A, A → B ∴ B)—B is literally part of the second premise. Thus a deductive reasoning, as a chain of these basic rules, cannot expand logically the premises.

  4. Simon (Kulkarni and Simon 1988) relaxed the structures of BACON in later programs such as KEKADA, which, unlike BACON that “was concerned mainly with the ways in which theories could be generated from empirical data, with little or no help from theory” (Kulkarni and Simon 1988, 140), tries to deal with issues such as the question of where the data came from, the processes of designing experiments and programs of observation.

  5. I would like to thank Tom Nickles for a clarification of this point.

  6. See also (Gillies 2014 and Roberts 1989).

  7. This idea goes back to Plato, and Aristotle (see Quarantotto 2017) and has been re-proposed also recently (Laudan 1977, 1981; Nickles 1981; Cellucci 2017b).

  8. Plausible here, following Aristotle’s notion of andoxa, simply means that the arguments for the hypothesis are ‘stronger’ (in quality) than those against it on the basis of the existing knowledge.

  9. A positive heuristics guides us in the construction of admissible paths during the search of a solution for a problem. A negative heuristics prevents us from building certain paths—by blocking the modus tollens on a specific part of the theory.

  10. Stock examples are: change of unit of analysis, change of level of analysis, focus on processes vs focus on variables.

  11. An atomistic view of heuristics, that there is an ultimate, base set from which all others can be compounded, has also been put forward by Gigerenzer and Todd (see Gigerenzer et al. 1999).

  12. There are many examples of problems generated by heuristic procedures, like Poincaré’s conjecture.

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Acknowledgements

I would like to thank Tom Nickles for all his support and advice in general (not only for this paper), and the anonymous referees for their comments and suggestions.

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    Emiliano Ippoliti

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Ippoliti, E. Scientific Discovery Reloaded. Topoi 39, 847–856 (2020). https://doi.org/10.1007/s11245-017-9531-3

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