Abstract
In this paper I lay out a non-formal kernel for a heuristic logic—a set of rational procedures for scientific discovery and ampliative reasoning—specifically, the rules that govern how we generate hypotheses to solve problems. To this end, first I outline the reasons for a heuristic logic (Sect. 1) and then I discuss the theoretical framework needed to back it (Sect. 2). I examine the methodological machinery of a heuristic logic (Sect. 3), and the meaning of notions like ‘logic’, ‘rule’, and ‘method’. Then I offer a characterization of a heuristic logic (Sect. 4) by arguing that heuristics are ways of building problem-spaces (Sect. 4.1). I examine (Sect. 4.2) the role of background knowledge for the solution to problems, and how a heuristic logic builds upon a unity of problem-solving and problem-finding (Sect. 4.3). I offer a first classification of heuristic rules (Sect. 5): primitive and derived. Primitive heuristic procedures are basically analogy and induction of various kinds (Sect. 5.1). Examples of derived heuristic procedures (Sect. 6) are inversion heuristics (Sect. 6.1) and heuristics of switching (Sect. 6.2), as well as other kinds of derived heuristics (Sect. 6.3). I then show how derived heuristics can be reduced to primitive ones (Sect. 7). I examine another classification of heuristics, the generative and selective (Sect. 8), and I discuss the (lack of) ampliativity and the derivative nature of selective heuristics (Sect. 9). Lastly I show the power of combining heuristics for solving problems (Sect. 10).
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- 1.
- 2.
The most famous defender of this view is Herbert Simon (see Simon et al. 1987), who argued for an ‘algorithmic discovery’, a computational model for discovery, implemented in his software BACON. In the end we can consider this view untenable. At most it can model the result of the construction of a hypothesis—the only one that can be treated effectively by a computer, namely the one after the conceptualization, selection of data and choice of variables, has already been made by humans (for a critical appraisal of Simon’s approach see in particular Nickles 1980; Kantorovich 1993, 1994; Gillies 1996; Weisberg 2006).
- 3.
A representation theorem is a theorem that states that an abstract structure with certain properties can be reduced to, or is isomorphic to, another structure.
- 4.
See Cellucci (2013), Chap. 20.
- 5.
An analogy is a type of inference that concludes, from the fact that two objects are similar in certain respects and that one of them has a certain property, that the other has the same property. It therefore enables the transfer of certain properties from a source (object or set of objects) to a target (object or set of objects). According to the meaning that we attach to the expression “to be similar”, we can define different kinds of analogies (e.g. analogy for quasi-equality, separate indistinguishability, inductive analogy, proportional analogy).
- 6.
In their paper Spiro et al. discuss these ways of combining analogies only by means of didactical examples: they do not offer examples of real cases of scientific discovery, or hypotheses, obtained by these combinations. Nonetheless their taxonomy is effective and can be put in use.
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Acknowledgements
I would like to thank David Danks, Carlo Cellucci, the two anonymous referees, and the speakers at the conference ‘Building Theories’ (Rome, 16–18 June 2016) for their valuable comments on an early version of this paper.
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Ippoliti, E. (2018). Heuristic Logic. A Kernel. In: Danks, D., Ippoliti, E. (eds) Building Theories. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-72787-5_10
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