Towards a General Model of Applying Science

Abstract

How is scientific knowledge used, adapted, and extended in deriving phenomena and real‐world systems? This paper aims at developing a general account of ‘applying science’ within the exemplar‐based framework of Data‐Oriented Processing (DOP), which is also known as Exemplar‐Based Explanation (EBE). According to the exemplar‐based paradigm, phenomena are explained not by deriving them all the way down from theoretical laws and boundary conditions but by modelling them on previously derived phenomena that function as exemplars. To accomplish this, DOP proposes to maintain a corpus of derivation trees of previous phenomena together with a matching algorithm that combines subtrees from the corpus to derive new phenomena. By using a notion of derivational similarity, a new phenomenon can be modelled as closely as possible on previously explained phenomena. I will propose an instantiation of DOP which integrates theoretical and phenomenological modelling and which generalises over various disciplines, from fluid mechanics to language technology. I argue that DOP provides a solution for what I call Kuhn’s problem and that it redresses Kitcher’s account of explanation.

Notes

[1] Bernoulli used a precursor of this principle which was known as ‘equality between the potential ascent and actual descent’ (Mikhailov 2002, 70).

[2] Even if the notion of ‘grammar’ is still used by many systems, it is not succinct but consists of (tens of) thousands of rules that are derived from actual language corpora (Knuuttila and Voutilainen 2003).

[3] A grammar rule like S => NP VP says that a sentence (S) consists of a noun phrase (NP) followed by a verb phrase (VP).

[4] Most models also take into account the frequency of occurrence of derivational chunks in the corpus (Manning and Schütze 1999), but I will not go into this here.

Additional information

Notes on contributors

Rens Bod

Rens Bod is at University of St. Andrews, UK.