Towards a General Model of Applying Science
Rens Bod

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.