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The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms

In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag (2019)

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  1. Humanistic interpretation and machine learning.Juho Pääkkönen & Petri Ylikoski - 2021 - Synthese 199:1461–1497.
    This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the (...)
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  • Towards a dual process epistemology of imagination.Michael T. Stuart - 2019 - Synthese (2):1-22.
    Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes (...)
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  • Decentring the discoverer: how AI helps us rethink scientific discovery.Elinor Clark & Donal Khosrowi - 2022 - Synthese 200 (6):1-26.
    This paper investigates how intuitions about scientific discovery using artificial intelligence can be used to improve our understanding of scientific discovery more generally. Traditional accounts of discovery have been agent-centred: they place emphasis on identifying a specific agent who is responsible for conducting all, or at least the important part, of a discovery process. We argue that these accounts experience difficulties capturing scientific discovery involving AI and that similar issues arise for human discovery. We propose an alternative, collective-centred view as (...)
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