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Machine learning and social theory: Collective machine behaviour in algorithmic trading.
- Source :
- European Journal of Social Theory; Nov2022, Vol. 25 Issue 4, p503-520, 18p
- Publication Year :
- 2022
-
Abstract
- This article examines what the rise in machine learning (ML) systems might mean for social theory. Focusing on financial markets, in which algorithmic securities trading founded on ML-based decision-making is gaining traction, I discuss the extent to which established sociological notions remain relevant or demand a reconsideration when applied to an ML context. I argue that ML systems have some capacity for agency and for engaging in forms of collective machine behaviour, in which ML systems interact with other machines. However, ML-based collective machine behaviour is irreducible to human decision-making and thereby challenges established sociological notions of financial markets (including that of embeddedness). I argue that such behaviour can nonetheless be analysed through an adaptation of sociological theories of interaction and collective behaviour. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13684310
- Volume :
- 25
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- European Journal of Social Theory
- Publication Type :
- Academic Journal
- Accession number :
- 159841808
- Full Text :
- https://doi.org/10.1177/13684310211056010