Machine learning and social theory: Collective machine behaviour in algorithmic trading
Research output: Contribution to journal › Journal article › peer-review
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.
Original language | English |
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Journal | European Journal of Social Theory |
Pages (from-to) | 136843102110560 |
ISSN | 1368-4310 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
ID: 319888635