Machine learning and social theory: Collective machine behaviour in algorithmic trading

Research output: Contribution to journalJournal articlepeer-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 languageEnglish
JournalEuropean Journal of Social Theory
Pages (from-to)136843102110560
ISSN1368-4310
DOIs
Publication statusPublished - 2021
Externally publishedYes

ID: 319888635