Machine Learning and Social Theory: Collective Machine Behaviour in Algorithmic Trading

Christian Borch*

*Corresponding author for this work

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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
Issue number4
Pages (from-to)503-520
Number of pages18
Publication statusPublished - Nov 2022

Bibliographical note

Published online: 1. November 2021.


  • Algorithmic trading
  • Collective behaviour
  • Embeddedness
  • Interaction
  • Machine learning

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