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

Christian Borch*

*Corresponding author for this work

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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.
Original languageEnglish
JournalEuropean Journal of Social Theory
Volume25
Issue number4
Pages (from-to)503-520
Number of pages18
ISSN1368-4310
DOIs
Publication statusPublished - Nov 2022

Bibliographical note

Published online: 1. November 2021.

Keywords

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

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