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

Christian Borch*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

3 Downloads (Pure)

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
Number of pages18
ISSN1368-4310
DOIs
Publication statusPublished - 1 Nov 2021

Bibliographical note

Epub ahead of print. Published online: 1. November 2021

Keywords

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

Cite this