Model Talk: Calculative Cultures in Quantitative Finance

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This paper explores how calculative cultures shape perceptions of models and practices of model use in the financial industry. A calculative culture comprises a specific set of practices and norms concerning data and model use in an organizational setting. Drawing on interviews with model users (data scientists, software developers, traders, and portfolio managers) working in algorithmic securities trading, I argue that the introduction of complex machine-learning models changes the dynamics in calculative cultures, which leads to a displacement of human judgment in quantitative finance. In this paper, I distinguish between three calculative cultures: (1) an idealistic culture of undivided trust in models, (2) a pragmatic culture of skepticism toward model accuracy, and (3) a pragmatic idealist culture of early stage skepticism and implementation and production-phase idealism. Based on the empirical material, the analysis engages with examples of each of the three calculative cultures. The study contributes to the social studies of finance and science and technology studies more broadly by showing how perceptions of models shape and are shaped through model work in data-intensive, computerized finance.
Original languageEnglish
JournalScience, Technology & Human Values
Issue number3
Pages (from-to)600-627
Number of pages28
Publication statusPublished - May 2021

Bibliographical note

Published online: 22. July 2020


  • Calculative cultures
  • Quantitative finance
  • Machine learning
  • Calculative idealism
  • Calculative pragmatism
  • Judgment

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