An essay by Max Grünberg for Historical Materialism journal.
Abstract. Within the planning discourse two poles have materialised over the last decades: a participatory ideal guided by substantive rationality, opposed to an algorithmic governmentality subordinated to instrumental reason. This rift within socialist thought is also observable when it comes to the discovery of needs. The paper understands this discovery procedure primarily as a forecasting problem and demonstrates how many authors dedicated to a participatory planning process call for consumers to write down their desires in the form of wish lists. As a response to this epistemically questionable discovery procedure, the state of the art in capitalist demand-forecasting at enterprises like Amazon is presented, where machine-learning algorithms excel at modelling interrelated time series on a global level by extrapolating demand patterns in real-time. The paper closes with a proposal to reconfigure this predictive apparatus for socialist ends and raises questions concerned with the political implications of centralising decision-making in black-box algorithms.
Full citation: Max Grünberg, “The Planning Daemon: Future Desire and Communal Production”, Historical Materialism (published online ahead of print 2023). https://doi.org/10.1163/1569206x-bja10001
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The essay is a research output of the KIM project ‘Modeling the Crisis. The Role of AI and Statistical Models in the COVID-19 Pandemic’ funded by the Volkswagen Foundation. The project also included the Breaking Models workshop at Max Planck Institute for the History of Science in Berlin.