{"title":"The Planning Daemon: Future Desire and Communal Production","authors":"Max Grünberg","doi":"10.1163/1569206x-bja10001","DOIUrl":null,"url":null,"abstract":"\nWithin 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.","PeriodicalId":46231,"journal":{"name":"Historical Materialism-Research in Critical Marxist Theory","volume":" ","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Historical Materialism-Research in Critical Marxist Theory","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1163/1569206x-bja10001","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"PHILOSOPHY","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍:
Historical Materialism is an interdisciplinary journal dedicated to exploring and developing the critical and explanatory potential of Marxist theory. The journal started as a project at the London School of Economics from 1995 to 1998. The advisory editorial board comprises many leading Marxists, including Robert Brenner, Maurice Godelier, Michael Lebowitz, Justin Rosenberg, Ellen Meiksins Wood and others. Marxism has manifested itself in the late 1990s from the pages of the Financial Times to new work by Fredric Jameson, Terry Eagleton and David Harvey. Unburdened by pre-1989 ideological baggage, Historical Materialism stands at the edge of a vibrant intellectual current, publishing a new generation of Marxist thinkers and scholars.