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.
Purchase
Buy instant access (PDF download and unlimited online access):
Institutional Login
Log in with Open Athens, Shibboleth, or your institutional credentials
Personal login
Log in with your brill.com account
Adorno, Theodor W. 2004 [1966], Negative Dialectics, translated by E.B. Ashton, London: Taylor & Francis.
Albert, Michael and Robin Hahnel 1991a, The Political Economy of Participatory Economics, Princeton, NJ: Princeton University Press.
Albert, Michael and Robin Hahnel 1991b, Looking Forward: Participatory Economics for the Twenty First Century, Boston, MA: South End Press.
Amazon Science 2021, ‘The History of Amazon’s Forecasting Algorithm’, available at: <https://www.amazon.science/latest-news/the-history-of-amazons-forecasting-algorithm>.
Apolito, Aurora 2020, ‘The Problem of Scale in Anarchism and the Case for Cybernetic Communism’, available at: <https://www.its.caltech.edu/~matilde/ScaleAnarchy.pdf>.
Arboleda, Martín 2021, Gobernar La Utopía: Sobre La Planificación y El Poder Popular, Buenos Aires: Caja Negra.
Beer, Stafford 1973, ‘Fanfare for Effective Freedom: Cybernetic Praxis in Government’, available at: <https://kybernetik.ch/dwn/Fanfare_for_Freedom.pdf>.
Benanav, Aaron 2020a, Automation and the Future of Work, London: Verso.
Benanav, Aaron 2020b, ‘How to Make a Pencil’, Logic Magazine, 12, available at: <https://logicmag.io/commons/how-to-make-a-pencil/>.
Benidis, Konstantinos, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot and Tim Januschowski 2022, ‘Deep Learning for Time Series Forecasting: Tutorial and Literature Survey’, ACM Computing Surveys, 55, 6: 1–36.
Bernes, Jasper 2013, ‘Logistics, Counterlogistics, and the Communist Prospect’, Endnotes, 3: 172–201.
Bernes, Jasper 2020, ‘Planning and Anarchy’, South Atlantic Quarterly, 119, 1: 53–73.
Boettke, Peter J. and Rosolino A. Candela 2023, ‘On the Feasibility of Technosocialism’, Journal of Economic Behavior & Organization, 205: 44–54.
Bratton, Benjamin 2015, The Stack: On Software and Sovereignty, Cambridge, MA: The MIT Press.
Cockshott, W. Paul and Allin Cottrell 1993, Towards a New Socialism, Nottingham: Spokesman Books.
Dapprich, Jan Philipp 2022, ‘Optimal Planning with Consumer Feedback: A Simulation of a Socialist Economy’, Review of Political Economy, <https://doi.org/10.1080/09538259.2021.2005367>.
Devine, Pat. J. 1988, Democracy and Economic Planning: The Political Economy of a Self-Governing Society, Cambridge: Polity Press.
Dietvorst, Berkeley J., Joseph P. Simmons and Cade Massey 2015, ‘Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing them Err’, Journal of Experimental Psychology: General, 144, 1: 114–126.
Dobb, Maurice 1935, ‘Review of Brutzkus 1935 and Hayek 1935’, The Economic Journal, 45, 179: 532–535.
Dobb, Maurice 1967 [1964], ‘Some Further Comments on the Discussion about Socialist Price-policy’, in Papers on Capitalism, Development, and Planning, London: Routledge & Kegan Paul.
Edwards, Paul N. 2010, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, Cambridge, MA: The MIT Press.
Eisenach, Carson, Yagna Patel and Dhruv Madeka 2022, ‘MQTransformer: Multi- Horizon Forecasts with Context Dependent and Feedback-Aware Attention’, arXiv, <https://arxiv.org/abs/2009.14799>.
Engels, Friedrich 1975 [1843], ‘Outlines of a Critique of Political Economy’, in Marx/ Engels Collected Works, Volume 3, Moscow: Progress Publishers.
Engels, Friedrich 1987 [1878], Anti-Duhring, in Marx/Engels Collected Works, Volume 25, Moscow: Progress Publishers.
Engels, Friedrich 1990 [1884], The Origin of the Family, Private Property and the State, in the Light of the Researches of Lewis H. Morgan, in Marx/Engels Collected Works, Volume 26, Moscow: Progress Publishers.
Fildes, Robert and Paul Goodwin 2021, ‘Stability in the Inefficient Use of Forecasting Systems: A Case Study in a Supply Chain Company’, International Journal of Forecasting, 37, 2: 1031–1046.
Fildes, Robert, Paul Goodwin, Michael Lawrence and Konstantinos Nikolopoulos 2009, ‘Effective Forecasting and Judgmental Adjustments: An Empirical Evaluation and Strategies for Improvement in Supply-chain Planning’, International Journal of Forecasting, 25, 1: 3–23.
Fisher, Mark 2009, Capitalist Realism: Is There No Alternative?, Winchester: Zero Books.
Gilliland, M., L. Tashman and U. Sglavo (eds.) 2021, Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning, Wiley and SAS Business Series, Hoboken, NJ: Wiley.
Hacking, Ian 1982, ‘Biopower and the Avalanche of Printed Numbers’, Humanities in Society, 5, 3 & 4: 279–295.
Hahnel, Robin 2021, Democratic Economic Planning, New York: Routledge.
Härdin, Tomas 2021, ‘Planning Complexity for Model Economies’, available at: <https://www.xn--hrdin-gra.se/blog/2021/02/24/planning-complexity-for-model-economies/>.
Hayek, Friedrich A. 1948a [1945], ‘The Use of Knowledge in Society’, in Individualism and Economic Order, Chicago: The University of Chicago Press.
Hayek, Friedrich A. 1948b [1937], ‘Economics and Knowledge’, in Individualism and Economic Order, Chicago: The University of Chicago Press.
Hayek, Friedrich A. 2002 [1968], ‘Competition as a Discovery Procedure’, Quarterly Journal of Austrian Economics, 5, 3: 9–23.
Hayek, Friedrich A. 2017 [1952], ‘The Sensory Order: An Inquiry into the Foundations of Theoretical Psychology’, in The Collected Works of F.A. Hayek, Volume XIV, The Sensory Order: And Other Writings on the Foundations of Theoretical Psychology, edited by Viktor J. Vanberg, Chicago: The University of Chicago Press.
Hyndman, R.J. 2020, ‘A Brief History of Forecasting Competitions’, International Journal of Forecasting, 36, 1: 7–14.
Januschowski, Tim, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram and Laurent Callot 2018, ‘Deep Learning for Forecasting: Current Trends and Challenges’, Foresight: The International Journal of Applied Forecasting, 51: 42–47.
Januschowski, Tim, Yuyang Wang, Kari Torkkola, Timo Erkkilä, Hilaf Hasson and Jan Gasthaus 2022, ‘Forecasting with Trees’, International Journal of Forecasting, 38, 4: 1473–1481.
Joque, Justin 2022, Revolutionary Mathematics: Artificial Intelligence, Statistics, and the Logic of Capitalism, London: Verso.
Kadra, Arlind, Marius Lindauer, Frank Hutter and Josif Grabocka 2021, ‘Well-tuned Simple Nets Excel on Tabular Datasets’, arXiv, <https://arxiv.org/abs/2106.11189>.
Kantorovich, Leonid V. 1965, The Best Use of Economic Resources, translated by G. Morton, translated by P.F. Knightsfield, Oxford: Pergamon Press.
Kornweitz, Arif 2021, ‘Function Creep: Change as a Trace of Dominant Norms’, A New AI Lexicon, 4 August, available at: <https://medium.com/a-new-ai-lexicon/a-new-ai-lexicon-function-creep-1c20834fab4a>.
Laibman, David 2015, ‘Multilevel Democratic Iterative Coordination: An Entry in the “Envisioning Socialism” Models Competition’, MARXISM 21, 12, 1: 307–345.
Laibman, David 2022, ‘Systemic Socialism: A Model of the Models’, Science & Society, 86, 2: 225–247.
Lange, Oskar 1967, ‘The Computer and the Market’, in Socialism, Capitalism and Economic Growth, edited by C.H. Feinstein, London: Cambridge University Press.
Lenin, Vladimir Ilyich 2015, State and Revolution, annotated by Todd Chretien, Chicago: Haymarket Books.
Logg, Jennifer M., Julia A. Minson and Don A. Moore 2019, ‘Algorithm Appreciation: People Prefer Algorithmic to Human Judgment’, Organizational Behavior and Human Decision Processes, 151: 90–103.
Makridakis, Spyros and Michele Hibon 1979, ‘Accuracy of Forecasting: An Empirical Investigation’, Journal of the Royal Statistical Society, 142, 2: 97.
Makridakis, Spyros, Evangelos Spiliotis and Vassilios Assimakopoulos 2020, ‘The M4 Competition: 100,000 Time Series and 61 Forecasting Methods’, International Journal of Forecasting, 36, 1: 54–74.
Makridakis, Spyros, Evangelos Spiliotis and Vassilios Assimakopoulos 2021a, ‘The M5 Competition: Background, Organization, and Implementation’, International Journal of Forecasting, 38, 4: 1325–1336.
Makridakis, Spyros, Evangelos Spiliotis and Vassilios Assimakopoulos 2021b, ‘Predicting/ Hypothesizing the Findings of the M5 Competition’, International Journal of Forecasting, 38, 4: 1337–1345.
Makridakis, Spyros, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin and Robert L. Winkler 2021, ‘The M5 Uncertainty Competition: Results, Findings and Conclusions’, International Journal of Forecasting, 38, 4: 1365–1385.
Mandel, Ernest 1986, ‘In Defence of Socialist Planning’, New Left Review, I, 159: 5–37.
Marx, Karl 1986 [1939], Grundrisse, in Marx/Engels Collected Works, Volume 28, Moscow: Progress Publishers.
Marx, Karl 1996 [1867], Capital. Volume I, in Marx/Engels Collected Works, Volume 35, Moscow: Progress Publishers.
Marx, Karl 1998 [1894], Capital. Volume III, in Marx/Engels Collected Works, Volume 37, Moscow: Progress Publishers.
Marx, Karl and Friedrich Engels 1987 [1848], Manifesto of the Communist Party, in Marx/Engels Collected Works, Volume 6, Moscow: Progress Publishers.
Medina, Eden 2011, Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile, Cambridge, MA: The MIT Press.
Meehl, Paul E. 1996 [1954], Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, Lanham, MD: Jason Aronson Inc.
Miller, Peter and Ted O’Leary 1987, ‘Accounting and the Construction of the Governable Person’, Accounting, Organizations and Society, 12, 3: 235–265.
Mises, Ludwig von 1935 [1920], ‘Economic Calculation in the Socialist Commonwealth’, in Collectivist Economic Planning: Critical Studies on the Possibilities of Socialism, London: Routledge.
Morozov, Evgeny 2019, ‘Digital Socialism? The Calculation Debate in the Age of Big Data’, New Left Review, II, 116/117: 33–67.
Newbold, Paul and Clive Granger 1974, ‘Experience with Forecasting Univariate Time Series and the Combination of Forecasts’, Journal of the Royal Statistical Society, Series A (General), 137, 2: 131–165.
Nieto, Maxi 2021, ‘Entrepreneurship and Decentralised Investment in a Planned Economy: A Critique of the Austrian Reading’, Historical Materialism, 30, 1: 133–163.
O’Neill, John 2019, ‘From Socialist Calculation to Political Ecology’, in Marx200: The Significance of Marxism in the 21st Century, edited by Mary Davis, Glasgow: Praxis Press.
Nove, Alec 1991, The Economics of Feasible Socialism Revisited, London: Harper Academic.
Phillips, Leigh and Michal Rozworski 2019, The People’s Republic of Walmart: How the World’s Biggest Corporations Are Laying the Foundation for Socialism, London: Verso.
Postone, Moishe 1995, Time, Labor, and Social Domination: A Reinterpretation of Marx’s Critical Theory, Cambridge: Cambridge University Press.
Rahimi, Ali and Ben Recht 2017, ‘Reflections on Random Kitchen Sinks’, arg min blog, 5 December, available at: <http://www.argmin.net/2017/12/05/kitchen-sinks/>.
Rindzevičiūtė, Eglė 2023, The Will To Predict: Orchestrating the Future from the Communist Utopia to Global Climate Crisis, Ithaca, NY: Cornell University Press.
Samothrakis, Spyridon 2021, ‘Artificial Intelligence Inspired Methods for the Allocation of Common Goods and Services’, PLOS ONE, 16, 9: 1–16, <https://doi.org/10.1371/journal.pone.0257399>.
Saros, Daniel E. 2014, Information Technology and Socialist Construction: The End of Capital and the Transition to Socialism, London: Routledge.
Schumpeter, Joseph 2003 [1942], Capitalism, Socialism, and Democracy, London: Routledge.
Schweickart, David 2006, ‘Nonsense on Stilts: Michael Albert’s Parecon’, available at: <http://dschwei.sites.luc.edu/parecon.pdf>.
Schwerzmann, Katia 2021, ‘Abolish! Against the Use of Risk Assessment Algorithms at Sentencing in the US Criminal Justice System’, Philosophy and Technology, 34, 4: 1883–1904.
Seaman, Brian and John Bowman 2021, ‘Applicability of the M5 to Forecasting at Walmart’, International Journal of Forecasting, <https://doi.org/10.1016/j.ijforecast.2021.06.002>.
Smyl, Slawek 2020, ‘A Hybrid Method of Exponential Smoothing and Recurrent Neural Networks for Time Series Forecasting’, International Journal of Forecasting, 36, 1: 75–85.
Somepalli, Gowthami, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss and Tom Goldstein 2021, ‘SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training’, arXiv, <https://doi.org/10.48550/arXiv.2106.01342>.
Srnicek, Nick and Alex Williams 2015, Inventing the Future: Postcapitalism and a World Without Work, London: Verso.
Star, Susan Leigh 1999, ‘The Ethnography of Infrastructure’, American Behavioral Scientist, 43, 3: 377–391.
Taleb, Nassim Nicholas 2007, The Black Swan: The Impact of the Highly Improbable, New York: Random House.
Thompson, Neil C., Kristjan Greenewald, Keeheon Lee and Gabriel F. Manso 2021, ‘Deep Learning’s Diminishing Returns: The Cost of Improvement is Becoming Unsustainable’, IEEE Spectrum, 58, 10: 50–55.
Toscano, Alberto 2011, ‘Logistics and Opposition’, Mute, 3, 2, available at: <https://www.metamute.org/editorial/articles/logistics-and-opposition>.
Toscano, Alberto 2014, ‘Lineaments of the Logistical State’, Viewpoint Magazine, available at: <https://viewpointmag.com/2014/09/28/lineaments-of-the-logistical-state/>.
Trotsky, Leon 1933, Soviet Economy in Danger: The Expulsion of Zinoviev, New York: Pioneer Publishers.
Uebel, Thomas 2018, ‘Calculation in Kind and Substantive Rationality: Neurath, Weber, Kapp’, History of Political Economy, 50, 2: 289–320.
Weber, Max 2019, Economy and Society: A New Translation, edited by Keith Tribe, Cambridge, MA: Harvard University Press.
Wetzel, Tom 2008, ‘Workers’ Power and the Russian Revolution’, in Real Utopia: Participatory Society for the 21st Century, edited by Chris Spannos, Oakland, CA: AK Press.
Zuboff, Shoshana 2018, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, New York: PublicAffairs.
All Time | Past 365 days | Past 30 Days | |
---|---|---|---|
Abstract Views | 2799 | 1137 | 92 |
Full Text Views | 2504 | 53 | 3 |
PDF Views & Downloads | 7163 | 137 | 7 |
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.
All Time | Past 365 days | Past 30 Days | |
---|---|---|---|
Abstract Views | 2799 | 1137 | 92 |
Full Text Views | 2504 | 53 | 3 |
PDF Views & Downloads | 7163 | 137 | 7 |