The Predictions on the Future of Labour are not Grounded; Some Arguments for a Bayesian Approach

Viorel Rotila ()
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Viorel Rotila: Professor PhD, Dunarea de Jos University of Galati, Romania, Director of the Solidaritatea Research and Development Center, Galati, Romania.

Postmodern Openings, 2018, vol. 9, issue 3, 36-63

Abstract: Expectations on predicting the future of work are not substantiated, and can only control some of the variables that define its character. The following variables could help to shape a Bayesian methodological framework of the future of labour and could interfere in modelling labour status: 1) The meaning of labour depends on the context of significance specific to each historical period 2) The fate of labour is dependent on the ontological status of the utensil 3) The status of labour is defined to a significant extent in the self-fulfilling / defeating prophecy horizon 4) The normative perspective: the future of labour will be as we want it to be, but we cannot predict the evolution of our desires 5) The present confirms to a small extent the expectations of the past 6) Predictability in the field of labour is not protected by black swans: the evolution of artificial intelligence outlines the most important dimension of the extremistan 7) If the decision belongs to the human, there will be at least some areas where human labour will be preferred 8) The increase in the number of jobs and the decrease in their quality cannot be excluded 9) the progress can also lead to the increase of the number of jobs 10) The diminishing of social control over labour will persist. The arguments in favour of labour show that it will still exist, but we cannot be sure who and how it will be, or what status it will have.

Keywords: future of labour; prediction; Bayesian method; fitness argument. (search for similar items in EconPapers)
JEL-codes: A23 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:lum:rev3rl:v:9:y:2018:i:3:p:36-63

DOI: 10.18662/po/35

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