Artificial Intelligence inspired methods for the allocation of common goods and services

Artificial Intelligence inspired methods for the allocation of common goods and services

The debate over the optimal way of allocating societal surplus (i.e. products and services) has been raging, in one form or another, practically forever; following the collapse of the Soviet Union in 1991, the market has taken the lead vs the public sector to do this. Working within the tradition of...

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Bibliographic Details
Published in:PloS one 2021-09, Vol.16 (9), p.e0257399-e0257399
Main Author: Samothrakis, Spyridon
Format: Article
Language:eng
Subjects:
Artificial Intelligence
Automation
Biology and Life Sciences
Communism
Computer and Information Sciences
Delivery of Health Care
Developing Countries
Engineering and Technology
Evaluation
Forecasts and trends
History, 20th Century
Humans
Medicine and Health Sciences
Methods
Models, Economic
Physical Sciences
Privatization
Production planning
Public Sector
Research and Analysis Methods
Social Justice
Social Sciences
Socialism
USSR
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Summary:The debate over the optimal way of allocating societal surplus (i.e. products and services) has been raging, in one form or another, practically forever; following the collapse of the Soviet Union in 1991, the market has taken the lead vs the public sector to do this. Working within the tradition of Marx, Leontief, Beer and Cockshott, we propose what we deem an automated planning system that aims to operate on unit level (e.g., factories and citizens), rather than on aggregate demand and sectors. We explain why it is both a viable and desirable alternative to current market conditions and position our solution within current societal structures. Our experiments show that it would be trivial to plan for up to 50K industrial goods and 5K final goods in commodity hardware. Our approach bridges the gap between traditional planning methods and modern AI planning, opening up venues for further research.
ISSN:1932-6203
1932-6203