Samothrakis, Spyridon (2021) Artificial Intelligence inspired methods for the allocation of common goods and services. PLoS One, 16 (9). e0257399-e0257399. DOI https://doi.org/10.1371/journal.pone.0257399
Samothrakis, Spyridon (2021) Artificial Intelligence inspired methods for the allocation of common goods and services. PLoS One, 16 (9). e0257399-e0257399. DOI https://doi.org/10.1371/journal.pone.0257399
Samothrakis, Spyridon (2021) Artificial Intelligence inspired methods for the allocation of common goods and services. PLoS One, 16 (9). e0257399-e0257399. DOI https://doi.org/10.1371/journal.pone.0257399
Abstract
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Humans; Models, Economic; Developing Countries; Communism; Public Sector; Social Justice; History, 20th Century; Artificial Intelligence; Delivery of Health Care; USSR |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 10 Nov 2021 17:04 |
Last Modified: | 07 Aug 2024 18:29 |
URI: | http://repository.essex.ac.uk/id/eprint/31481 |
Available files
Filename: journal.pone.0257399.pdf
Licence: Creative Commons: Attribution 3.0