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Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt

  • Fenintsoa AndriamasinoroEmail author
  • Raphael Danino-Perraud
Original Paper
  • 39 Downloads

Abstract

The French public and commercial stakeholders need prospective tools to follow how mineral substances criticality change in the French market. After arguing that such tools should necessarily tackle criticality at a complex level, in particular on multiple scales (e.g., France and the EU), we present the first thematic and methodological discussions of our results from the ongoing design of a methodologically based simulation model on two subfields of artificial intelligence: agent-based computational economics (ACE) and machine learning (ML). In applying this to cobalt, our model aims to assess a supply shortage in France for prospective purposes. More precisely, we model a first individual agent (which is already complex by itself) acting at a country level: France. This model is not yet an ACE model per se since only one agent is designed. Nonetheless, we include ACE in the discussions since the work is a premise of such an end. The discussions also include how well the field accepts the methodology. At a thematic level, our preliminary prospective conclusion is a French cobalt supply shortage, should the case arise, would not be due to the variation of price from the UK, the transit leader of cobalt export to France. At a methodological level, we think the idea of methodologically coupling ML and ACE is necessary. ML is well-known in this field, but mainly for the study of mineral prospectivity in mining. Conversely, ACE covers the value chain but is not yet well known in the field and as such is still not trusted.

Keywords

Mineral raw material criticality Cobalt Machine learning Agent-based computational economics 

JEL

C63 C82 E17 F10 

Notes

Authors’ contributions

R. Danino-Perraud is a PhD student in economics. His research interest is the analysis of mineral raw materials criticality and in particular cobalt criticality at European and France scales, by using Material Flow Analysis approach (MFA). This work was however not a part of his thesis work. It was a parallel work in which his contribution and expertise were requested.

F. Andriamasinoro is a research scientist in mineral commodities market modeling and simulation by using AI approach (agent-based computational Economics, Machine learning).

All authors contributed to the description of the section “Introduction and background” of the paper (context and current state) based on their respective competences. Model description, implementation and analysis were carried out by F. Andriamasinoro under a control of R. Danino-Perraud regarding the thematic consistency of the model. All discussions related to the AI methodology in the paper were written by F. Andriamasinoro. The MFA sub-section of the discussion section was written by R. Danino-Perraud. R. Danino-Perraud also critically revised the whole work according to his knowledge of mineral raw material criticality issues. All authors read and approved the final manuscript.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.BRGMOrléans Cedex 2France
  2. 2.University of Orléans/Laboratoire Économique d’Orléans (LEO)Orléans Cedex 2France

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