Artificial Intelligence and the Future of the Labour Economy:A Multi-Criteria Expert Evaluation of Institutional Models of Adaptation

Jurica Bosna; Adis Puška; Darko Božanić

doi:10.31181/jscda41202683

Authors

Jurica Bosna Department of Economics, University of Zadar, Zadar, Croatia Author https://orcid.org/0000-0002-7633-4862
Adis Puška Department of Public Safety, Government of Brčko District of Bosnia and Herzegovina, Brčko, Bosnia and Herzegovina Author https://orcid.org/0000-0003-3274-0188
Darko Božanić Military Academy, University of Defence in Belgrade, Belgrade, Serbia Author https://orcid.org/0000-0002-9657-0889

DOI:

https://doi.org/10.31181/jscda41202683

Keywords:

Artificial intelligence (AI), Models of institutional adjustment, Labour market, Fuzzy-rough approach, SiWeC, WASPAS

Abstract

This study evaluates institutional models for adapting labour markets to the growing adoption of automation and artificial intelligence (AI). As AI continues to transform labour markets, identifying effective institutional responses has become increasingly important. Six institutional adjustment models were assessed against eight evaluation criteria using a hybrid fuzzy-rough decision-making framework that integrates the SiWeC (Simple Weight Calculation) and WASPAS (Weighted Aggregated Sum Product Assessment) methods based on expert judgments. The proposed approach explicitly incorporates uncertainty and imprecision into the evaluation process, thereby reducing subjectivity in decision-making. The results indicate that innovation and productivity incentives and institutional feasibility are the most influential evaluation criteria. A case study conducted in the Republic of Croatia further shows that the mass retraining and participatory AI capital models provide the most suitable institutional responses. The study contributes both methodologically, by proposing a robust fuzzy-rough evaluation framework, and practically, by supporting evidence-based policy decisions for labour market adaptation in the era of AI.

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