Data driven computational models for prediction and simulation of path dependencies in complex dynamic labour market systems

It will achieve this through the use of novel nature-inspired Computational Intelligence (CI) and agent based modelling techniques which, when combined with relevant large multi-source datasets, will be used to model complex socio-economic conditions that affect labour mobility and diversification especially in the private sector.

The project aims to support policy makers and stakeholders in making more effective policy design decisions as part of the current 2020 National Transformation Program. This will include an intelligent modelling tool that will construct virtual labour markets. Policy makers will be able to use this tool to simulate the impact and effects of policy strategies before implementing them in to government policy.

Read the project abstract (.PDF) to find out more.

Outcomes

D. Alves, F. Doctor, R. Iqbal and A. Kattan “A Soft Computing Methodology based on Fuzzy Measures and Integrals for Ranking Workers Informing Labour Hiring Policies”, 20th Annual International Conference on Digital Government Research, Dubai, UAE, June 2019, pp. 117-122

Policy insight - Who will be hired, Saudis or Expats? (.PDF)

Policy Insight - Who enters and exits the Saudi labour market? (.PDF)

Partners and funding

The project is in collaboration with the Ministry of Labour and Social Development (MLSD), and is funded through Evidence for Policy Design at the Harvard Kennedy School, USA and Human Resources Development Fund (HRDF) of the Kingdom of Saudi Arabia (KSA).