  Economic issues, such as inflation, energy costs, taxes, and interest rates,
are a constant presence in our daily lives and have been exacerbated by global
events such as pandemics, environmental disasters, and wars. A sustained
history of financial crises reveals significant weaknesses and vulnerabilities
in the foundations of modern economies. Another significant issue currently is
people quitting their jobs in large numbers. Moreover, many organizations have
a diverse workforce comprising multiple generations posing new challenges.
Transformative approaches in economics and labour markets are needed to protect
our societies, economies, and planet. In this work, we use big data and machine
learning methods to discover multi-perspective parameters for
multi-generational labour markets. The parameters for the academic perspective
are discovered using 35,000 article abstracts from the Web of Science for the
period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn
posts from 2022. We discover a total of 28 parameters and categorised them into
5 macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries,
Learning & Employment Issues, and Generations-specific Issues. A complete
machine learning software tool is developed for data-driven parameter
discovery. A variety of quantitative and visualisation methods are applied and
multiple taxonomies are extracted to explore multi-generational labour markets.
A knowledge structure and literature review of multi-generational labour
markets using over 100 research articles is provided. It is expected that this
work will enhance the theory and practice of AI-based methods for knowledge
discovery and system parameter discovery to develop autonomous capabilities and
systems and promote novel approaches to labour economics and markets, leading
to the development of sustainable societies and economies.
