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.

翻译：经济问题如通胀、能源成本、税收和利率持续存在于我们的日常生活中，并因疫情、环境灾害和战争等全球事件而加剧。长期金融危机史揭示了现代经济体系根基中的显著弱点和脆弱性。另一个当前重大问题是大量员工离职潮。此外，许多组织拥有由多代际构成的多元化劳动力队伍，这带来了新的挑战。我们需要在经济学和劳动力市场领域采取变革性方法，以保护我们的社会、经济和地球。本研究利用大数据和机器学习方法，为多代际劳动力市场发现多视角参数。学术视角的参数通过Web of Science中1958-2022年间35,000篇论文摘要发现，从业者视角的参数则基于2022年57,000条领英帖子。我们共发现28个参数，并将其归类为5个宏观参数：学习与技能、就业行业、消费产业、学习与就业问题、代际特定问题。我们开发了一套完整的数据驱动参数发现机器学习软件工具，应用了多种定量分析与可视化方法，并提取出多个分类体系以探索多代际劳动力市场。基于100余篇研究论文，本文提供了多代际劳动力市场的知识结构与文献综述。预期本研究将增强基于人工智能的知识发现与系统参数发现的理论与实践，助力开发自主能力与系统，推动劳动力经济学与市场领域的创新方法，最终促进可持续社会与经济的发展。