Skip to main content

Advertisement

SpringerLink
Log in

Applications of Artificial Intelligence in Social Science Issues: a Case Study on Predicting Population Change

  • Published:
Journal of the Knowledge Economy Aims and scope Submit manuscript

Abstract

One of the most important issues in recent years has been the issue of population aging and its effects on the economy. It is clear that aging leads to increased healthcare costs, decreased productivity, saving, investment, risk taking, etc.; finally, the economic growth will slow. On the other hand, it is necessary to address the issues of sustainable development, namely inequality, life expectancy, and green life for enhancing the quality of life. The application of artificial intelligence approaches such as machine learning (ML), artificial neural network (ANN), and deep learning (DL) can create condition that make the life easier for humans and make things easier. The aim of article is to pay attention to the importance of population aging and sustainable development goals in G20 countries and the potential application of artificial intelligence to increase the quality of life. Centralized programs can be considered to reduce the negative consequences of population aging and lack of attention to sustainable development goals. As a case study and in order to show the benefits of artificial intelligence, we have tried to predict the population changes in England. The main contribution of this article is that we have integrated the issue of sustainable development and aging problem in G20 countries in terms of theoretical and analytical study as a complementary method. We have tried to fill the gap between social science subjects such as aging and SDGs in G20 countries using AI-based potential applications. We used artificial neural network (ANN) and genetic algorithm (GA) as prediction methods. Some economic indicators such as GDP, inflation rate, and import are used as input variables. GA is used as feature selection and finding the most important variables. The results show that the rate of fertility is decreasing and the rate of aging is increasing. So, AI-based production and approaches can be impactful in achieving SDGs and improving elderly life. We can conclude that by investing and identifying potential threats, the effects of reduced economic growth and productivity can be prevented or reduced.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

Data Availability

All the sources used in this article are listed in the references section.

Code Availability

No code was used in this article.

References

  • Attia, Z. I., Friedman, P. A., Noseworthy, P. A., Lopez-Jimenez, F., Ladewig, D. J., Satam, G., Pellikka, P. A., Munger, T. M., Asirvatham, S. J., Scott, C. G., Carter, R. E., & Kapa, S. (2019). Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circulation: Arrhythmia and Electrophysiology, 12(9), e007284.

    Google Scholar 

  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.

    Article  Google Scholar 

  • Barnett, G. A., & Park, H. W. (2014). Examining the international internet using multiple measures: New methods for measuring the communication base of globalized cyberspace. Quality & quantity, 48(1), 563–575.

    Article  Google Scholar 

  • Barnett, G. A., Park, H. W., & Chung, C. J. (2016). Evolution of the international hyperlink network. Journal of Global Information Technology Management, 19(3), 174–189.

    Article  Google Scholar 

  • Bloom, D. E., Canning, D., & Lubet, A. (2015). Global population aging: Facts, challenges, solutions & perspectives. Daedalus, 144(2), 80–92.

    Article  Google Scholar 

  • Casey, B., Oxley, H., Whitehouse, E., Antolin, P., Duval, R., & Leibfritz, W. (2003). Policies for an ageing society: Recent measures and areas for further reform

  • Chen, Z. (2022). Artificial intelligence-virtual trainer: Innovative didactics aimed at personalized training needs. J Knowl Econ. https://doi.org/10.1007/s13132-022-00985-0

  • Choudrie, J., Zamani, E., & Obuekwe, C. (2021). Bridging the digital divide in ethnic minority older adults: An organisational qualitative study. Information Systems Frontiers, 24, 1–21.

    Google Scholar 

  • Din, S. U., Khan, M. Y., Khan, M. J., & Nilofar, M. (2021). Nexus between sustainable development, adjusted net saving, economic growth, and financial development in South Asian emerging economies. Journal of the Knowledge Economy, 13, 1–14.

    Google Scholar 

  • Di Vaio, A., Palladino, R., Hassan, R., & Escobar, O. (2020). Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review. Journal of Business Research, 121, 283–314.

    Article  Google Scholar 

  • Dieleman, J. L., Cowling, K., Agyepong, I. A., Alkenbrack, S., Bollyky, T. J., Bump, J. B., Chen, C. S., Grépin, K. A., Haakenstad, A., Harle, A. C., Kates, J., & Murray, C. J. (2019). The G20 and development assistance for health: Historical trends and crucial questions to inform a new era. The Lancet, 394(10193), 173–183.

    Article  Google Scholar 

  • Dyakova, M. (2017). Investment for health and well-being: A review of the social return on investment from public health policies to support implementing the Sustainable Development Goals by building on Health 2020

  • Fritz, R. L., & Dermody, G. (2019). A nurse-driven method for developing artificial intelligence in “smart” homes for aging-in-place. Nursing Outlook, 67(2), 140–153.

    Article  Google Scholar 

  • Gong, M., Ren, M., Dai, Q., & Luo, X. (2019). Aging-suitability of urban waterfront open spaces in Gongchen Bridge section of the Grand Canal. Sustainability, 11(21), 6095.

    Article  Google Scholar 

  • Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.

    Article  Google Scholar 

  • He, W., Goodkind, D., & Kowal, P. R. (2016). An aging world: 2015.

  • Ishikawa, H., & Yano, E. (2008). Patient health literacy and participation in the health-care process. Health Expectations, 11(2), 113–122.

    Article  Google Scholar 

  • Jakovljevic, M., Timofeyev, Y., Ranabhat, C. L., Fernandes, P. O., Teixeira, J. P., Rancic, N., & Reshetnikov, V. (2020). Real GDP growth rates and healthcare spending–comparison between the G7 and the EM7 countries. Globalization and Health, 16(1), 1–13.

    Article  Google Scholar 

  • Kanfer, R., & Ackerman, P. L. (2004). Aging, adult development, and work motivation. Academy of Management Review, 29(3), 440–458.

    Article  Google Scholar 

  • Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126.

    Article  Google Scholar 

  • Kloke-Lesch, A. (2015). The G20 and the Sustainable Development Goals (SDGs): Reflections on future roles and tasks. In Chongyang Institute for Financial Studies (Ed.), G20 and global governance: blue book of G20 Think Tank 2016 (pp. 55–71).

    Google Scholar 

  • Lafortune, G., Fuller, G., Moreno, J., Schmidt-Traub, G., & Kroll, C. (2018). SDG index and dashboards detailed methodological paper. Retrieved, 1, 2018

  • Maity, S., & Sinha, A. (2021). Linkages between economic growth and population ageing with a knowledge spillover effect. Journal of the Knowledge Economy, 12(4), 1905–1924.

    Article  Google Scholar 

  • Ma, Q., Chan, A. H., & Teh, P. L. (2020). Bridging the digital divide for older adults via observational training: Effects of model identity from a generational perspective. Sustainability, 12(11), 4555.

    Article  Google Scholar 

  • Mathers, C. D., & Loncar, D. (2006). Projections of global mortality and burden of disease from 2002 to 2030. PLoS Medicine, 3(11), e442.

    Article  Google Scholar 

  • McBride, B., Hawkes, S., & Buse, K. (2019). Soft power and global health: The sustainable development goals (SDGs) era health agendas of the G7, G20 and BRICS. BMC Public Health, 19(1), 1–14.

    Article  Google Scholar 

  • Monden, C. W., Van Lenthe, F., De Graaf, N. D., & Kraaykamp, G. (2003). Partner’s and own education: Does who you live with matter for self-assessed health, smoking and excessive alcohol consumption? Social Science & Medicine, 57(10), 1901–1912.

    Article  Google Scholar 

  • Quartey, S. H. (2019). Geographies of knowledge and sustainable development: Towards a conceptual model with research propositions. Journal of the Knowledge Economy, 10(2), 878–897.

    Article  Google Scholar 

  • Sapci, A. H., & Sapci, H. A. (2019). Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: systematic review. JMIR Aging, 2(2), e15429.

    Article  Google Scholar 

  • Schehl, B., Leukel, J., & Sugumaran, V. (2019). Understanding differentiated Internet use in older adults: A study of informational, social, and instrumental online activities. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2019.03.031

  • Schmidt-Traub, G., Kroll, C., Teksoz, K., Durand-Delacre, D., & Sachs, J. D. (2017). National baselines for the sustainable development goals assessed in the SDG index and dashboards. Nature Geoscience, 10(8), 547–555.

    Article  Google Scholar 

  • Shankar, A., McMunn, A., Banks, J., & Steptoe, A. (2011). Loneliness, social isolation, and behavioral and biological health indicators in older adults. Health Psychology, 30(4), 377.

    Article  Google Scholar 

  • Shlisky, J., Bloom, D. E., Beaudreault, A. R., Tucker, K. L., Keller, H. H., Freund-Levi, Y., Fielding, R. A., Cheng, F. W., Jensen, G. L., Wu, D., & Meydani, S. N. (2017). Nutritional considerations for healthy aging and reduction in age-related chronic disease. Advances in Nutrition, 8(1), 17.

    Article  Google Scholar 

  • Stewart, M., Redonda, A., Galassao, V., Mazur, M., & Whittaker, M. (2020). Taxation in aging societies: Increasing the effectiveness and fairness of pension systems

  • Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 1–10.

    Article  Google Scholar 

  • World Health Organization. (2016). World health statistics 2016: Monitoring health for the SDGs sustainable development goals. World Health Organization.

    Google Scholar 

  • Yang, X., Li, N., Mu, H., Ahmad, M., & Meng, X. (2022). Population aging, renewable energy budgets and environmental sustainability: Does health expenditures matter? Gondwana Research, 106, 303–314.

    Article  Google Scholar 

  • Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019). Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Research Reviews, 49, 49–66.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Finance, Faculty of Management, Khatam University, Tehran, Iran

    Milad Shahvaroughi Farahani

Authors
  1. Milad Shahvaroughi Farahani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All of the contents and idea belong to the corresponding author.

Corresponding author

Correspondence to Milad Shahvaroughi Farahani.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Conflict of interest

The author declares no competing interests.

Employment

Any organization or employment would not gain or loss financially through publication of this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 10 and 11 and Figs. 26 and 27.

Table 10 SDGs
Full size table
Table 11 G20 principles on silver economy and active aging
Full size table
Fig. 26
figure 26

Training state

Full size image
Fig. 27
figure 27

Error histogram

Full size image

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farahani, M.S. Applications of Artificial Intelligence in Social Science Issues: a Case Study on Predicting Population Change. J Knowl Econ (2023). https://doi.org/10.1007/s13132-023-01270-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13132-023-01270-4

Keywords

JEL classification

Search

Navigation