Beyond Efficiency: Artificial Intelligence as a Driver for Resilient Market Organisations

Enrico Moch

doi:10.37284/eajis.8.1.3178

Enrico Moch, PhD GrandEdu Research School

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Résumé

Artificial intelligence is increasingly becoming a regulatory factor. While efficiency has long been regarded as the guiding principle of economic systems, crises show the vulnerability of highly optimised structures. This paper examines the conditions under which AI contributes to the resilience of market organisations, understood as the ability to deal productively with uncertainty. Theoretical perspectives by Hayek and Taleb emphasise decentralised knowledge processing and antifragile learning processes. Empirical case studies from platform markets and critical infrastructure illustrate that AI either increases efficiency or enables stability, depending on how it is institutionally embedded. In platform markets, short-term optimisation dominates, while infrastructure systems rely on redundancy, scenario diversity and human correction capability. The results show that resilience does not come from technology but from system architecture. AI can become an adaptive module in dynamic markets if uncertainty is understood as a structural condition rather than an error

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Références

Arner, D. W., Auer, R., & Frost, J. (2020). Stablecoins: risks, potential and regulation. Bank for International Settlements Working Papers, No. 905. https://www.bis.org/publ/work905.htm

Brennen, S., Howard, P. N. & Nielsen, R. K. (2020). An automated public: Algorithmic bots, fake news, and the future of public discourse. Oxford: Oxford Internet Institute.

Brynjolfsson, E. & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W. W. Norton & Company.

Chen, V., Liao, Q. V., Vaughan, J. W. & Bansal, G. (2023). Understanding the role of human intuition on reliance in human-AI decision-making with explanations. arXiv preprint. Available at: https://arxiv.org/abs/2301.07255 [Accessed 2 June 2025].

Fama, E.F. (1970): Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), pp.383-417.

Gai, P., Haldane, A. & Kapadia, S. (2011). Complexity, concentration and contagion. Journal of Monetary Economics, 58(5), pp.453-470. https://doi.org/10.1016/j.jmoneco.2011.05.005

Haldane, A. G. & May, R. M. (2011). Systemic risk in banking ecosystems. Nature, 469(7330), pp.351- 355. https://doi.org/10.1038/nature09659

Hayek, F. A. (1945). The Use of Knowledge in Society. The American Economic Review, 35(4), pp.519-530. Available at: https://www.jstor.org/stable/1809376 [Accessed 2 June 2025].

Holling, C.S. (2001): Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4(5), pp. 390-405. https://doi.org/10.1007/s10021-001-0101-5

Ivanov, D. (2020). Viable supply chain model: Integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 316, 1–21. https://doi.org/10.1007/s10479-020-03640-6

Ilcic, A., Fuentes, M. & Lawler, D. (2025). Artificial intelligence, complexity, and systemic resilience in global governance. Frontiers in Artificial Intelligence, 8, 1562095. Available at: https://doi.org/10.3389/frai.2025.1562095 (accessed 2 June 2025).

Krugman, P. and Wells, R. (2018): Economics. 6th ed. Stuttgart: Schäffer-Poeschel.

Kuiper, O., van den Berg, M., van der Burgt, J. & Leijnen, S. (2021). Exploring explainable AI in the financial sector: Perspectives of banks and supervisory authorities. arXiv preprint. Available at: https://arxiv.org/abs/2111.02244 [Accessed 2 June 2025].

Linkov, I., Eisenberg, D. A., Plourde, K., Seager, T. P., Allen, J. & Kott, A. (2014). Resilience metrics for cyber systems. Environment Systems and Decisions, 34, pp.451-464.

Mayring, P. (2014). Qualitative content analysis: theoretical foundation, basic procedures and software solution. Klagenfurt: Beltz.

Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., et al. (2019). Machine behaviour. Nature, 568(7753), pp.477-486. https://doi.org/10.1038/s41586-019-1138-y

Schröder, M., Storch, D.-M., Marszal, P. & Timme, M. (2020). Anomalous supply shortages from dynamic pricing in on-demand mobility. Nature Communications, 11, 4831. Available at: https://doi.org/10.1038/s41467-020-18370-3 (accessed on 2 June 2025)

Taleb, N. N. (2010). The Black Swan: The Impact of the Highly Improbable. London: Penguin Books.

Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. New York: Random House.

Varian, H.R. (2014): Microeconomic Analysis. 3rd ed. New York: W. W. Norton & Company.