Gösterge Panelindeki Kırmızı Kraliçe: Algoritmik Kontrol ile İşçi Direnişinin Eş-Evrimsel Dinamikleri

Öz

Gig ekonomisi platformları işgücü arzını optimize etmek için giderek daha fazla algoritmik yönetime başvurdukça, işçiler de özerkliklerini yeniden kazanmak amacıyla daha sofistike karşı-stratejiler geliştirmektedir. Geleneksel mikroekonomik modeller bu etkileşimleri çoğu zaman statik vekil–asil (principal–agent) problemleri olarak ele almaktadır. Bu çalışma, algoritmik kontrol ile işçi davranışı arasındaki ilişkiyi, hiçbir tarafın statik ve kararlı bir dengeye yakınsamadığı bir eş-evrimsel “Kızıl Kraliçe” dinamiği çerçevesinde incelemek üzere Evrimsel Oyun Teorisini (EOT) kullanmaktadır. Modelde, işçiler uyum (compliance) ile algoritmik manipülasyon arasında seçim yaparken, platform gözetim katılığını ayarlamaktadır. Lotka–Volterra tipi bir replikatör yapısı altında iç denge noktası bir merkez (center) olarak karakterize edilmekte ve başlangıç koşullarına bağlı, yakınsamayan döngüsel yörüngeler ortaya çıkmaktadır. Bulgular, katı algoritmik kontrolün koordineli direnişin evrimsel uyumunu artırabildiğini ve iç merkez etrafında kapalı yörüngelerden oluşan kalıcı, nötr kararlı salınımsal dinamikler ürettiğini göstermektedir. Bu sonuçlar, “algoritmik sendikaların” aşırı optimize edilmiş yönetim sistemlerine karşı süregelen ve yakınsamayan platform–işçi etkileşimleri içinde uyum sağlayıcı bir tepki olarak kendiliğinden ortaya çıkabileceğine işaret etmektedir.

Anahtar Kelimeler

• Evrimsel Oyun Teorisi
• İktisat Teorisi
• Replikatör Dinamikleri

The Red Queen in the Dashboard: Co-Evolutionary Dynamics of Algorithmic Control and Worker Resistance

Öz

As gig economy platforms increasingly rely on algorithmic management to optimize labor supply, workers are developing sophisticated counter-strategies to regain autonomy. Conventional microeconomic models often treat these interactions as static principal–agent problems. This paper adopts an Evolutionary Game Theory framework to analyze the relationship between algorithmic control and worker behavior as a “Red Queen” dynamic—a co-evolutionary arms race in which the system does not converge to a stable static equilibrium. We model a population of workers choosing between compliance and algorithmic gaming (e.g., coordinated log-offs) against a platform that adjusts its surveillance strictness. Within a Lotka–Volterra–type replicator structure, the interior equilibrium is characterized as a center, generating path-dependent, non-convergent cyclical trajectories. We show that strict algorithmic control can increase the evolutionary fitness of coordinated resistance, producing persistent, neutrally stable oscillatory dynamics in the form of families of closed orbits around an interior center. These findings suggest that “algorithmic unions” may emerge organically as adaptive responses within ongoing, non-convergent platform–worker interactions.

Anahtar Kelimeler

• Evolutionary Game Theory
• Economic Theory
• Replicator Dynamics

Kaynakça

1. Acemoglu, D. and Restrepo, P. (2018). Artificial intelligence, automation and work (NBER Working Paper No. 24196). Retrieved from http://www.nber.org/papers/w24196
2. Acemoglu, D. and Restrepo, P. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488-1542. https://doi.org/10.1257/aer.20160696
3. Acemoglu, D. and Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716
4. Acemoglu, D. and Robinson, J.A. (2012). Why nations fail: The origins of power, prosperity, and poverty. New York: Random House.
5. Cameron, L. (2021). "Making out" while driving: Relational and efficiency games in the gig economy. Organization Science, 33(1), 231-252. https://doi.org/10.1287/orsc.2021.1547
6. Crawford, K. (2016). Can an algorithm be agnostic? Ten scenes from life in calculation. Science, Technology, & Human Values, 41(1), 77-92. https://doi.org/10.1177/0162243915589635
7. Dubal, V.B. (2017). Wage slave or entrepreneur? Contesting the dualism of legal worker identities. California Law Review, 105(1), 65-123. https://doi.org/10.15779/Z38M84X
8. Hall, J.V. and Krueger, A.B. (2018). An analysis of the labor market for Uber’s driver-partners in the United States. ILR Review, 71(3), 705-732. https://doi.org/10.1177/0019793917717222

9. Harris, S.D. and Krueger, A.B. (2015). A proposal for modernizing labor laws for twenty-first-century work. Washington DC: Brookings Institution.
10. Hofbauer, J. and Sigmund, K. (1998). Evolutionary games and population dynamics. Cambridge: Cambridge University Press.
11. Holmström, B. (1979). Moral hazard and observability. The Bell Journal of Economics, 10(1), 74-91. https://doi.org/10.2307/3003320
12. Kellogg, K.C., Valentine, M.A. and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
13. Khan, L.M. (2017). Amazon’s antitrust paradox. Yale Law Journal, 126(3), 710–805. Retrieved from http://www.jstor.org/
14. Möhlmann, M. (2015). Collaborative consumption: Determinants of satisfaction and the likelihood of using a sharing economy option. Journal of Consumer Services, 26, 193-205. https://doi.org/10.1002/cb.1512
15. North, D.C. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press.
16. Ostrom, E. (2005). Understanding institutional diversity. New Jersey: Princeton University Press.
17. Parker, G.G., Van Alstyne, M.W. and Choudary, S.P. (2016). Platform revolution: How networked markets are transforming the economy. New York: W. W. Norton & Company.
18. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Cambridge: Harvard University Press.
19. Rochet, J.C. and Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. https://doi.org/10.1162/154247603322493212
20. Rosenblat, A. and Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784. Retrieved from https://ijoc.org/
21. Samuelson, L. (1998). Evolutionary games and equilibrium selection. Cambridge: MIT Press.
22. Srnicek, N. (2016). Platform capitalism. Cambridge: Polity Press.
23. Taylor, P.D. and Jonker, L.B. (1978). Evolutionarily stable strategies and game dynamics. Mathematical Biosciences, 40(1-2), 145-156. https://doi.org/10.1016/0025-5564(78)90077-9
24. Van Valen, L. (1973). A new evolutionary law. Evolutionary Theory, 1, 1–30. Retrieved from https://www.mn.uio.no
25. Weibull, J.W. (1995). Evolutionary game theory. Cambridge: MIT Press.
26. Wood, A.J., Graham, M., Lehdonvirta, V. and Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56–75. https://doi.org/10.1177/0950017018785616