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

Year 2026, Volume: 11 Issue: 1 , 140 - 159 , 31.03.2026

Aras Yolusever

https://doi.org/10.30784/epfad.1829094

https://izlik.org/JA38DX47SA

Abstract

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.

Keywords

Evrimsel Oyun Teorisi , İktisat Teorisi , Replikatör Dinamikleri

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

https://izlik.org/JA38DX47SA

Abstract

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.

Keywords

Evolutionary Game Theory , Economic Theory , Replicator Dynamics

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