The Nash Equilibrium of Creative Labor: Game-Theoretic Approaches To Human–Ai Negotiation in Film Production

Main Article Content

B. Uma
E.Ramesh Babu
Vinothini V R
Santosh Kumar S
B. Senthilnayaki
Manoj Raghunath Kharde
A. Vinayagamoorthy
Suganya Baby.N
T. Vengatesh
C Vijayalakshmi
M. Mageshwari

Abstract

The increasing integration of artificial intelligence (AI) into film production is transforming creative labor, production costs, decision-making authority, and the distribution of economic value. AI systems can contribute to script development, visual effects, editing, storyboarding, sound design, and other production activities, yet their adoption introduces conflicts concerning creative control, compensation, attribution, accountability, and employment. Existing approaches frequently examine AI adoption through technological efficiency, legal compliance, or ethical principles without providing a unified mathematical model of negotiation between human creative professionals and AI-enabled production organizations.This paper proposes the Creative Labor Negotiation Equilibrium (CLNE) framework, a game-theoretic approach to modeling strategic interaction among filmmakers, production companies, and AI service providers. The framework represents creative control, labor compensation, attribution, disclosure, production efficiency, and human oversight as interdependent negotiation variables. A constrained bargaining model is developed by combining Nash bargaining theory with a non-cooperative game formulation and explicit fairness constraints. The model aims to identify agreements that improve production efficiency without treating human creative contributions as interchangeable with automated output. A multi-objective social-welfare function is proposed to evaluate negotiated outcomes in terms of production cost, creative quality, worker welfare, transparency, and perceived procedural fairness. The paper outlines hypotheses and a reproducible empirical evaluation strategy using simulated negotiations and, where feasible, anonymized interviews or production-case data. The proposed framework offers a formal basis for studying when cooperation between human creators and AI systems can produce stable, economically viable, and ethically defensible agreements.

Article Details

How to Cite
Uma, B., Babu, E., V R, V., Kumar S, S., Senthilnayaki, B., Kharde, M. R., … Mageshwari, M. (2026). The Nash Equilibrium of Creative Labor: Game-Theoretic Approaches To Human–Ai Negotiation in Film Production. CINEFORUM, 66(S5), 883–891. https://doi.org/10.66669/cineforum.v66iS5.2030
Section
Original Articles

References

Bender, S. (2025). Generative-AI, the media industries, and the disappearance of human creative labour. Media Practice and Education, 26(2), 200–217. https://doi.org/10.1080/25741136.2024.2355597

Celis Bueno, C., & Salvador-Mata, B. (2026). Creative labour and generative AI: A typology of asymmetrical relations. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03267-4

Holt, C. A., & Roth, A. E. (2004). The Nash equilibrium: A perspective. Proceedings of the National Academy of Sciences, 101(12), 3999–4002. https://doi.org/10.1073/pnas.0308738101

Nash, J. F. (1950). Equilibrium points in n-person games. Proceedings of the National Academy of Sciences, 36(1), 48–49. https://doi.org/10.1073/pnas.36.1.48

Nash, J. F. (1950). The bargaining problem. Econometrica, 18(2), 155–162. https://doi.org/10.2307/1907266

Nash, J. F. (1953). Two-person cooperative games. Econometrica, 21(1), 128–140.

Petre, C., & Ticona, J. (2026). From motorized projectors to generative AI: Cultural conflicts over labor automation in Hollywood. AI & SOCIETY, 41, 9379–9394. https://doi.org/10.1007/s00146-026-03167-7

Thomas, S. (2024). “Somebodies” and “Nobodies”: Generative AI and audiovisual performer labor. The Velvet Light Trap, 94, 64–75. https://doi.org/10.7560/vlt9407

Huang, J. (2026). Self-programmable creative labour: Generative AI and the reorganisation of work in the UK creative industries. Media, Culture & Society. https://doi.org/10.1177/01634437261494891

Huang, J., Hitchen, G., & Dogan, S. (2026). Drawing boundaries: How creative practitioners navigate precarity in AI-embedded creative work. Convergence. https://doi.org/10.1177/13548565261494818