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Player behavior in games is treated as a raw asset, extracted, predicted, and monetized through algorithmic modeling.
The analysis explores the intersection of video game design and artificial intelligence ethics, positing that games serve as critical lenses for understanding contemporary data politics and surveillance. By examining how digital environments simulate and normalize monitoring, the research argues that the mechanisms of control found in fictional game worlds increasingly mirror the real-world architectures of surveillance capitalism. The study highlights a shift from traditional disciplinary surveillance, characterized by Foucault’s panoptic models, toward Deleuzian societies of control, where continuous, networked modulation and predictive modeling dictate user behavior.
Through case studies of titles such as Little Nightmares, The Sims, Cobra Club, and Watch Dogs, the research demonstrates how game mechanics function as "allegorithms"—systems that enact arguments about power, consent, and privacy through play. These games reveal how behavioral tracking and adaptive feedback loops are used to extract data, nudge player actions, and maintain predictable systems. The analysis emphasizes that these simulated environments often replicate the same exploitative logics found in broader digital culture, where user activity is treated as an asset to be harvested for economic and sociopolitical manipulation.
Ultimately, the research advocates for a procedural ethics of transparency in game development. It concludes that by critically engaging with the systems that constrain player agency, the industry can move toward more ethical AI implementations. The findings suggest that recognizing the convergence between fictional surveillance and real-world technological governance is essential for fostering player trust and addressing the broader implications of pervasive data extraction in modern digital life.
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