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DiGRA
This paper examines the "illusion of choice" strategy in story-based videogames, where developers present player decisions that appear consequential without substantially altering the narrative. The author frames the problem as a dilemma inherent to hybrid media: players want meaningful choices, yet branching narratives are costly to produce with animated graphics and are often viewed by developers as an inefficient use of resources. The study draws on videogame theory, narratology, and discourse analysis to address two central questions, namely what methods games use to signal narrative consequence and whether those methods succeed in persuading players that their actions matter.
The paper distinguishes between internal and external strategies for creating this impression. Internal methods include reviewing past in-game events, mechanics explicitly tied to narrative choice, and direct communication with players about the outcomes of their actions. External methods encompass promotional material and advertising that frame a game as highly responsive to player decisions. The analysis is intended to show how these techniques shape player expectations about interactivity.
To assess effectiveness, the author analyzes a corpus of published videogame reviews to identify which narrative elements reviewers treat as central when judging whether player choices are meaningful. The findings point to substantial disagreement among players about how much influence certain games actually grant over narrative outcomes. The paper also identifies unintended consequences of illusion-of-choice design, noting that strategies meant to foster perceived agency can alter player expectations in ways that ultimately undermine the sense of interactivity.
The scope of the work is centered on narrative-driven games with animated graphics, with examples such as Until Dawn and The Walking Dead, and it was presented as an extended abstract at the Chinese DiGRA 2018 conference. The text is a conference-length abstract, so its empirical findings are described at a high level rather than in full statistical detail.
DiGRA · 2026
DiGRA · 2025