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The paper offers a formal state-transition framework for the tension between player agency and narrative control in interactive stories, with quantitative indicators for comparing Experience Manager policies. Its value is in making a widely discussed design trade-off testable through simulation, though the case study's scope and how well the indicators generalise to real games are open questions. For AI readers, it is a planning-style formalisation of AI-driven storytelling rather than a new model or product.
Analysing the Narrative Paradox through Formal Models and Properties in Interactive Narrative
arXiv:2611.02801v1 Announce Type: new Abstract: Interactive narratives aim to let players influence the unfolding of a story while preserving a coherent and satisfying narrative progression. This creates a central tension, commonly described as the narrative paradox, between player autonomy and narrative control. Experience Managers (EMs) manage the story by determining how the narrative progresses in response to player behaviour. Although this paradox is widely discussed, it is rarely modelled in a way that supports systematic analysis, simulation, and comparison across management strategies. This paper proposes a formal framework for modelling and analysing the narrative paradox as a trade-off between interdependent properties shaped by EM decisions. We represent interactive narrative behaviour through an extended state-transition system in which world states are connected by player actions and system events. From this model, we formalize key properties involved in the paradox such as the player agency, EM controllability, author intent, and robustness, and define quantitative indicators derived from simulated plans. These indicators make it possible to compare EM strategies according to their ability to balance freedom, control, and narrative satisfaction. A case study compares different EM policies, showing how simulations can reveal correlations and conflicts between properties and identify compromise zones in the design space. This work contributes a reusable modelling and analysis framework for studying the narrative paradox and evaluating heterogeneous interactive narratives on shared formal grounds, with a focus on supporting the comparison and testing of EM strategies.
Abstract:Interactive narratives aim to let players influence the unfolding of a story while preserving a coherent and satisfying narrative progression. This creates a central tension, commonly described as the narrative paradox, between player autonomy and narrative control. Experience Managers (EMs) manage the story by determining how the narrative progresses in response to player behaviour. Although this paradox is widely discussed, it is rarely modelled in a way that supports systematic analysis, simulation, and comparison across management strategies. This paper proposes a formal framework for modelling and analysing the narrative paradox as a trade-off between interdependent properties shaped by EM decisions. We represent interactive narrative behaviour through an extended state-transition system in which world states are connected by player actions and system events. From this model, we formalize key properties involved in the paradox such as the player agency, EM controllability, author intent, and robustness, and define quantitative indicators derived from simulated plans. These indicators make it possible to compare EM strategies according to their ability to balance freedom, control, and narrative satisfaction. A case study compares different EM policies, showing how simulations can reveal correlations and conflicts between properties and identify compromise zones in the design space. This work contributes a reusable modelling and analysis framework for studying the narrative paradox and evaluating heterogeneous interactive narratives on shared formal grounds, with a focus on supporting the comparison and testing of EM strategies.
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2611.02801 [cs.AI]
(or arXiv:2611.02801v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.02801 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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