Playing with Persiflage: How Free-Form Dialogue Enables Emergent Narratives in Orchestrated Digital Games
Bibliographic record
Abstract
Digital games struggle to blend compelling narrative with interactivity. For example, computer role-playing games allow players the freedom to explore an open world, yet limit their interaction with the world’s inhabitants to selecting from pre-determined dialogue choices. This constrains players’ interactions and the game’s narrative to those conceived in advance by the game’s designers. We show how game orchestration enables a human to play the role of non-player characters, expanding interactive narrative through truly open-ended conversation. This idea is concretely realized in the novel game Persiflage. Through a study of five groups playing the game, we show how players and orchestrator converse, interact, and play using natural language. Players engage deeply in the game’s story, but do not adhere rigidly to the game’s setting, liberally employing slang and anachronistic references. Orchestrators accommodate the players but themselves play truer to the game’s setting, leading to asymmetric dialogue. Groups exhibited a range of dominance structures that affect the fluidity of the gameplay. Players and orchestrators use open ended conversations to collaboratively construct a narrative that emerges through gameplay. Orchestrators take the lead in authoring the story by preparing the game world for the players to explore. As the game progresses players make contributions and authorial control shifts between players and orchestrators. We show that orchestrated computer roleplaying games are an enjoyable outlet for players to exercise their creativity and that interesting and complex narratives can emerge from their play.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".