Narrative Gold Digging: Sifting and Narrating Stories from a Procedural Simulation
TL;DR
An experiment in formalizing and implementing “story-sifting” heuristics to automatically identify likely “interesting stories” from a large mass of events transpiring from the Chroniqueur procedural social simulation results in inconclusive results about heuristics making any significant difference.
Abstract
This paper reports on an experiment in formalizing and implementing “story-sifting” heuristics to automatically identify likely “interesting stories” from a large mass of events transpiring from the Chroniqueur procedural social simulation. Following preliminary experimentation with various concepts inspired by narratology, we proceeded to formally evaluate “unexpectedness” and “dramatic situations” as key heuristics to sample six stories from a simulation run and rendered each as two short stories, one written by a human author and the other by a LLM (in order to help distinguish the effect of narrative from story). These were scored by 24 participants for narrative appreciation. Inconclusive results about heuristics making any significant difference prompted self-reflection on what makes emergent narratives interesting (in a distinct way from traditional fiction), pointing both at “story effects” (some selected event chains constituting better story material) and “narrative effect” (better narrativization being able to cope with poor story material).