An Embodied, Affect-Driven Multi-Agent Social Simulation as a Log Generator for Computational Narrative Tasks

Computational narrative—the automatic sifting, composition and interpretation of stories—needs raw material: event logs rich enough to yield tellable stories. Social simulations are an attractive source but only if their logs are embodied, eventful and interconnected so that concurrent threads share the coordinates a telling must weave. We present a dependency-light multi-agent social simulation built to that specification as a synthetic log generator for downstream narrative tasks. Its distinguishing feature is that perception follows from position: agents encounter one another by co-location, and every event records the set of agents present to witness it. That explicit, queryable who-perceived-what relation—left disembodied or implicit by prior simulations—is what focalised composition needs, and recovering it is the gap this work addresses. Agents negotiate shared activities and exclusive relationships directly, driven by affect and per-agent temperament. Run in a calibrated, seeded free mode, social structure—couples, jealousy, affairs, break-ups—emerges unscripted from local interaction: across 150 random casts the structural invariants hold in every run, while dramatic outcomes vary widely from seed to seed, and a disjoint held-out population of 150 fresh seeds reproduces every calibrated band. A narrative-interest diagnostic instruments the logs, separating routine from active runs and reading off which dramatic configurations occur.