Title: Multiverse Mechanica: A Testbed for Learning Game Mechanics via Counterfactual Worlds
Primary area: causal reasoning
Keywords: Causal generative modelling, Game generative AI models, Counterfactual reasoning

Abstract: We study how generative world models trained on video games can go beyond mere reproduction of gameplay visuals to learning game mechanics—the modular rules that causally govern gameplay. We introduce a formalization of the concept of game mechanics that operationalizes mechanic-learning as a causal counterfactual inference task and uses the causal consistency principle to address the challenge of generating gameplay with world models that do not violate game rules. We present Multiverse Mechanica, a playable video game testbed that implements a set of ground truth game mechanics based on our causal formalism. The game natively emits training data, where each training example is paired with a set of causal DAGs that encode causality, consistency, and counterfactual dependence specific to the mechanic that is in play—these provide additional artifacts that could be leveraged in mechanic-learning experiments. We provide a proof-of-concept that demonstrates fine-tuning a pre-trained model that targets mechanic learning. Multiverse Mechanica is a testbed that provides a reproducible, low-cost path for studying and comparing methods that aim to learn game mechanics—not just pixels.

TL;DR: We introduce Multiverse Mechanica, a causal testbed game that generates consistency-guaranteed counterfactual data to evaluate whether generative models can learn underlying game mechanics—not just reproduce visuals.