Generative World Models for Multi-Agent Coordination and Decision-Making in Complex Virtual Environments

Abstract

Generative world models are emerging as a unifying computational substrate for reasoning about dynamic environments. Unlike static datasets, these models learn compact representations of environment transitions and can generate plausible future trajectories under candidate actions. When multiple agents interact in complex virtual environments, the coordination problem becomes entangled with nonstationarity, partial observability, and strategic interdependence. This paper examines the role of generative world models in multi-agent coordination and decision-making from a systems perspective rather than focusing narrowly on algorithmic novelty. We argue that the value of a generative world model lies not only in predictive accuracy but also in its capacity to support counterfactual reasoning, structured exploration, and policy evaluation across interacting agents. The discussion foregrounds architectural trade-offs such as centralized simulation versus decentralized policy execution, the placement of memory and attention across a coordination stack, and the separation of training, inference, and evaluation infrastructure. We further analyze deployment challenges including latency constraints, data governance, robustness under distribution shift, fairness in multi-agent outcomes, and the sustainability of large-scale generative simulation. The paper situates these concerns within broader socio-technical and policy contexts, proposing that responsible deployment requires layered governance mechanisms that connect technical audit, institutional oversight, and societal accountability. Throughout, we emphasize structural design choices rather than specific optimization procedures, providing an interdisciplinary synthesis for researchers and practitioners who design generative world models for cooperative, competitive, and mixed-motive virtual environments.