arXiv：cs.AI· Cecil Pang, Hiroki Sayama·· 19 小时前
面向商业环境的 Business World Model（BWM）概念与架构提出
Business World Model
AI 导读
论文提出 Business World Model（BWM），一种专为商业与组织环境设计的世界模型，以业务语义为中心，将状态、动态和可行动作关联到业务实体及其属性与关系。
正文
Abstract:World model has emerged as a powerful paradigm in artificial intelligence, enabling agents to represent their environments, predict future states, and evaluate possible actions before acting. However, existing world models have largely been developed for visual or physical domains such as computer vision, robotics, gaming, and autonomous driving, which are governed by relatively stable dynamics. These formulations are not directly applicable to business practice, where the relevant environment is semantic, organizational, and market-driven, and outcomes depend on context-sensitive factors such as customer behavior, pricing, competition, and regulation. This paper introduces the concept and architecture of a Business World Model (BWM), a world model specialized for business and organizational environments. We propose a business-semantics-centric formulation in which states, dynamics, and feasible actions are linked to business entities, their attributes, and their relationships. Within this framework, intelligent agents can simulate alternative action sequences, estimate their effects on future business outcomes, and evaluate trade-offs under uncertainty and business constraints. We present a reference design and open-source implementation in which LLM agents interpret natural-language business goals and explain results, while a deterministic, metadata-driven simulation engine integrating machine learning models, business rules, and action spaces performs all calculations. A churn case study of a media subscription business demonstrates that the system recommends options meeting the churn-reduction target within the revenue constraint. This work establishes a conceptual foundation for autonomous business systems capable of moving from instruction-based execution toward goal-driven planning, optimization, and execution.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.10044 [cs.AI] |
| (or arXiv:2606.10044v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10044 arXiv-issued DOI via DataCite |
Submission history
From: Cecil Pang [view email]
[v1]
Mon, 8 Jun 2026 18:16:04 UTC (939 KB)
[v2]
Sun, 28 Jun 2026 13:25:07 UTC (925 KB)
[v3]
Sun, 11 Oct 2026 15:20:39 UTC (1,071 KB)
来源：arXiv：cs.AI · arxiv.org