arXiv cs.AI· Cecil Pang, Hiroki Sayama·· 3 天前AI 评分32
面向商业环境的 Business World Model（BWM）概念与架构
Business World Model
AI 导读
论文提出 Business World Model（BWM），一种面向商业与组织环境的世界模型，将状态、动态和可行动作与业务实体及其属性和关系绑定。其参考设计与开源实现中，LLM 智能体负责解读自然语言业务目标并解释结果，确定性、元数据驱动的仿真引擎整合机器学习模型、业务规则和动作空间完成全部计算。媒体订阅业务的流失案例研究显示，系统能在收入约束下推荐满足流失率降低目标的方案。
正文 · 原文
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