Business World Model

Abstract: Businesses are increasingly adopting AI-enabled tools to improve productivity, reduce costs, and enhance products and services. However, the transformative potential of AI extends beyond automating predefined tasks: it lies in enabling intelligent systems to plan, optimize, and execute business initiatives from high-level strategic objectives.

摘要： 企业正日益采用人工智能驱动的工具来提高生产力、降低成本并优化产品与服务。然而，人工智能的变革潜力远不止于自动化预定义的任务：其核心在于使智能系统能够从高层战略目标出发，规划、优化并执行商业计划。

This paper introduces the concept and architecture of a business world model (BWM), a world model specialized for business and organizational environments. Inspired by world models in artificial intelligence, cognitive science, and control theory, a BWM encodes business states, dynamics, constraints, objectives, and feasible action space to support autonomous decision-making.

本文介绍了商业世界模型（Business World Model, BWM）的概念与架构，这是一种专门针对商业和组织环境的世界模型。受人工智能、认知科学和控制理论中“世界模型”概念的启发，BWM 对商业状态、动态、约束、目标以及可行行动空间进行编码，以支持自主决策。

We propose a business-semantics-centric formulation in which business states, dynamics and actions are linked to key business entities. Within this framework, agents can simulate alternative action sequences, estimate their effects on future business outcomes, and evaluate trade-offs under uncertainty.

我们提出了一种以商业语义为中心的表述方式，将商业状态、动态和行动与关键商业实体相链接。在此框架内，智能体可以模拟不同的行动序列，评估其对未来商业结果的影响，并在不确定性下权衡利弊。

The proposed architecture integrates semantic data representations, probabilistic machine learning models, deterministic business rules, and explicit action space into a coherent structure for planning and counterfactual reasoning. Although its individual components are not new, the contribution of BWM lies in organizing them as an executable internal simulator for business initiatives.

该架构将语义数据表示、概率机器学习模型、确定性商业规则和显式行动空间整合为一个连贯的结构，用于规划和反事实推理。尽管其各个组成部分并非首创，但 BWM 的贡献在于将它们组织成一个可执行的商业计划内部模拟器。

This work establishes a conceptual foundation for autonomous business systems capable of moving from instruction-based execution toward goal-driven planning and execution.

这项工作为自主商业系统奠定了概念基础，使其能够从基于指令的执行转向目标驱动的规划与执行。

Paper Details:

Authors: Cecil Pang, Hiroki Sayama
Subject: Artificial Intelligence (cs.AI)
arXiv ID: 2606.10044
Date: 8 Jun 2026