用语义分析融合战略模型与决策经验，生成可操作建议。
Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics
- 通过NLP将战略框架与决策策略映射到语义空间，实现智能匹配。
- 在企业战略案例中验证有效，支持多种框架与策略组合。
- 适合需要系统化决策支持的管理者和战略咨询人员。
我们提出一种新方法，通过语义分析将战略框架与决策启发式结合，推荐可执行策略。尽管战略框架提供系统性评估与规划模型，决策启发式则承载经验知识，但两者长期分离。本方法利用先进的自然语言处理技术，将6C模型等框架与《三十六计》等启发式进行整合。通过向量空间表示与语义相似度计算，将框架参数映射至启发式模式，并构建融合深度语义处理与受限大模型使用的计算架构。该方法同时处理文本内容与图表、矩阵等辅助信息作为互补语言表征，在企业战略案例研究中展示有效性。该方法具备通用性，可扩展至多种分析框架与启发式集合，最终形成即插即用的推荐系统架构，实现战略框架与决策启发式的协同集成，生成连贯的行动指导。
原文摘要 · Abstract (English)
We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decision heuristics through semantic analysis. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics encode experiential knowledge,these traditions have historically remained separate. Our methodology bridges this gap using advanced natural language processing (NLP), demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems. The approach employs vector space representations and semantic similarity calculations to map framework parameters to heuristic patterns, supported by a computational architecture that combines deep semantic processing with constrained use of Large Language Models. By processing both primary content and secondary elements (diagrams, matrices) as complementary linguistic representations, we demonstrate effectiveness through corporate strategy case studies. The methodology generalizes to various analytical frameworks and heuristic sets, culminating in a plug-and-play architecture for generating recommender systems that enable cohesive integration of strategic frameworks and decision heuristics into actionable guidance.
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