Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics

2025-01-24

AI Summary

Existing decision support systems treat analytical frameworks (e.g., 6C) and heuristic strategies (e.g., Thirty-Six Stratagems) as disjoint entities, lacking semantic-level integration. Method: We propose a semantics-driven strategy recommendation system that (i) establishes the first semantic alignment between analytical frameworks and heuristics via a cross-paradigm semantic mapping mechanism; (ii) designs a multimodal language representation to uniformly encode heterogeneous strategic knowledge—including text, matrices, and diagrams; and (iii) adopts an LLM-constrained computational architecture to ensure interpretable and controllable reasoning. Our approach integrates deep semantic NLP, vector-space modeling, cross-framework similarity computation, and lightweight collaborative inference. Contribution/Results: Experiments on multiple corporate strategy cases demonstrate its effectiveness. The system supports plug-and-play recommendation for arbitrary framework–heuristic combinations and generates strategy proposals that balance theoretical rigor with practical feasibility.

Technology Category

• Search and Optimization: Heuristic Search
• Cognitive Modeling & Cognitive Systems: Analogy
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Decision-Making Frameworks
• Data Analysis
• Natural Language Processing (NLP)

Innovation

• NLP-based Decision-making
• Modular Strategy System
• Text and Data Analysis