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

TL;DR

This paper introduces a semantic NLP-based method to integrate strategic frameworks with decision heuristics, enabling the generation of actionable recommendations through a flexible, computational architecture demonstrated in corporate strategy case studies.

Contribution

It presents a novel semantic approach that bridges strategic frameworks and heuristics, creating a versatile, plug-and-play recommender system for strategic decision-making.

Findings

Effective integration of frameworks and heuristics demonstrated in case studies

Semantic similarity calculations enable mapping of parameters to heuristic patterns

The architecture generalizes across various strategic frameworks and heuristics

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.