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

arXiv: https://arxiv.org/abs/2501.14634 · HTML: https://mlsanity.com/paper/2501.14634/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024)
- [Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research](https://mlsanity.com/paper/2307.10250/) (2023)

## Built on by (most cited first, 3 of 3)

- [From Extraction to Synthesis: Entangled Heuristics for Agent-Augmented Strategic Reasoning](https://mlsanity.com/paper/2507.13768/) (2025)
- [Quantum Abduction: A New Paradigm for Reasoning under Uncertainty](https://mlsanity.com/paper/2509.16958/) (2025)
- [Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications](https://mlsanity.com/paper/2601.00421/) (2026)

## Abstract (arXiv)

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.