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

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

We present a novel approach for <b>recommending actionable strategies</b> by integrating strategic frameworks with decision heuristics through <b>semantic analysis</b>. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics en...

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Bibliographic Details
Main Authors: Renato Ghisellini, Remo Pareschi, Marco Pedroni, Giovanni Battista Raggi
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Information
Subjects:
recommender systems
semantic analysis
strategic frameworks
decision heuristics
natural language processing
plug-and-play architecture
Online Access:https://www.mdpi.com/2078-2489/16/3/192
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Summary:We present a novel approach for <b>recommending actionable strategies</b> by integrating strategic frameworks with decision heuristics through <b>semantic analysis</b>. 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 <b>advanced natural language processing (NLP)</b>, demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems. The approach employs <b>vector space representations</b> and <b>semantic similarity calculations</b> 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 <b>primary content</b> and <b>secondary elements</b> (diagrams, matrices) as complementary linguistic representations, we demonstrate effectiveness through corporate strategy case studies. The methodology <b>generalizes</b> to various analytical frameworks and heuristic sets, culminating in a <b>plug-and-play architecture</b> for generating <b>recommender systems</b> that enable cohesive integration of strategic frameworks and decision heuristics into actionable guidance.
ISSN:2078-2489

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