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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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/3/192 |
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| author | Renato Ghisellini Remo Pareschi Marco Pedroni Giovanni Battista Raggi |
| author_facet | Renato Ghisellini Remo Pareschi Marco Pedroni Giovanni Battista Raggi |
| author_sort | Renato Ghisellini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8de2b9718eb048c1b81da86fc664ec05 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj-art-8de2b9718eb048c1b81da86fc664ec052025-08-20T01:48:50ZengMDPI AGInformation2078-24892025-03-0116319210.3390/info16030192Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision HeuristicsRenato Ghisellini0Remo Pareschi1Marco Pedroni2Giovanni Battista Raggi3Institute for Generative Strategy, 44121 Ferrara, ItalyStake Lab, University of Molise, 86100 Campobasso, ItalyInstitute for Generative Strategy, 44121 Ferrara, ItalyInstitute for Generative Strategy, 44121 Ferrara, ItalyWe 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.https://www.mdpi.com/2078-2489/16/3/192recommender systemssemantic analysisstrategic frameworksdecision heuristicsnatural language processingplug-and-play architecture |
| spellingShingle | Renato Ghisellini Remo Pareschi Marco Pedroni Giovanni Battista Raggi Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics Information recommender systems semantic analysis strategic frameworks decision heuristics natural language processing plug-and-play architecture |
| title | Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics |
| title_full | Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics |
| title_fullStr | Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics |
| title_full_unstemmed | Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics |
| title_short | Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics |
| title_sort | recommending actionable strategies a semantic approach to integrating analytical frameworks with decision heuristics |
| topic | recommender systems semantic analysis strategic frameworks decision heuristics natural language processing plug-and-play architecture |
| url | https://www.mdpi.com/2078-2489/16/3/192 |
| work_keys_str_mv | AT renatoghisellini recommendingactionablestrategiesasemanticapproachtointegratinganalyticalframeworkswithdecisionheuristics AT remopareschi recommendingactionablestrategiesasemanticapproachtointegratinganalyticalframeworkswithdecisionheuristics AT marcopedroni recommendingactionablestrategiesasemanticapproachtointegratinganalyticalframeworkswithdecisionheuristics AT giovannibattistaraggi recommendingactionablestrategiesasemanticapproachtointegratinganalyticalframeworkswithdecisionheuristics |