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

1. Introduction

Modern strategic decision-making faces a persistent dichotomy: organizations must choose between comprehensive analytical frameworks (e.g., SWOT, Porter’s Five Forces) that risk analysis paralysis, and agile heuristic patterns (e.g., Thirty-Six Stratagems) that may oversimplify complex scenarios. While both approaches encode vital strategic knowledge—the former through systematic models, the latter via experiential rules—they have evolved as separate traditions with limited integration. This paper bridges this divide through semantic natural language processing (NLP), enabling recommender systems that synergistically combine framework rigor with heuristic practicality.

Our approach leverages recent advances in artificial intelligence to analyze linguistic patterns and conceptual relationships within strategic texts. By processing both formal frameworks and heuristic collections as complementary textual inputs, we construct automated mappings between methodological analysis and actionable guidance. This is demonstrated through two representative models: the military-derived 6C framework (context, capabilities, capacity, competitiveness, resources, timing) and the Thirty-Six Stratagems from Chinese strategic tradition. Through vector-space embeddings and semantic similarity metrics, we establish systematic correlations between framework parameters and heuristic patterns, enabling evidence-based strategy recommendations.

Three key innovations distinguish our work:

• An interactive simulation environment that translates natural language scenario descriptions into framework-specific parameterizations
• Semantic validation pipelines ensuring robust alignment between heuristic patterns and analytical constructs
• Large Language Model (LLM) integration constrained to explanatory reporting, preserving human strategic primacy while enhancing insight accessibility

The methodology is deliberately framework-agnostic, processing Western analytical models and non-Western heuristic traditions with equal facility. This enables organizations to bridge structured strategic analysis with tacit practical knowledge—a critical advancement over single-paradigm systems. We demonstrate this through corporate case studies showing effective deployment across industries, followed by empirical validation against expert judgments.

The remainder of this paper is structured as follows: Section 2 establishes theoretical foundations, Section 3 details our semantic methodology, and Section 4 presents the recommender architecture. Section 5 and Section 6 provide case studies and validation, with Section 7 and Section 8 discussing related work and implications. Collectively, we demonstrate how organizations can leverage AI-mediated integration of strategic frameworks and heuristics to achieve both analytical depth and operational agility.

2. Background

Integrating analytical frameworks with decision heuristics through semantic analysis represents a highly interdisciplinary endeavor, drawing from multiple domains, including strategic management, heuristics, computer science, and linguistics. This confluence of fields necessitates thoroughly examining key concepts and prior work across several dimensions. Specifically, we must understand (1) how strategic frameworks systematize decision parameters, as exemplified by the 6C model; (2) how decision heuristics encapsulate experiential knowledge, illustrated through the Thirty-Six Stratagems; (3) how semantic analysis enables framework–heuristic integration; (4) how mathematical formulations like Kullback–Leibler divergence support validation; and (5) how gamification principles and Large Language Models facilitate practical implementation. The following subsections provide this essential foundation, which underlies our novel recommender system architecture for strategic decision support.

2.1. The 6C Framework as an Analytical Classification Tool

The 6C framework was conceived to provide a set of clear, well-defined parameters that would enable straightforward experimentation with our semantic integration approach. While established frameworks like SWOT Analysis and Porter’s Five Forces offer comprehensive analytical tools, the 6C parameters were specifically designed to facilitate initial testing and validation of our methodology, with the understanding that the same principles could then be transferred to these well-known and widely adopted frameworks. The parameters were distilled from an extensive study of strategic literature, offering a simplified yet robust foundation for our initial framework–heuristic integration experiments.

The key parameters of the 6C model are as follows:

• Offensive Strength: The ability to proactively shape and influence the strategic landscape.
• Defensive Strength: The resilience to respond effectively to adversarial actions or challenges.
• Relational Capacity: The ability to manage and leverage relationships with external stakeholders.
• Potential Energy: The availability and strategic deployment of resources.
• Temporal Availability: The strategic use of time and timing in decision-making.
• Contextual Fit: The degree to which decisions align with the strategic context, ensuring they are well-informed and relevant.

These classification processes emerged from a comparative study of prominent military strategists—ranging from ancient figures such as Sun Tzu and Chanakya to modern thinkers like Machiavelli and Clausewitz and more contemporary theorists such as Beaufre and Liddell Hart. Over time, they have transcended their military origins to influence corporate strategy, echoing broader perspectives that integrate historical wisdom with rigorous managerial concepts [1].

The 6C framework functions as a classification system for organizing and analyzing data obtained through competitive intelligence and data analytics, a capability especially valuable in digitally transforming industries [2]. This classification provides a structured approach to understanding competitive landscapes, where the six parameters can be assessed quantitatively or qualitatively. While we do not delve into the specific mechanics of setting each parameter—this is handled through the gamified environment described in Section 2.8—the focus of this paper is on how these parameters (once established) can be integrated with heuristic decision patterns via semantic analysis.

2.2. The Thirty-Six Stratagems: Crystallized Decision Patterns

The Thirty-Six Stratagems comprise a collection of ancient Chinese military decision heuristics that have evolved into widely applicable strategic principles [3]. Rooted in political and military traditions dating back to Sun Tzu’s The Art of War [4], they position cunning as a central strategic component rather than merely a tactical ploy [5]. This emphasis contrasts with many Western strategic paradigms, which historically treat deception as ethically marginal [6], making the Thirty-Six Stratagems particularly valuable for examining non-Western heuristic traditions. By transcending wartime contexts and providing a theoretical foundation for heuristics in strategic decision-making, the Chinese tradition effectively granted cunning a conceptual legitimacy that Western thought would only come to recognize centuries later. As such, these stratagems are not only militarily relevant but also lend themselves to broader applications in business, politics, and other domains requiring flexible, context-aware strategies.

These stratagems embody three key characteristics that make them ideal for semantic analysis:

• Metaphorical Encoding: Expressed through pithy maxims like “Kill with a borrowed knife” (借刀杀人), requiring cultural-linguistic bridging
• Structural Rigor: Organized into six operational categories:
  • Winning advantageous positions;
  • Direct confrontation;
  • Offensive maneuvers;
  • Creating confusion;
  • Territorial gains;
  • Desperate scenarios.
• Actionable Specificity: Each stratagem defines concrete tactical patterns rather than abstract principles

Their linguistic complexity (ancient metaphors) and conceptual richness (400+ documented historical applications [5]) create a rigorous test case for framework integration—successful semantic mapping here suggests strong generalizability to modern heuristic sets. This challenges conventional NLP approaches optimized for Western business texts, requiring specialized handling of indirect strategic language.

2.3. Additional Strategic Frameworks

While the 6C framework and the Thirty-Six Stratagems serve as primary examples in our study, our approach generalizes to multiple analytical frameworks. Following the systematic nature of the 6C framework, other prominent analytical frameworks provide structured parameters for strategic assessment:

2.3.1. SWOT Analysis

SWOT Analysis, developed by Albert Humphrey at Stanford Research Institute, offers a systematic approach to evaluating internal and external factors [7]. Its clear parameter structure makes it particularly suitable for semantic analysis:

• Strengths: Internal capabilities and resources that provide competitive advantages.
• Weaknesses: Internal limitations that may hinder strategic objectives.
• Opportunities: External factors or trends that could benefit the organization.
• Threats: External challenges that could negatively impact performance.

2.3.2. Porter’s Five Forces

This analytical framework, developed by Michael Porter, systematically dissects industry structure through five well-defined parameters [8]:

• Competitive Rivalry: Intensity of competition among existing players.
• Supplier Power: Bargaining power of suppliers.
• Buyer Power: Bargaining power of customers.
• Threat of New Entrants: Ease with which new competitors can enter.
• Threat of Substitution: Availability of alternative products or services.

Like the 6C framework, SWOT and Five Forces provide clear analytical parameters that can be vectorized and processed through semantic analysis for framework–heuristic integration. The effectiveness of our approach with these widely adopted frameworks demonstrates its potential applicability to other strategic analysis tools, both contemporary and classical, a direction we discuss in our future work.

2.4. Semantic Analysis in Strategic Text Processing

Our approach employs several key techniques from Natural Language Processing (NLP) to analyze and connect strategic frameworks (like the 6C model) with decision heuristics (such as the Thirty-Six Stratagems). Recent advances in NLP, especially Transformer-based architectures [9], have shown that vector-space language representations can capture nuanced semantic meaning and contextual relationships.

• Vector Space Representations: We encode strategic concepts using word embeddings and sentence transformers, building on methods such as BERT [10], Sentence-BERT [11], that excel in capturing contextual nuances. These techniques enable mathematical operations on semantic meaning and allow us to compare entire passages or phrases in a high-dimensional embedding space.
• Topic Modeling (Optional): We can also apply Latent Dirichlet Allocation (LDA) and related approaches [12] to identify high-level themes in strategic texts (e.g., “alliances”, “resource optimization”). Although not the core driver in our current implementation, such thematic analysis can support explainability by highlighting relevant topics that link to each framework parameter or stratagem.
• Semantic Similarity Metrics: Using cosine similarity or similar measures, we quantify relationships between vectors representing framework parameters and stratagem patterns. In our implementation with the Thirty-Six Stratagems, this objectively measures how well each stratagem aligns with specific Offensive Strength, Relational Capacity.

The semantic analysis process involves the following steps:

• Preprocessing (if needed) and Concept Extraction: In scenarios where texts are unstructured, we may apply standard NLP techniques (tokenization, chunking, domain ontology extraction). However, this step becomes straightforward if frameworks like 6C or Porter’s Five Forces are already delineated.
• Creating vector representations of framework parameters and heuristic patterns;
• Computing similarity matrices to link frameworks (e.g., 6C) with heuristics (e.g., Thirty-Six Stratagems);
• Identifying significant semantic connections and ranking them for further interpretation.

Our work builds on emerging applications of BERT-like methods to nontraditional NLP contexts [13], where domain-specific texts require fine-grained semantic understanding. Adapting these cutting-edge techniques preserves both the textual richness of each strategic expression and the interpretability needed for real-world strategic decision-making. Having said that, it must be added that although these models have proven effective, they inherit statistical biases from their training corpora, which may affect how parameters and heuristics are aligned. Because we employ LLMs only for explanatory output, the overall recommendation pipeline remains largely heuristic- and framework-driven. Nevertheless, mitigating potential embedding biases—by fine-tuning domain-specific corpora, employing bias detection tools, or adopting interpretability frameworks—is an important direction for future development.

2.5. Kullback–Leibler Divergence

In information theory and statistics, the Kullback–Leibler (KL) divergence measures how one probability distribution diverges from a second, reference distribution [14]. Given two discrete probability distributions P and Q over the same outcome space $\mathsf{\Omega }$, the KL divergence is defined as:

$$D_{\mathrm{KL}}(P\Vert Q)=\sum _{i\in \mathsf{\Omega }}P\left(i\right)log\left({\displaystyle \frac{P\left(i\right)}{Q\left(i\right)}}\right).$$

(1)

Intuitively, if P represents the “true” or expert-labeled distribution (e.g., the relative importance of each parameter in a scenario), while Q is the model’s approximate distribution, the KL divergence quantifies how inefficient it is to use Q in place of P. A lower $D_{\mathrm{KL}}(P\Vert Q)$ indicates a closer match between the two distributions, while higher values signify greater disparity.

Unlike many distance metrics, KL divergence is not symmetric, meaning that:

$$D_{\mathrm{KL}}(P\Vert Q)\ne D_{\mathrm{KL}}(Q\Vert P).$$

(2)

This asymmetry implies that the direction of comparison matters. Additionally, because KL divergence does not satisfy the triangle inequality, it is not a true metric in the formal sense; however, it remains a widely used “distance-like” measure for comparing probability distributions.

2.6. Why KL Divergence?

We selected KL divergence for three key reasons:

• Interpretability: It offers a straightforward interpretation of the “cost” of using an approximate distribution, aligning well with our need to validate semantic analysis against expert annotations.
• Directionality: Its asymmetric nature suits our context, where we specifically care about how well our approximate distributions match expert distributions, rather than vice versa.
• Established Usage: Its widespread adoption in machine learning and information theory provides a well-tested foundation for measuring distributional differences.

While metrics like the symmetric Jensen–Shannon divergence could be considered, KL divergence provides an efficient and intuitive tool for our semantic analysis workflow, where we compare system-discovered parameter distributions to expert-annotated ones. A lower divergence indicates our system captures expert priorities effectively, while higher values highlight areas needing further calibration.

2.7. Usage in This Study

In our context, we leverage KL divergence to compare the discovered parameter distributions (e.g., derived from the system’s semantic analysis) to expert-annotated distributions. This comparison provides a quantitative measure of how closely our system’s interpretations align with domain experts’ judgments, thereby helping to validate and refine the robustness of our semantic mapping process. A lower divergence indicates our system is capturing expert priorities effectively, while higher values highlight areas needing further calibration.

2.8. Gamification of Strategic Decision-Making

To streamline the usage of these analytic results, we have developed a prototype interactive simulation environment that enables:

• Exploration of different strategic scenarios;
• Testing of various decision combinations;
• Immediate feedback on potential outcomes;
• Gradual learning from simulated experiences.

Such gamification introduces a user-centric interface through which decision-makers set or adjust the situation-specific scores of the chosen analytical framework. For instance, users can enter or modify the 6C parameters—Offensive Strength, Defensive Strength, and so on—based on real competitive intelligence data or hypothetical "what-if" explorations. The system then applies its semantic mappings (Section 2.1) to generate immediate feedback on how each parameter configuration impacts recommended strategies or outcomes.

Although this gamified environment is fully implemented and was used to produce the case studies described in this paper, its detailed interface design and mechanics lie beyond our current scope. Such a user-interface analysis merits its own dedicated treatment and can be assumed here without loss of relevant information about the underlying framework. By highlighting interactive experimentation and rapid feedback, our approach resonates with broader digital transformation trends [2] and encourages deeper engagement and practical experimentation among corporate decision-makers.

3. Language-Analysis Methodology

Our methodology for integrating analytical frameworks with decision heuristics centers on semantic analysis of strategic texts. This section details the technical approach to discovering and quantifying relationships between framework parameters and heuristic patterns.

3.1. Vector Space Representation

The first step involves creating vector representations of both framework parameters and heuristic descriptions:

$$v\left(t\right)=\sum _{w\in t}{\alpha }_w·e\left(w\right)$$

(3)

where

• $v\left(t\right)$ is the vector representationwhere of text t;
• w represents individual words or phrases;
• $e\left(w\right)$ is the embedding vector for word w;
• ${\alpha }_w$ is the weight assigned to word w.

For framework parameters (in our case, the 6Cs), we create vectors from their definitions and associated descriptive text:

$$p_i=v\left(d_i\right)+\lambda \sum _{j}v\left(c_{ij}\right)$$

(4)

where

• $p_i$ is the vector for parameter i;
• $d_i$ is the formal definition of parameter i;
• $c_{ij}$ are associated contextual descriptions;
• $\lambda$ is a weighting factor for contextual information.

These vector space representations form the foundation for processing different strategic frameworks. While the mathematical formulation remains consistent, the implementation varies based on each framework’s structure, as detailed in

Table 1. Framework-specific data-processing pipeline.

Framework	Processing Steps	Output Format
6C Model	1. Parameter extraction 2. Vector embedding 3. Similarity scoring	Parameter vectors
SWOT	1. Category classification 2. Factor weighting 3. Cross-impact analysis	Category matrices
Porter’s Five Forces	1. Force identification 2. Intensity scoring 3. Relationship mapping	Force networks

.

Table 1 demonstrates the adaptive nature of our processing pipeline. Each framework requires distinct computational steps while maintaining the core principle of enabling semantic similarity comparisons between strategic elements. The 6C model emphasizes parameter-level analysis, SWOT focuses on categorical relationships, and Porter’s framework maps competitive forces—yet all produce comparable outputs for heuristic matching.

3.2. Semantic Similarity Computation

With these framework-specific vector representations in place, we can compute semantic similarities between parameters and heuristics using cosine similarity:

$$sim(p_i,h_j)={\displaystyle \frac{p_i·h_j}{\left|\right|p_i\left|\right|·\left|\right|h_j\left|\right|}}$$

(5)

where

• $p_i$ is the vector for parameter i;
• $h_j$ is the vector for heuristic j.

This produces a similarity matrix S where each element $s_{ij}$ represents the strength of relationship between parameter i and heuristic j:

$$S=\left[\begin{array}{cccc}{s}_{11}& s_{12}& \cdots & s_{1n}\\ s_{21}& s_{22}& \cdots & s_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ s_{m1}& s_{m2}& \cdots & s_{mn}\end{array}\right]$$

(6)

A higher similarity value $s_{ij}$ indicates a stronger semantic relationship between the corresponding parameter and heuristic. Detailed calculations and examples of this process appear in Appendix A.2.1.

3.3. Distribution Discovery

For each heuristic $h_j$, our system generates a discovered distribution across the framework parameters (e.g., the 6C parameters). Concretely, we first compute similarity scores $s_{ij}$ between parameter $p_i$ and heuristic $h_j$ (Section 3.2). We then normalize these scores to form a probability-like distribution:

$$d_{ij}={\displaystyle \frac{s_{ij}}{{\sum }_k{s}_{kj}}},$$

(7)

where $d_{ij}$ represents the weight (or relative importance) of parameter $p_i$ in heuristic $h_j$. This summation-based (L1) normalization treats each heuristic’s parameter weights as if they were probabilities that sum to 1. It thus naturally encodes the idea that a given heuristic distributes its “attention” across the available parameters.

Note on Alternative Normalization. An alternative would be to use the Euclidean (L2) norm, where

$$d_{ij}={\displaystyle \frac{s_{ij}}{\sqrt{{\sum }_k{\left(s_{kj}\right)}^2}}},$$

thereby turning each heuristic’s parameter vector into a unit vector in ${\ell }_2$ space. In our approach, we opt for L1 normalization to mirror a “probability-like” interpretation—each heuristic can be seen as distributing its “weight” over parameters in a manner analogous to probabilities. L2 normalization could be equally valid in other contexts, especially if one prefers strictly geometric interpretations of distance in the parameter space.

To validate these distributions, we compare them against expert-annotated distributions using the Kullback–Leibler (KL) divergence measure. For Stratagem 24, this comparison yielded a KL divergence of 0.0273, indicating strong alignment between system-discovered and expert-provided distributions. Detailed calculations and additional examples of this validation process are provided in Appendix A.2.2.

By comparing discovered distributions against expert-annotated ones for each heuristic, we obtain a numerical sense of alignment or mismatch. This process can be iterated: a large KL divergence flags a heuristic whose vector representation needs either textual refinements (e.g., additional synonyms or clarifications) or updates to the weighting scheme. Over time, machine-based distribution discovery converges with expert insights, yielding robust mappings that faithfully reflect how these strategic heuristics fit into analytical parameters.

As we already pointed out, the experts in this step are specialists in the methodologies (e.g., the 6C model, the Thirty-Six Stratagems) rather than sector-specific domain experts. This distinction ensures that the high-level semantic structure of each stratagem is validated by those who understand it conceptually, independent of particular industries or case studies.

3.4. Stratagem Selection Algorithm

After the distribution discovery phase (Section 3.3), we obtain an invariant distribution of analytical properties for each heuristic. This invariant distribution reflects how strongly each heuristic (e.g., a particular stratagem) aligns with each framework parameter (e.g., the 6C model) based on the text analysis and expert validation.

Situation-Specific Parameter Vector. In contrast, a variable (or current) situation vector, denoted by x, describes how the analytical parameters apply to the present scenario. For instance, if a certain strategic context demands high Offensive Strength ($p_1$) and moderate Relational Capacity ($p_3$), x will capture these intensities accordingly.

Matching Heuristics to the Situation. By comparing x with each heuristic’s invariant distribution, we produce a recommendation score that indicates how well that heuristic fits the present conditions. This process is summarized in Algorithm 1.

Algorithm 1 Stratagem selection

Require: Situation vector x, Similarity matrix S, Threshold $\theta$ Ensure: Ranked list of relevant stratagems  1: $scores\leftarrow \varnothing$  2: for each stratagem j do  3:    $d_j\leftarrow \mathrm{normalize}\left(S[:,j]\right)$ {Invariant distribution of parameters for heuristic j}  4:    $scor{e}_j\leftarrow \mathrm{similarity}(x,d_j)$ {Compare current situation vector to heuristic distribution}  5:    if $scor{e}_j\ge \theta$ then  6:      $scores.\mathrm{append}\left((j,scor{e}_j)\right)$  7:    end if  8: end for  9: return sort($scores$, descending=True)

The algorithm produces a ranked list of relevant heuristics, from strongest to weaker matches, filtered by a minimum threshold $\mathsf{\theta }$ to ensure only sufficiently strong alignments are proposed. When applied to strategic scenarios in our case studies, the algorithm successfully identified relevant stratagems matching the strategic context. For instance, in the hydrogen vs. electric vehicle competition case, it highlighted stratagems focused on indirect positioning and resource leveraging. Detailed examples of the algorithm’s application, including specific calculations and case study connections, are provided in Appendix A.3.

3.5. Semantic Validation

To ensure robustness and credibility in the discovered semantic mappings, we conduct a three-pronged validation:

• Cross-Validation: We compare parameter–heuristic distributions generated by multiple embedding approaches (e.g., different Transformer models, dimensionality settings). If the mappings remain consistent across these variations, it indicates resilience against model-specific biases or hyperparameter choices.
• Perturbation Analysis: We introduce small textual modifications (e.g., synonyms, minor paraphrasing) to heuristic descriptions or framework definitions and observe whether the resulting distributions change drastically. A stable mapping under such perturbations implies that the system captures deeper semantic relationships rather than overfitting to exact word forms.
• Expert Review: We invite experts knowledgeable about both the analytic framework (e.g., 6C) and the heuristics (e.g., the Thirty-Six Stratagems) to label how strongly each heuristic aligns with each parameter. By comparing these expert judgments to algorithmic outputs, we can detect alignment or uncover conceptual mismatches (see Section 3.3 for details on KL divergence).

The validation process produces a confidence score $c_{ij}$ for each parameter–heuristic mapping:

$$c_{ij}=\alpha ·v_{ij}+\beta ·s_{ij}+\gamma ·e_{ij},$$

(8)

where

• $v_{ij}$ is the cross-validation score, reflecting consistency across embedding variants;
• $s_{ij}$ is the stability score, derived from perturbation analysis;
• $e_{ij}$ is the expert agreement score, capturing how closely the system’s outputs align with expert annotations;
• $\alpha$, $\beta$, and $\gamma$ are weighting parameters that can be tuned (e.g., through trials or domain priorities).

A higher $c_{ij}$ indicates that the mapping from parameter $p_i$ to heuristic $h_j$ is consistently validated by multiple lines of evidence: model-invariant cross-validation, perturbation resilience, and expert concordance. This systematic approach to verifying semantic relationships underpins our goal of automating the integration of traditionally separate analytical frameworks and decision heuristics with confidence.

Validation Example

To illustrate this validation process, let’s examine how we validate the mapping between parameter $p_3$ (Relational Capacity) and Stratagem 24 (“Use Allies’ Resources”):

• Cross-Validation:

We compute the distribution using three different embedding models:

• BERT-base: $\left[\right(p_1:0.10),(p_2:0.07),(p_3:0.61),(p_4:0.13),(p_5:0.03),(p_6:0.06\left)\right]$;
• RoBERTa: $\left[\right(p_1:0.11),(p_2:0.08),(p_3:0.58),(p_4:0.14),(p_5:0.03),(p_6:0.06\left)\right]$;
• Sentence-BERT: $\left[\right(p_1:0.09),(p_2:0.07),(p_3:0.63),(p_4:0.12),(p_5:0.04),(p_6:0.05\left)\right]$.

The consistent emphasis on parameter $p_3$ (0.58–0.63) yields $v_{3,24}=0.92$.

• Perturbation Analysis:

We introduce variations in the stratagem description:

• Original: “Use Allies’ Resources”;
• Variant 1: “Leverage Partnership Assets”;
• Variant 2: “Utilize Collaborative Resources”.

The stable distribution patterns across variants produce $s_{3,24}=0.88$.

• Expert Review:

Three expert ratings of parameter $p_3$’s importance:

• Expert 1: 0.55;
• Expert 2: 0.60;
• Expert 3: 0.58.

The close alignment with our computed distribution for parameter $p_3$ (0.61) gives $e_{3,24}=0.94$.

With weighting parameters $\alpha =0.3$, $\beta =0.3$, and $\gamma =0.4$ (emphasizing expert judgment slightly), the final confidence score is:

$$\begin{array}{cc}\hfill c_{3,24}& =0.3·0.92+0.3·0.88+0.4·0.94\hfill \\ \hfill & =0.916\hfill \end{array}$$

This high confidence score (>0.9) suggests strong validation across all three approaches, indicating reliable semantic mapping between parameter $p_3$ (Relational Capacity) and Stratagem 24.

4. Computational Architecture

The computational architecture integrates user inputs, strategic analysis, semantic processing, and decision-making support, leveraging both semantic analysis and Large Language Models (LLMs) for insight generation and reporting. A key feature is that the architecture manages a structured conversation flow to guide users through scenario parameter collection, framework–heuristic mapping, and final report generation.

The system architecture consists of the following components (see Figure 1):

• Strategic Data Input Layer: Users interact with a structured graphical environment (the context editor) to input competitive intelligence data, market information, and other relevant strategic details. This environment supports both quantitative data and qualitative descriptions, with workflow states managing data validation and format requirements. Detailed implementation specifications are provided in Appendix A.4.
• Semantic Analysis Engine: This component processes input data using the methodology described in Section 3. Specifically, it:
  • Creates vector representations of strategic situations;
  • Computes semantic similarities with framework parameters;
  • Maps situations to relevant heuristic patterns;
  • Generates initial parameter distributions.
• Framework-Integration Layer: The system translates semantic analysis results into the chosen analytical framework’s parameters. This layer ensures that framework-agnostic analysis can be mapped to specific strategic tools while maintaining consistent evaluation metrics across frameworks.
• Strategic Processing Core: The main engine applies framework-specific weightings, evaluates strategic options, matches situations with relevant heuristics, and generates preliminary recommendations. The processing core incorporates conversation-state information to produce context-appropriate guidance.
• LLM-Integration Layer: The system interfaces with LLMs through standardized APIs to transform technical analysis into actionable insights. The architecture constrains LLM tasks via predefined templates to ensure structured, safe, and consistent outputs. Key functions include:
  • Translation of semantic similarities into natural language explanations;
  • Contextualization of framework–heuristic matches;
  • Generation of both executive summaries and detailed reports;
  • Template-based validation mechanisms for generated content.
• Report Generation and Visualization: The final layer produces comprehensive strategic analysis reports, visual representations of strategic options, and detailed implementation recommendations. These outputs integrate data from all prior steps, including scenario parameters, semantic analysis scores, and LLM-generated commentaries.

The architecture’s modular design allows different frameworks and heuristic sets to be integrated without modifying the core system. Implementation details, including JSON workflow definitions, state management specifications, and component interaction protocols, are provided in Appendix A.4.

This architecture provides several key advantages:

• Flexibility: Supports multiple strategic frameworks and heuristic sets;
• Scalability: Handles increasing complexity in strategic analysis;
• Safety: Constrains LLM use to well-defined tasks;
• Reproducibility: Ensures consistent analysis and recommendations.

Workflow Integration

The architecture supports a systematic workflow for connecting analytical frameworks with decision heuristics:

• Parameter Analysis: The system analyzes actors and situations using the chosen framework’s parameters through semantic processing of input data.
• Objective Evaluation: Strategic objectives are identified and evaluated through semantic analysis of stated goals and contextual information.
• Heuristic Matching: The system matches situation parameters with relevant heuristics from the chosen set, using semantic similarity measures to rank appropriateness.
• Implementation Planning: Practical applications are derived through pattern matching between heuristic principles and concrete action possibilities.
• Risk Assessment: Potential outcomes and risks are evaluated through semantic analysis of historical patterns and contextual factors.
• Report Generation: Results are synthesized into comprehensive reports through constrained LLM processing.

This workflow demonstrates how the architecture supports the systematic integration of any analytical framework with any set of decision heuristics through semantic analysis and pattern matching.

5. Case Studies

We demonstrate our semantic integration approach through two case studies:

• A contemporary scenario of competing innovation pathways in the automotive industry;
• A historical competition in the personal computer market.

These cases illustrate how our methodology connects strategic frameworks with decision heuristics in different domains.

5.1. Semantic Analysis of the Hydrogen vs. Electric Competition in the Automotive Industry

This first case study examines the strategic rivalry between hydrogen-based and electric-based propulsion systems in the global automotive industry. Drawing on scenario inputs, the system processes and compares two principal actors (HydrogenEngines and ElectricEngines) with respect to their capacities for achieving market dominance in sustainable automotive. By applying the semantic analysis pipeline described in Section 3, we derive quantitative parameter values, match them to appropriate stratagems, and generate strategic recommendations.

5.1.1. Parameter Analysis

The parameter values shown below were derived by aggregating insights from structured interviews with stakeholders in both the automotive and energy industries (e.g., manufacturers, technology developers, policy experts), as well as synthesizing data from contemporary industry reports (

Table 2. Semantic analysis of hydrogen vs. electric parameters.

Parameter	Value (HydrogenEngines)	Value (ElectricEngines)
Defensive Strength	3.25	4.0
Offensive Strength	3.75	4.2
Relational Capacity	3.60	4.5
Potential Energy	4.00	4.8
Time Availability	3.20	4.3
Context Fit	3.80	4.6

). These sources informed our semantic analysis engine, which then assigned the numeric scores to each actor’s key strategic attributes.

HydrogenEngines exhibits relatively strong Potential Energy (4.0), reflecting substantial investments and technological innovation, but shows lower Time Availability (3.2), indicating urgency to secure market share. ElectricEngines, by contrast, attains higher overall parameter values, including robust Relational Capacity(4.5) and Context Fit (4.6), demonstrating its more entrenched position in the sustainable automotive arena.

5.1.2. Stratagem-Parameter Matching

The semantic analysis pipeline connects quantitative parameter values with heuristic patterns through systematic matching.

Table 3. Framework–heuristic matching analysis: HydrogenEngines case study.

6C Parameter	Value	Matched Stratagem	Score	Alignment Factors
Offensive Strength	3.75	Stratagem 16 (Illusory Ways Out)	0.89	Indirect positioning, Market differentiation
Potential Energy	4.00	Stratagem 15 (Lure into Unfav. Env.)	0.82	Resource leverage, Niche focus
Relational Capacity	3.60	Stratagem 24 (Use Allies’ Resources)	0.93	Partnerships, Infrastructure sharing
Context Fit	3.80	Stratagem 1 (Acting Unnoticed)	0.85	Development timing, Market preparation
Defensive Strength	3.25	Stratagem 3 (Act Through an Ally)	0.78	Indirect influence, Policy alignment
Temporal Availability	3.20	Stratagem 17 (Cast a Brick)	0.76	Strategic timing, Readiness

demonstrates this process for HydrogenEngines, showing alignment between 6C parameters and relevant stratagems.

The matching analysis reveals critical strategic alignments: Relational Capacity’s high score (0.93) with Stratagem 24 underscores partnership-building importance for infrastructure development, while Offensive Strength’s strong alignment (0.89) with Stratagem 16 suggests indirect positioning effectiveness. These patterns demonstrate how semantic analysis surfaces non-obvious connections between quantitative parameters and qualitative heuristics.

5.1.3. Stratagem Semantic Analysis

Following the methodology from Section 3, the system analyzes each of the Thirty-Six Stratagems to derive parameter weights:

$$w_{ij}={\displaystyle \frac{{\sum }_{t\in T_j}s(t,p_i)}{{\sum }_k{\sum }_{t\in T_j}s(t,p_k)}},$$

(9)

where

• $w_{ij}$ is the weight of parameter i in stratagem j;
• $T_j$ is the set of terms in the textual description of stratagem j;
• $s(t,p_i)$ is the semantic similarity between term t and parameter i.

For illustration, consider Stratagem 16 ("Leave the opponent illusory ways out"), ranked highly for HydrogenEngines. A linguistic examination of key terms such as illusory, deception, and misdirection led to higher weights for Offensive Strength and Relational Capacity, aligning with the actor’s moderate ability to engage in indirect actions.

5.1.4. Situation–Stratagem Matching

The system computes an alignment score between each actor’s parameter distribution and each stratagem’s profile:

$$\mathrm{alignment}(s,h)=\sum _i{w}_i·p_i·c_i,$$

(10)

where $w_i$ is the parameter weight in the stratagem, $p_i$ is the parameter value for the actor, and $c_i$ is a contextual relevance factor. Top matching stratagems for HydrogenEngines (with effectiveness scores, EFF) are listed in

Table 4. Top matching stratagems for HydrogenEngines.

Stratagem	Score (EFF)	Key Alignment	Implementation Focus
16: Illusory Ways Out	6.03	Offensive (3.75)	Misleading EV sector
15: Lure into Unfavorable Env.	5.72	Potential (4.0)	Exploit EV limitations
24: Use Allies’ Resources	5.68	Relational (3.6)	Infrastructure partnerships
3: Act Through an Ally	5.56	Offensive (3.75)	Indirect policy influence
1: Acting Unnoticed	5.41	Context Fit (3.8)	Quiet tech development

.

5.1.5. Strategic Recommendations

Based on these matching results, the system generates actionable recommendations for HydrogenEngines to achieve market dominance in sustainable automotive:

• Primary Strategy: Indirect Positioning
  • Stratagem 16 (Illusory Ways Out): Create paths leading ElectricEngines into complacency or unproductive markets, while HydrogenEngines solidifies niches such as freight and heavy-duty applications.
  • Alignment Score: 6.03
• Supporting Strategy: Target Vulnerable Segments
  • Stratagem 15 (Lure into Unfavorable Env.): Exploit EV weaknesses (e.g., limited mileage in heavy-duty use) by focusing hydrogen tech where EVs are less dominant.
  • Alignment Score: 5.72
• Alliances and Borrowed Influence
  • Stratagem 24 (Use Allies’ Resources) & Stratagem 3 (Act Through an Ally): Establish partnerships with governments, energy sectors, and logistics enterprises to co-develop hydrogen infrastructure and coordinate policy support.
  • Alignment Scores: 5.68, 5.56
• Discreet Development Efforts
  • Stratagem 1 (Acting Unnoticed): Invest quietly in R&D, infrastructure, and lobbying until hydrogen-based solutions are ready for large-scale deployment.
  • Alignment Score: 5.41

5.1.6. Implementation Pathways

Figure 2 highlights concrete implementation steps: forging covert alliances, occupying underdeveloped markets, and progressively rolling out hydrogen infrastructure. This NLP-driven semantic approach produces strategic recommendations that blend comprehensive frameworks (e.g., 6C) with concise heuristic insights (e.g., the Thirty-Six Stratagems). By leveraging alliances and focusing on niche strengths, HydrogenEngines can challenge ElectricEngines’ market dominance in the evolving automotive landscape.

5.2. Semantic Analysis of the Commodore–Apple Market Competition

The second case study turns to historical business competition: in the late 1980s, Commodore, a pioneer in the personal computer market, faced fierce rivalry from Apple. Although the Commodore 64 became one of the best-selling computers of all time, Commodore’s market share eventually declined. Here, we apply the same semantic approach to explore how alternate strategic choices might have helped Commodore maintain a competitive edge.

5.2.1. Parameter Analysis

These parameter values were derived by examining a range of historical documents and reports detailing the so-called “PC wars” of the 1980s (e.g., market analyses, shareholder reports, and industry assessments) (

Table 5. Semantic analysis of Commodore–Apple parameters.

Parameter	Value (Commodore)	Value (Apple)
Offensive Strength	3.5	4.0
Defensive Strength	3.0	3.5
Relational Capacity	2.8	3.8
Potential Energy	3.0	4.2
Time Availability	3.5	4.0
Context Fit	2.9	4.0

). This documentary evidence was then processed by the semantic analysis engine to produce the numeric scores that reflect each actor’s strategic attributes during that period.

Commodore shows moderate Offensive Strength (3.5) but lower Relational Capacity (2.8) compared to Apple, indicating less success in forging strategic partnerships or consumer alliances. Meanwhile, Apple consistently registers higher scores across multiple dimensions, including Potential Energy (4.2).

5.2.2. Stratagem-Parameter Matching

The system applies the semantic weighting methodology from Section 3 to analyze Thirty-Six Stratagems against Commodore ’s strategic parameters. For example, Stratagem 18 (Capture Core Strengths) demonstrates dual alignment through:

• Offensive Strength: Keywords attack, capture, dominate
• Potential Energy: Terms resources, capabilities, power

The analysis reveals strategic priorities through key alignments: Offensive Strength’s high score (0.85) with Stratagem 18 emphasizes product innovation importance, while Potential Energy’s strong match (0.82) with Stratagem 11 highlights resource optimization potential (

Table 6. Framework–heuristic matching analysis: Commodore case study.

6C Parameter	Value	Matched Stratagem	Score	Alignment Factors
Offensive Strength	3.5	Stratagem 18 (Capture Core)	0.85	Product innovation, Market positioning
Potential Energy	3.0	Stratagem 11 (Connect Series)	0.82	Resource optimization, Development focus
Time Availability	3.5	Stratagem 23 (Alliance Bldg)	0.78	Strategic timing, Partnership development
Context Fit	2.9	Stratagem 8 (Adaptive Resp.)	0.76	Market adaptation, Consumer alignment
Defensive Strength	3.0	Stratagem 13 (Strike Weakness)	0.74	Brand protection, Competitive response
Relational Capacity	2.8	Stratagem 31 (Beauty Trap)	0.71	Channel management, UX focus

). These patterns suggest Commodore could have countered Apple’s expansion by focusing R&D investments and streamlining operational capabilities—opportunities obscured without systematic framework–heuristic integration.

5.2.3. Strategic Recommendations

• Primary Strategy: Core Capability Development
  • Focus on product innovation and user-interface development;
  • Directly counter Apple’s market differentiators;
  • Alignment Score: 0.85.
• Supporting Strategy: Resource Optimization
  • Reallocate development resources toward high-potential product lines;
  • Streamline less profitable divisions;
  • Alignment Score: 0.82.
• Tactical Implementation
  • Launch targeted product-development campaigns;
  • Invest in market positioning;
  • Seek out strategic partnerships.

5.2.4. Implementation Pathways

Figure 3 illustrates potential implementation paths. Using semantic analysis to spotlight Commodore’s opportunities for core capability development and resource optimization, the approach reveals how historical outcomes might have diverged if Commodore had adopted structured strategic planning tied to concise, proven heuristics.

5.3. Cross-Case Analysis

Although the hydrogen–electric automotive and Commodore–Apple PC contexts are quite different in scope and era, both case studies confirm the flexibility and effectiveness of our semantic approach:

• Domain Adaptation:
  • In the automotive domain, the semantic analysis highlighted indirect positioning and alliances as critical.
  • In the PC domain, a focus on core capabilities and resource allocation emerged as priorities.
• Recurring Patterns:
  • Even across distinct industries, resource optimization, partnership development, and strategic positioning are repeatedly identified as success factors.
  • Specific stratagems (e.g., alliance-building) have wide applicability, provided the correct parameter alignment exists.
• Implementation Pathways:
  • Both studies exhibit primary and supporting strategies, tactical steps, and cross-strategy synergies.
  • Clear alignment scores lend transparency to why certain recommendations are prioritized over others.
• Framework–Heuristic Integration:
  • In the automotive sector, the integrated approach effectively linked 6C parameters (e.g., Potential Energy, Context Fit) with heuristics emphasizing deception and alliance.
  • In the PC sector, the same pipeline tied Offensive Strength and Potential Energy to historically proven guidelines about capturing core strengths and leveraging limited resources.

This comparative analysis demonstrates how a consistent semantic methodology can bridge analytical frameworks and decision heuristics, regardless of domain differences.

5.4. Enhanced Understanding Through LLM Reporting

In practice, the system’s semantic scores and recommendations can be further enriched by Large Language Models (LLMs). Once the alignment scores and suggested strategies are determined, an LLM can:

• Generate Summaries: Provide executive overviews for stakeholders, focusing on the highest-scoring tactics.
• Explain Reasoning: Offer narrative justifications for why certain stratagems align well with particular parameters.
• Highlight Potential Risks: Enumerate conditions or assumptions that might invalidate certain recommendations.

This capability empowers decision-makers to understand not just which strategies are recommended but also the rationale behind them—ultimately improving trust and adoption in corporate or organizational settings.

5.5. Implementation Insights

Across the two case studies, several insights emerge:

• Text-Processing Nuances:
  • Industry-specific jargon can alter semantic similarity calculations.
  • Historical data enriches pattern recognition but may require separate preprocessing.
• Pattern Matching Consistency:
  • Similar strategic patterns recur, such as positioning, resource optimization, or alliance-building.
  • Modest parameter differences can push one stratagem over another in the ranking.
• Validation and Context:
  • Historical (Commodore–Apple) outcomes offer tangible lessons in how lacking a suitable strategy might lead to missed market opportunities.
  • Contemporary contexts (hydrogen vs. electric) show how real-time data can inform flexible, AI-assisted decisions.

Together, these insights underscore the value of a robust, domain-agnostic methodology for integrating analytical frameworks and decision heuristics supported by semantic analysis and enriched by LLM-driven reporting.

5.6. Extended Analysis Reports

The semantic analysis pipeline presented in this paper has been applied to numerous strategic scenarios beyond the two detailed case studies above. Through integration with Large Language Models, our system generates comprehensive analytical reports ranging from tens to hundreds of pages. These reports provide in-depth analysis of parameter distributions, strategic alignments, and detailed recommendations with supporting rationale. The complete reports for both the hydrogen-electric automotive competition and the Commodore–Apple market rivalry, along with analyses of many other strategic scenarios, are available at https://www.linkedin.com/company/103262552/admin/dashboard/ (accessed on 15 February 2023). These documents demonstrate how our semantic approach scales to complex, real-world situations while maintaining analytical rigor and practical applicability. Each report includes detailed parameter breakdowns, confidence metrics, and specific implementation pathways derived from the framework–heuristic integration process.

6. Empirical Validation

To validate our framework–stratagem integration approach, we conducted a focused empirical study examining how effectively the Thirty-Six Stratagems can be integrated with different analytical frameworks. Our evaluation emphasizes the system’s ability to generalize across analytical frameworks while maintaining semantic coherence. While Section 3.5 established the theoretical foundations for validating semantic mappings through cross-validation, perturbation analysis, and expert review—yielding confidence scores ($c_{ij}$)—this section extends and operationalizes these concepts into measurable performance metrics.

6.1. Experimental Setup

We evaluated three key aspects of the system:

• Framework Integration: Testing with three analytical frameworks:
  • 6C Framework (primary test case);
  • SWOT Analysis;
  • Porter’s Five Forces.
• Framework–Stratagem Integration: Testing the integration of the Thirty-Six Stratagems with each analytical framework
• Cross-Framework Consistency: Evaluating recommendation stability across frameworks

6.2. Results

6.2.1. Framework-Integration Performance

Table 7. Framework-integration results with the Thirty-Six Stratagems.

Framework	Coverage	Consistency	Adaptability
6C Framework	0.89	0.92	0.87
SWOT	0.85	0.88	0.84
Porter’s Five Forces	0.82	0.85	0.81

shows the integration quality metrics across different frameworks:

Key findings include:

• High coverage (>0.80) across all frameworks;
• Strong consistency in parameter mapping;
• Declining but still robust performance with more complex frameworks.

6.2.2. Stratagem-Integration Performance

Expert Agreement scores were calculated based on distributions generated by five experts with deep knowledge of both the analytical frameworks and the Thirty-Six Stratagems. The results show strong agreement with expert-derived mappings (>0.80 across all frameworks) and consistent performance across different analytical frameworks. Detailed calculations and extended examples are provided in Appendix A.5.

6.3. Discussion

The empirical results validate three key aspects of our approach:

• Integration Capability: The system successfully integrates the Thirty-Six Stratagems with diverse analytical frameworks while maintaining high semantic accuracy.
• Scalability: Performance remains robust when adapting to new frameworks, with only modest degradation in accuracy.
• Efficiency: Significant reduction in integration time compared to manual approaches while maintaining expert-level accuracy.

Limitations and considerations:

• Performance slightly decreases with more complex frameworks;
• Expert validation remains valuable for novel framework combinations;
• System requires initial training data for optimal performance.

These results demonstrate that our semantic approach provides a viable method for automating framework–stratagem integration while maintaining accuracy and enabling systematic scaling to new domains.

6.4. Further Considerations on Scalability

Although our current validation demonstrates the system’s effectiveness across several analytical frameworks and moderate-sized data sets, additional research is needed to assess performance at large scales or in highly complex business scenarios. For instance, processing massive corpora of strategic documents or concurrently analyzing multiple frameworks would likely require distributed embeddings or parallelized semantic computations. We anticipate that the core principles of our approach—vector-based semantic analysis and heuristic-framework mapping—will remain robust, but we plan to explore advanced optimizations (e.g., sharding, caching, or GPU acceleration) in future work.

7. Related Work

This section surveys prior research informing our approach to developing a recommender system architecture for strategic decision-making. We organize the discussion into five core dimensions: (1) theoretical foundations of heuristic decision-making, (2) recommender systems for decision support, (3) strategy tools and platforms, (4) AI-assisted decision systems, and (5) framework–heuristic integration attempts.

7.1. Heuristic Decision-Making Foundations

The recognition of heuristic decision-making has evolved significantly through scholarship on tacit knowledge [15,16] and bounded rationality [17]. Where classical models emphasized comprehensive analysis, modern work acknowledges the necessity of pragmatic shortcuts [18,19,20]. However, organizations often treat such heuristics as informal supplements rather than systematized components—particularly when they involve cunning or deception historically marginalized in Western strategic paradigms [5]. This creates a persistent gap between formal frameworks and operational reality that our methodology addresses through semantic integration.

7.2. Recommender Systems for Strategic Support

Recommendation technologies have evolved from consumer-focused systems [21] to context-aware models handling dynamic organizational variables [22]. While recent work integrates LLMs for cross-domain recommendations [23,24], few systems adapt analytical frameworks like Porter’s Five Forces as recommendation targets. Our architecture fills this gap through content-based matching of heuristic “items” against framework parameters, combining strategic matrices with context-aware modeling.

7.3. Strategy Tools and Platform Evolution

Strategic decision support has progressed through several paradigms:

• Business Rules: Systematized operational logic [25];
• Scenario Planning: Managed uncertainty through environmental exploration [26,27];
• Gamified Interfaces: Enhanced engagement through flow-based simulations [28].

Despite these advances, existing tools struggle to integrate structured frameworks with tacit heuristics. Business rules lack strategic adaptability, scenario tools rely on predefined logic, and gamification often prioritizes engagement over analytical depth. Our approach bridges these limitations through NLP-driven semantic analysis that links framework parameters with heuristic patterns.

7.4. AI-Assisted Centaurian Systems

Modern decision architectures increasingly adopt centaurian design principles combining human and artificial intelligence [29,30]. Unlike disruptive implementations in creative domains [31], our monotonic centaur approach enhances existing strategic workflows through:

• Automated semantic scoring between framework parameters/heuristics;
• LLM-powered explanation generation [32];
• Abductive reasoning support for hypothesis exploration.

This preserves human strategic primacy while augmenting analytical scope—a critical distinction from autonomous AI decision systems.

7.5. Previous Integration Attempts

Prior efforts to connect frameworks and heuristics typically adopted manual mapping processes [33] or single-paradigm approaches. The Business Rules community achieved limited success through rigid logic formalization [25], while scenario planning tools focused on environmental factors over tactical patterns [27]. Our semantic NLP methodology represents the first systematic approach to automate framework–heuristic integration across cultural and linguistic boundaries.

8. Conclusions

This paper has presented a context-aware, content-based recommender system designed to bridge the gap between analytical frameworks and decision heuristics through semantic analysis. By treating strategic frameworks (e.g., 6C, Porter’s Five Forces) and heuristic collections (e.g., the Thirty-Six Stratagems) as textual resources, our approach addresses a core challenge in strategic decision-making: how to systematically align the rigor of structured analysis with the pragmatism and speed of heuristic-based action.

8.1. Key Contributions

The primary contributions of this work include:

• Recommender System Architecture: A novel design that reframes strategic knowledge as “items” for recommendation, enabling context-aware and content-based matching between analytical parameters and decision heuristics.
• Semantic Integration Framework: A systematic methodology for connecting different frameworks (e.g., 6C, SWOT) with heuristic sets (e.g., the Thirty-Six Stratagems, OODA loops) via vector embeddings and semantic similarity scores.
• Computational Implementation: A flexible architecture combining deep NLP pipelines, heuristic mapping, and AI-assisted reporting. This includes a gamified simulation layer that allows users to explore strategic scenarios in an interactive manner.
• Generality Across Domains: Evidence from multiple case studies (e.g., hydrogen vs. electric automotive competition, the Commodore–Apple rivalry) illustrating that the approach can scale to diverse strategic contexts, from business to technology.

8.2. Practical Implications

Our system delivers several tangible benefits for organizations seeking an actionable decision-support tool:

• Enriched Decision Support: Merges quantitative analysis (framework-based) with qualitative insight (heuristics), fostering more balanced strategic decisions.
• Scalable Knowledge Transfer: Translates textual frameworks and heuristics into easily comparable vector forms, reducing the reliance on domain-specific expertise.
• Efficient Recommendations: Automates the matching process between high-level strategy parameters and heuristics, accelerating scenario analysis and cutting down on manual mapping.
• Human-Centric AI Integration: Constrains Large Language Models to explain and synthesize, thereby strengthening, rather than replacing, human strategists—a monotonic centaur approach.

8.3. Limitations and Future Work

Although the present study highlights promising results, several aspects require further exploration to enhance the robustness and applicability of our approach. One key challenge lies in semantic processing, particularly in refining domain adaptation techniques for industry-specific terminology and improving context modeling over extended textual inputs and time horizons. Additionally, incorporating dynamic scenario elements, such as real-time data streams, and mitigating biases in pre-trained language models remain critical considerations.

Another area for development is framework integration. While our study primarily employs the 6C Model as a representative case, we also incorporate SWOT Analysis and Porter’s Five Forces to demonstrate the adaptability of our approach. Future work could further extend this integration by incorporating additional strategic tools, such as VRIO (Value, Rarity, Imitability, Organization) [34] and PESTLE (Political, Economic, Social, Technological, Legal, and Environmental) [35] analyses. Automating the detection of framework-specific sub-parameters and incorporating quantitative indicators, such as financial metrics, would further enhance decision accuracy and cost-effectiveness. Ultimately, this expanded perspective would realize the full potential of our methodology by enabling a seamless connection between structured strategic models and heuristic-based decision-making, as exemplified by the Thirty-Six Stratagems.

Validation and user studies are crucial to ensuring the system’s reliability and real-world impact. Cross-domain evaluations across various organizational contexts, longitudinal studies assessing long-term adoption, and usability research on simulation environments and Large Language Model (LLM)-based explanations will contribute to refining our methodology. Additionally, validating the system against organization-specific heuristic sets beyond the Thirty-Six Stratagems will test its adaptability to diverse strategic paradigms.

From an ethical and domain considerations perspective, ensuring fairness, accountability, and transparency in decision-making is paramount. The system must integrate mechanisms to evaluate and promote these principles while identifying and mitigating cognitive biases that may arise during heuristic application. Adapting the methodology to domain-specific regulatory requirements and extending the framework to incorporate stakeholder impact assessments will reinforce its applicability in practice.

Building upon these limitations, two primary directions for future work emerge. First, developing organization-specific heuristics and simple rules presents a promising avenue. Organizations often rely on distinct decision-making principles that evolve based on market feedback and managerial refinement. Capturing and structuring these principles into an iteratively updated recommendation system would provide tailored strategic guidance while maintaining alignment with established frameworks such as SWOT and Porter’s Five Forces.

Second, translating classic strategic texts into practical heuristics offers an opportunity to expand the applicability of our approach. Works like Sun Tzu’s Art of War, Machiavelli’s The Prince, and Chanakya’s Arthashastra contain rich strategic insights, yet their metaphorical language often complicates direct application in modern business contexts. By systematically mapping their conceptual guidance onto structured analytical frameworks, we can extract actionable decision-making principles that bridge historical wisdom with contemporary strategy.

To support these primary trajectories, several technical and methodological advances are necessary. Enhanced semantic analysis techniques, such as incorporating temporally aware embeddings and domain-specific ontologies, will improve our ability to capture evolving strategic conditions. Multi-framework orchestration will allow the system to recommend the most suitable analytical framework under given conditions, thereby increasing its adaptability.

Furthermore, integrating adaptive feedback loops that leverage user interactions and outcome data will enable continuous refinement of heuristic mappings. Extending gamification and scenario planning elements will facilitate interactive exploration of decision scenarios, providing users with a more immersive and intuitive strategic decision-making experience.

Finally, domain adaptation remains a crucial consideration. By integrating specialized business ontologies, automating domain terminology extraction, and dynamically updating semantic models as new business concepts emerge, the system will remain relevant in rapidly evolving industries. Moreover, implementing a balancing criterion to ensure that no single parameter dominates heuristic mapping will mitigate potential biases introduced by training corpora, promoting a more balanced and robust strategic analysis framework.

In summary, while our study demonstrates the potential of integrating analytical frameworks with decision heuristics through semantic analysis, several enhancements remain necessary to refine its precision, adaptability, and ethical robustness. By addressing these challenges and expanding our methodological scope, we can further establish a comprehensive and scalable strategic decision-support system for real-world applications.

8.4. Concluding Remarks

Integrating analytical frameworks with decision heuristics through NLP-driven recommenders represents a compelling advance in strategic decision-making. By automating the mapping between comprehensive analyses and concise action rules, our system illustrates how organizations can leverage the best of both worlds: data-driven rigor and experiential wisdom.

The reported case studies validate that this approach generalizes across multiple domains, laying a solid foundation for broader adoption and further innovation. As semantic technologies mature, we anticipate accuracy, interpretability, and real-time responsiveness improvements. Ultimately, this research contributes to the growing conversation on how human-centered AI can seamlessly amplify existing strategic processes, offering a blueprint for interactive, context-aware, and heuristic-informed decision-support solutions.