Computational Conceptual Inquiry for Dynamic Strategy and Sensemaking

A Human–Machine Framework Integrating Self-Organizing Models and Semantic Embeddings

Abstract

Traditional strategic analysis and business intelligence methodologies are grounded in exhaustive data collection and theory-driven interpretation. These approaches assume conceptual stability during analysis, an assumption increasingly invalid in rapidly evolving socio-economic environments. This paper proposes ConceptMiner, a computational framework for conceptual inquiry that complements fact-based investigation by enabling continuous reorganization of meaning structures. ConceptMiner integrates semantic embeddings derived from large language models with self-organizing neural models such as Self-Organizing Maps (SOM) and Growing Neural Gas (GNG). The framework operationalizes key principles underlying qualitative methods such as the KJ method while overcoming their cognitive, organizational, and scalability limitations. ConceptMiner is positioned as a human–machine sensemaking system rather than a decision engine, supporting strategic reflection, conceptual revision, and insight generation under uncertainty.

1. Introduction

1.1 The Temporal Mismatch in Strategic Analysis

Since the latter half of the twentieth century, strategic management methodologies—particularly those developed and disseminated by large consulting firms—have emphasized exhaustive data collection followed by structured analysis within established theoretical frameworks. While analytically rigorous, these approaches presuppose that:

  1. The relevant problem domain can be comprehensively enumerated.
  2. Conceptual categories used for analysis remain valid throughout the investigation.
  3. Strategic insight emerges primarily from correct analytical application.

In practice, extended investigation cycles often result in a temporal mismatch: by the time analysis is completed, the strategic environment has already shifted. This mismatch is not merely a matter of speed but reflects a deeper structural limitation concerning the stability of conceptual frames.

2. From Fact-Based Investigation to Conceptual Inquiry

Fact-based investigation answers the question “What is happening?”
Conceptual inquiry addresses a prior and more fundamental question: “How should the situation itself be understood?”

Conceptual inquiry involves:

  • Suspending predefined categories,
  • Allowing provisional and ambiguous groupings,
  • Iteratively reorganizing meaning structures as understanding evolves.

In contrast to conventional analysis, conceptual inquiry accepts uncertainty and ambiguity as productive states rather than deficiencies to be eliminated.

3. Qualitative Foundations and Practical Constraints

3.1 The KJ Method as a Precedent

The KJ method represents a systematic attempt to formalize conceptual inquiry through:

  • Fragmentation of observations,
  • Iterative regrouping,
  • Deferred categorization until qualitative insight emerges.

Despite its methodological depth, the KJ method suffers from practical limitations:

  • High dependence on participant skill and discipline,
  • Cognitive fatigue,
  • Susceptibility to social dynamics and authority bias,
  • Limited scalability and reproducibility.

As a result, consistent application in organizational contexts has proven difficult.

4. Computational Self-Organization as Conceptual Infrastructure

4.1 Self-Organizing Maps and Growing Neural Gas

Self-organizing neural models provide a computational substrate for conceptual inquiry:

  • Self-Organizing Maps (SOM) project high-dimensional input vectors onto a low-dimensional topological lattice while preserving neighborhood relations.
  • Growing Neural Gas (GNG) dynamically adapts network topology to data distribution, allowing flexible representation of evolving concept spaces.
  • Minimum Spanning Tree (MST) overlays support structural interpretation and navigation.

These models share key properties:

  • No predefined categories,
  • Topological preservation,
  • Incremental adaptation.

However, earlier applications were constrained by limited feature representations.

5. Semantic Embeddings as a Breakthrough Enabler

Recent advances in large language models enable high-dimensional semantic embeddings that encode meaning as continuous vectors while preserving ambiguity. This development fulfills a critical prerequisite for computational conceptual inquiry:

Meaning can now be represented as a continuous space suitable for distance-based organization without premature discretization.

Embeddings enable:

  • Heterogeneous textual fragments to coexist in a shared semantic space,
  • Gradual reorganization without loss of nuance,
  • Integration with self-organizing models.

6. ConceptMiner Architecture

6.1 System Overview

ConceptMiner consists of three interacting layers:

  1. Semantic Representation Layer
    Textual fragments are embedded into high-dimensional semantic vectors.
  2. Structural Organization Layer
    Self-organizing models (SOM / GNG + MST) organize embeddings into evolving topologies.
  3. Human Interpretation Layer
    Users interact with generated structures to interpret, reinterpret, and revise conceptual understanding.

Crucially, ConceptMiner does not automate insight generation; it externalizes conceptual ambiguity into a computational structure that humans can engage with.

7. Mathematical Formulation (Overview)

Let:

  • X={xi}X = \{x_i\}X={xi​}, where xi∈Rdx_i \in \mathbb{R}^dxi​∈Rd, denote semantic embeddings.
  • A self-organizing model defines a mapping f:Rd→Rkf: \mathbb{R}^d \rightarrow \mathbb{R}^kf:Rd→Rk, with k≪dk \ll dk≪d.
  • Neighborhood preservation is enforced via competitive learning (SOM) or adaptive graph growth (GNG).

Learning minimizes a distortion function:E=∑i∥xi−wc(i)∥2E = \sum_i \| x_i – w_{c(i)} \|^2E=i∑​∥xi​−wc(i)​∥2

where wc(i)w_{c(i)}wc(i)​ denotes the prototype vector associated with the best-matching unit.

Importantly, ConceptMiner treats learned structures as provisional representations, not definitive classifications.

8. Comparison with Contemporary Knowledge Systems

8.1 KJ Method

  • Human-centered, ambiguity-tolerant.
  • Limited scalability and reproducibility.

8.2 Retrieval-Augmented Generation (RAG)

  • Optimized for factual recall and question answering.
  • Assumes stable conceptual structures.
  • Poor support for conceptual revision.

8.3 Knowledge Graphs

  • Require predefined ontologies.
  • Effective for formal reasoning.
  • Inflexible under conceptual uncertainty.

8.4 ConceptMiner

  • Preserves ambiguity.
  • Enables repeated reorganization.
  • Supports insight emergence rather than answer retrieval.

9. Relation to Strategic Management Theory

Traditional strategy frameworks emphasize:

  • Market structure,
  • Competitive positioning,
  • Resource optimization.

These frameworks implicitly assume stable conceptual domains. ConceptMiner complements strategic theory by providing a pre-analytical layer in which conceptual frames themselves are examined and revised before formal analysis.

10. ConceptMiner as a Human–Machine Sensemaking System

ConceptMiner reconfigures the division of labor:

  • Machines sustain ambiguity, memory, and structure.
  • Humans perform interpretation, judgment, and conceptual transformation.

This transforms qualitative sensemaking from an ephemeral group activity into a persistent, shareable process.

11. Implications and Applications

Potential applications include:

  • Early-stage strategy exploration,
  • Innovation research,
  • Policy analysis,
  • Organizational learning.

ConceptMiner is not a predictive or decision-making system but a conceptual memory infrastructure.

12. Conclusion

ConceptMiner formalizes conceptual inquiry as a computationally supported process. By integrating semantic embeddings with self-organizing models, it enables continuous conceptual revision under uncertainty. Rather than accelerating answers, ConceptMiner sustains the conditions under which meaningful questions—and thus strategic insight—can emerge.

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