Jean Albertsen

Jean AlbertsenJean AlbertsenJean AlbertsenJean Albertsen
  • HOME
  • e - BOOK
  • SERVICES
  • WHO I WORK WITH
  • BUSINESS NOW - BLOG
  • FREE CYBERSECURITY COURSE
  • ACTA ERP
  • PHILOSOPHY OF SCIENCE

Jean Albertsen

Jean AlbertsenJean AlbertsenJean Albertsen
  • HOME
  • e - BOOK
  • SERVICES
  • WHO I WORK WITH
  • BUSINESS NOW - BLOG
  • FREE CYBERSECURITY COURSE
  • ACTA ERP
  • PHILOSOPHY OF SCIENCE

Course Structure [PHILOSOPHY OF SCIENCE]

Module 1 — Ontology: The Nature of Reality

Key Thinkers:


  • René Descartes
  • Baruch Spinoza
  • George Berkeley


Focus:


  • Realism vs Relativism
  • Objectivism vs Nominalism
  • Structural vs constructed reality
  • Ontological–Epistemological Asymmetry:
     
    • Realist ontology can support multiple epistemologies.
    • Relativist ontology cannot coherently sustain strict positivism.
    • Reality constrains knowledge asymmetrically — knowledge does not symmetrically construct reality.


Applied to Business & IT:


  • Is “market performance” independent of perception?


  • Is “organizational culture” measurable or constructed?

Start

Module 2 — Epistemology: The Nature of Knowledge

Key Thinkers:


  • John Locke
  • David Hume
  • Gottfried Wilhelm Leibniz


Focus:


  • A priori vs A posteriori knowledge
  • Tabula Rasa
  • Induction problem
  • Innate structures
  • Induction Asymmetry (Hume): Past observations never guarantee future necessity.
  • Rational–Empirical Asymmetry (Leibniz): Logical certainty is stronger than empirical certainty.


Applied to Economics & IT:


  • Are algorithms discovered (logical necessity) or trained (empirical probability)?


  • Do economic models describe necessity or pattern regularity?

Start

Module 3 — Logic & Syllogistic Reasoning

Key Thinker:


  • Aristotle


Focus:


  • Deduction
  • Induction
  • Abduction
  • Logical validity vs empirical truth
  • Logical Strength Asymmetry:
     
    • Deduction preserves truth conditionally.
    • Induction increases uncertainty.
    • Abduction proposes the most plausible — not the certain.


Strategic Application:


  • When theory testing is stronger than theory building


  • When explanation is weaker than prediction

Start

Module 4 — Classical Positivism

Key Thinker:


  • Auguste Comte


Focus:


  • Hierarchy of sciences
  • Three stages of knowledge
  • Cumulative knowledge
  • Rejection of metaphysics
  • Verification Asymmetry:
    Accumulated confirmations do not eliminate logical vulnerability (Humean shadow over Comte).


Strategic Direction:


  • Statistical modeling
  • Law-like generalization
  • Quantitative research dominance

Start

Module 5 — Logical Positivism & Language

Key Thinkers:


  • Rudolf Carnap
  • Bertrand Russell


Focus:


  • Verification principle
  • Observation statements
  • Operationalization
  • Conceptual clarity
  • Meaning Asymmetry:
    Statements are asymmetrically divided into:
     
    • Verifiable = meaningful
    • Non-verifiable = meaningless


Application:


  • KPI construction
  • Measurement validity
  • Construct precision

Start

Module 6 — Phenomenology & Interpretivism

Key Thinker:


  • Edmund Husserl


Focus:


  • Lived experience
  • Consciousness
  • Meaning construction


Application:


  • User experience research
  • Leadership perception
  • Digital transformation narratives

Start

Module 7 — Critical Rationalism

Key Thinker:


  • Karl Popper


Focus:


  • Falsification
  • Theory survival
  • Scientific progress
  • Falsification Asymmetry:
     
    • Universal statements cannot be conclusively verified.
    • A single counterexample can refute them.
      Science eliminates error more reliably than it confirms truth.


Application:


  • Stress-testing economic models
  • AI robustness testing
  • Risk exposure analysis

Start

Module 8 — Scientific Revolutions & Paradigm Shifts

Key Thinker:


  • Thomas Kuhn


Focus:


  • Paradigms
  • Normal science
  • Anomalies
  • Crisis
  • Scientific revolutions
  • Incommensurability
  • Historical Asymmetry of Scientific Development:
     
    • Science progresses discontinuously
    • Paradigm shifts reorganize standards, logic, and data
    • Competing paradigms are not fully comparable


Application:


  • Economic paradigm shifts
  • AI paradigm evolution
  • Technological disruption cycles

Start

Module 9 — Hermeneutics & Understanding

Key Thinker:


  • Wilhelm Dilthey


Focus:


  • Erklären vs Verstehen
  • Ideographic methodology
  • Contextual meaning
  • Explanation–Understanding Asymmetry:
    Predicting behavior does not equal understanding meaning.


Strategic Use:


  • Case studies
  • Organizational interpretation
  • Cultural analysis

Start

Module 10 — Axiology & Research Ethics

Key Thinker:


  • Max Weber


Focus:


  • Value neutrality
  • Researcher bias
  • Reflexivity
  • Value Asymmetry:
     
    • Researchers aim for neutrality.
    • Topic selection and interpretation remain value-laden.
      Complete neutrality is asymmetrically unattainable.


Application:


  • Ethical governance in IT
  • Data power asymmetries
  • Institutional bias

Start

Module 11 — The 4-Quadrant Meta-Framework

Focus:


  • Integration of ontology, epistemology, logic, paradigm, and axiology
  • Structural asymmetry across research strategies


Students map their research design across:



  • Rationalist Quadrant
     
    • Core Asymmetry: Logical necessity > empirical certainty
    • What follows from reason and formal logic is stronger than what is derived from observation.


  • Empirical Quadrant
     
    • Core Asymmetry: Observation > necessity
    • Knowledge is grounded in experience, but observed patterns do not guarantee logical necessity. 


  • Critical Quadrant
     
    • Core Asymmetry: Error elimination > truth confirmation
    • Science is stronger at falsifying false theories than proving true ones.


  • Interpretive Quadrant
     
    • Core Asymmetry: Meaning depth > generalizability
    • Rich contextual understanding is prioritized over universal prediction.


Application:
 

  • Designing philosophically consistent theses
  • Identifying epistemic risk exposure
  • Aligning data type with ontological assumptions

Start

Copyright © 2025 JEAN ALBERTSEN - All Rights Reserved.

This website uses cookies.

We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.

DeclineAccept