Course Structure [PHILOSOPHY OF SCIENCE]

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?

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?

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

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

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

Key Thinker:

• Edmund Husserl

Focus:

• Lived experience
• Consciousness
• Meaning construction

Application:

• User experience research
• Leadership perception
• Digital transformation narratives

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

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

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

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

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