Overview

This course is designed to help prepare the prospective Ph.D. in Information Sciences (IS) candidate to engage in original research. The focus will be on understanding the underpinnings of doing science by studying the work of modern philosophers of science. The course will review the epistemologies (economic, behavioral, physics-based, and general systems based) serving as a scaffolding for the development of original theory development in the field of IS. The characteristic features of the received view, hypothetical-deductive formalism will be reviewed along with the modern challenges to this framework. The distinction between the instrumental-realist positions will be examined in light of its implications for theory development in IS. Students should understand the requirements for theory generation in terms of the underlying assumptions of given epistemic perspectives as a result of taking this course. Prerequisites: None.

Included in Degrees & Certificates

• 473

Learning Outcomes

This course is not intended to make you a philosopher, but rather inform and enhance your ability to become a scientific inquirer within and beyond the field of the information sciences.  The learning objectives are to:

• Understand key terms and concepts in the philosophy of science, including:
  • Epistemology, ontology, knowledge, truth, deduction, induction, abduction, reductionism, holism, objectivity, empiricism, rationalism, realism, instrumentalism, paradigm, anomalies, exemplar, normal science, research program, research tradition. explanation, theory, construct, theoretical model, hypothesis, worldview, dialectic, guarantor, ontology, etc.
• Develop familiarity with a several key philosophers of science, including: Ayer, Hempel, Popper, Duhem, Quine, the Salmons, Lipton, Kuhn, and Laudan
• Develop familiarity with C. West Churchman’s “Design of Inquiring Systems” via Mitroff & Linstone (1993) and Mitroff & Turoff’s (1973) thus gaining appreciation for Locke (consensus), Leibniz (analytical and formulaic), Kant (multiple representations), Hegel (conflict), and Singer (pragmatism) as these relate to the design and inquiry. 
• (Although sometimes a bridge too far, it would be nice to):  Understand key terms and concepts in systems (General Systems Theory -open systems) and cybernetics (closed systems), including: 
  • Systems thinking, system dynamics, levels of analysis, negative entropy, equilibrium, Sociotechnical systems (STS), Sociomaterial systems (SMS), emergence, complex adaptive systems, and punctuated equilibrium.  Systemic thinking is integral to the NPS Information Science Dept. and PhD program.
• Develop competence to answer key questions addressed in philosophy of science, such as:
  • What is science? What is a scientific theory? A scientific hypothesis? How does science differ from pseudo-science (e.g., creationism or astrology)? 
  • What is an explanation? How do scientists validate explanations? What does it mean to say a theory is true, confirmed, corroborated, supported, or falsified? What theories do we have for what a scientific explanation is?
  • What is knowledge? What is truth? Validity? Deduction? Induction and the problem of induction? Abduction? The scientific method (if there is one)?
  • What is a paradigm? An exemplar? Normal science? Anomalies? A scientific revolution? Scientific traditions? Kuhn’s theory of change vs. Popper’s?
  • How do the social sciences differ from the natural sciences? What is interpretivism?
  • How does design differ from and relate to science? How do the sciences of the artificial fit in?
  • What is positivism? What is a post-positivist approach or epistemic stance? What underlies the positivist-interpretive debate of the social sciences (the “science wars”?)
  • What are the sciences of the artificial?  How does design and a philosophy of design relate to science?  What other ways (e.g., religion, politics, art) are there and how might they differ from the way of science?
  • How does the history, sociology, psychology or politics of science relate to the philosophy of science?  How do developments in information technologies (e.g., simulation or big data) relate to the development of the science?  To what degree is the success of science to be understood in terms of how it is organized:  of its norms and reward systems?  What does it mean to naturalize the questions of the philosophy of science?  How does cognitive science and the organizational … “sciences” relate to the philosophy of science.
  • What does it mean to naturalize the questions of the philosophy of science? How does the history, sociology, psychology, or politics of science relate to the philosophy of science? To what degree is the success of science to be understood in terms of
    • how people think, reason, and make decisions;
    • the norms and reward systems of science;
    • how it is structured and governed and impacted by politics?