Videos of Philosophy Lectures, University of Twente

Philosophy of Engineering: Science
 

Aim and content

This course aims at better understanding of science and the engineering sciences. In your engineering education you have become acquainted with many mathematical formulas. But where do these formulas come from and how do we know where to apply them? Usually, we also make a distinction between fundamental or basic sciences versus applied sciences. But, is it really possible to simply 'apply' basic scientific knowledge? And how should we make a distinction between science and engineering sciences? Related to these question we will discuss several topics from philosophy of science such as "what is science?", "what is a scientific explanation?", "what are laws of nature?", "what is a scientific model". Other topics are: "what is a scientific methodology in the engineering sciences?", "what is the role of experiments in scientific research?", "what is the role of mathematics".
 

Learning objectives

  1. Basic to intermediate knowledge of the philosophy of science: main topics are: types of scientific reasoning, scientific method, scientific explanation, Hempel, Popper, Kuhn, instrumentalism, realism, relativism.
  2. Understanding how scientific theories are produced by studying some examples from the history of science.
  3. Capacity to use these ideas in order to achieve better understanding of science and the engineering sciences (and the relation between the two). For those who aim at a career in research it will give insights in several aspects of scientific methodology, and in how to become a good scientist. For those who aim at a career in design- and development, it will give a better understanding of what can be expected from science.
 

Prior knowledge

  1. No prior knowledge in philosophy is required.
  2. Prior knowledge of canonical examples from the natural sciences is recommended, such as high school level knowledge of Newton's laws, Bohr model of the atom, Boyle's law, …).
 

Literature

  1. James Ladyman. Understanding Philosophy of Science. Routledge 2002. [Book]
  2. PDFs provided for each lecture contain additional explanatory text to the slides.
  3. Handout on Truth and empirical adequacy
  4. Handout on B&K theory for analysing scientific articles
  5. Recommended: Peter Dear. The Intelligibility of Nature – How Science makes Sense of the World. The University of Chicago Press. 2007.
 

Lectures

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Videos:

  1. Introduction & Scientific Reasoning and Methodology I
    1. Course objectives and general learning aims
    2. What are presuppositions? Example: "The Nature of Business"
    3. Course content
    4. What is science?
    5. Why is scientific methodology important?
    6. Scientific method: Empiricism
    7. The problem of induction
  2. Scientific Reasoning and Methodology II
    1. Introduction
    2. Can we prove scientific knowledge?
    3. Epistemology: How do we prove knowledge?
    4. Epistemology: Rationalism
    5. Example of first principles
    6. The logic of scientific reasoning
    7. Epistemology: Empiricism
    8. Is Empiricism a methodology to prove knowledge?
    9. Empiricism: Laws and experiments
    10. Hume on the problem of induction
    11. Is Problem of Induction relevant to Practice?
    12. Manipulationism: Can we discover causal relationships?
  3. Falsification, Truth and Empirical Adequacy
    1. Introduction
    2. Richard Feynman on proving knowledge
    3. The Logic of Hypothetical Deductive Reasoning
    4. Why do we trust knowledge?
    5. What does ‘truth’ mean? [The problem of truth.]
    6. Correspondence theory of truth
    7. Why do we accept knowledge – Truth?
    8. The semantic conception of truth. [Tarski’s semantic notion of truth.]
    9. Why do we accept knowledge? Why Realism does not work.
    10. Why do we accept knowledge? The Anti-Realist alternative.
      [Van Fraassen’s ‘empirical adequacy’ as an alternative for the ‘correspondence notion of truth’.]
  4. Observation, Hypotheses / Explanations, Abductive Reasoning (IBE); Hypotheses & Modelling
    1. Introduction: The societal relevance of philosophy of science
    2. Summary: The logic of 'truth' and 'empirical adequacy'
    3. What is a hypothesis?
    4. Do laws of nature explain? (Socrative)
    5. Laws of nature: Descriptions, definitions or explanations of observed phenomena?
    6. Is your opinion really that extreme? - Are you really a realist / anti-realist?
    7. Are laws just descriptions of observed phenomena?
    8. How do we get theories (or models) that explain observed phenomena?
    9. The problem of correspondence between the scientific model and real-world (= The problem of Realism)
    10. Can we develop an alternative to Realism?
    11. The anti-Realism alternative
    12. Some other examples of observed phenomena and the scientific models explaining them
    13. Scientific knowledge as tools for thinking – called epistemic tools
    14. The B&K theory of scientific modeling
    15. Take home message
  5. Scientific Modelling
    1. Overcoming a naïve picture of how science is applied in engineering by understanding methodological and cognitive strategies in science.
    2. Introduction: The concept of Models as Epistemic Tool as a means to think about scientific knowledge at a meta-level (across scientific disciplines).
    3. How do we construct models that explains the observed phenomena in case the model goes beyond mathematical deduction from basic laws?
    4. Explaining the aspects of the B&K tool: The roles of scientific reasoning, epistemic criteria, measurements and theory playing a role in how we construct scientific models.
    5. The B&K tool as a strategy for (re-)constructing relevant aspects of scientific models and modelling.
    6. Applying the B&K tool to reconstruct the (‘constructed’ and published) scientific model that explains the observed phenomenon of sono-luminescence.
    7. How the B&K tool accounts for the construction of scientific models (that explain phenomena), and how it is part of the general HD-method of scientific research.
    8. Introducing the final assignment: using the B&K tool in analyzing a scientific article.
    9. What is engineering science? A definition: How engineering science differs from other natural sciences, and how researchers tend to talk about the technological application only (example of Pammography).
    10. An example of how a technological design problem (in bioleaching) is translated into scientific research project.
    11. Applying the B&K tool - steps i, iv, v &vii: What is/are the phenomena relevant to this technology? Which measurements and knowledge do we have about the phenomenon, and what are relevant physical circumstances.
    12. How to translate a technological problem into a scientific research project: trial and error solutions versus fundamental understanding.
    13. How do we construct a scientific model that explains the (technologically important) phenomenon?
    14. Scientific breakthroughs: The role of new measurement methods and experimental techniques (related to step v in B&K).
    15. The discovery and the new scientific model (related to steps ii and vii in B&K).
  6. Understanding in & of the Engineering Sciences
    1. Introduction: An engineering perspective at science – Looking at science as something that is constructed
    2. Overview of what we have done so far – Scientific knowledge as epistemic tool.
    3. Overview of last lecture – What is engineering science?
    4. How do we build scientific models?
    5. About what do the engineering sciences produce knowledge?
    6. What do the engineering sciences use knowledge for?
    7. Revisiting the question: What are laws of nature? – How laws are constructed in experimental practices.
    8. An example of constructing a phenomenological law – introducing new parameters (e.g. a material constant) and scientific concepts.
    9. Important conclusions on “What is a phenomenological law”: descriptions? / mathematical equations / empirical adequacy / same-conditions – same-effects / operational definitions of parameters / measurement procedure of the parameter.
    10. Similarities and differences between (constructing) scientific models and phenomenological laws.
    11. The role of measurements in constructing scientific knowledge – how the discovery of a technologically produced phenomenon turns into a measurement apparatus (two examples).
    12. The role of parameters in phenomenological laws – inventing parameters  as a common strategy to characterize (relatively) stable properties (of specific materials, and also of specific technological systems)
    13. What is a scientific concept? – Is it just a name, or a definition. If so, does it consist of a mere description of an observed phenomenon, or is it theoretical as well? And how about scientific concepts of unobservable phenomena?
    14. Take home message on the construction of knowledge
  7. Paradigms in Science: Kuhn versus Traditional View
    1. Overview: A schema on the line of argument in this course – Theme 1: Science produces true knowledge & why should we accept knowledge?
    2. Overview: A schema on the line of argument in this course – Theme 2: Objectivity and rationality of Science
    3. Introduction: The question that Kuhn asked in “The Structure of Scientific Revolution”, and the societal context which made that Kuhn’s ideas have had an enormous impact.
    4. The Copernican revolution as a metaphor for a paradigm shift (a new world view), and as an example of a scientific revolution
    5. Kuhn’s critique on Logical Positivist’s and Popper’s traditional picture of science. Part 1: Cumulative growth; Unified Science; Science is value free.
    6. Kuhn’s critique on Logical Positivist’s and Popper’s traditional picture of science. Part 2: Distinction between discovery and justification
    7. After a scientific revolution the abandoned theory seems irrational as it involves a paradigm-shift. What is a paradigm shift?
    8. Kuhn’s critique on Logical Positivist’s and Popper’s traditional picture of science. Part 3: Distinction between observation and theory
    9. Kuhn’s critique on Logical Positivist’s and Popper’s traditional picture of science. Part 4: Kuhn rejects that theories can be confirmed(or verified), and argues that the meaning of scientific concepts change in a paradigm-shift.
  8. Paradigms and Concepts in Science: Kuhn’s disciplinary matrix
    1. Introduction: The problem of objectivity and rationality of science.
    2. Kuhn’s notion of a disciplinary matrix.
    3. What are core principles in Kuhn’s disciplinary matrix? [1] Ontological principles: In order to do science anyway, we need indemonstrable(!) presuppositions that guide our scientific reasoning. Newton called them “rules of philosophizing.”
    4. What are core principles in Kuhn’s disciplinary matrix? [2] Principles of logic: Even the rules of logic, such as those originally articulated by Aristotle, are indemonstrable(!) presuppositions that guide our scientific reasoning. These rules have been challenged in modern philosophy of mathematics.
    5. What is a metaphysical picture of the world? [1] A general reductionist picture of the world commonly held in the natural sciences.
    6. What is a metaphysical picture of the world? [2] A corpuscular picture of the world: the assumption that everything consists of particles and forces between them, which implies for science that everything should be explained in terms of (unobservable) particles and (unobservable) forces between them.
    7. How do scientists reason within a paradigm? The example of Sadi Carnot – the inventor of thermodynamics.
    8. The change of a paradigm [1]. The change of a metaphysical picture in the history of science affects whether or not a scientific theory is intelligible. Example: From the concept of ‘force’ to ‘energy’.
    9. The change of a paradigm [2]. The change of a metaphysical picture. Example: How the concept of aether disappeared.
    10. Summary and Conclusions on the role of paradigms in science, and how this idea can help us to understand difficulties of working interdisciplinary.
    11. What have we done in this course, and why would an alternative idea about science be helpful for the engineering sciences?

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