Philosophy 560B: Plausibility and Analogical Reasoning
University of British Columbia
Department of Philosophy

Winter 2001
F 12:30 - 3:30, Buchanan B315

Instructor       Office             Telephone        E-mail                                                            Office Hours
Paul Bartha    Buch E358     822-2621                        MW 12:30-1:30

Description: Analogical reasoning plays a significant role in the evolution of scientific thought. Not only is analogy extensively used in the early stages of investigation to demonstrate the plausibility of hypotheses, but in some fields, such as archaeology and evolutionary biology, it may sometimes be the strongest possible form of theoretical confirmation. This widely used form of reasoning, however, has been subjected to only limited examination by philosophers of science; Mary Hesse’s Models and Analogies in Science is the most serious study. In the last two decades, researchers in AI have developed computational models of analogy. Because of the diversity of fields in which analogies are used, and because of its dependence on detailed contextual knowledge, finding a satisfactory model of analogical reasoning has proved elusive. In addition, relatively few philosophers have attempted to explain what justifies analogical reasoning.

In the first part of the course, after presenting some historical examples of analogical reasoning in science, we focus on the philosophical literature on this topic. We will then examine leading examples of computational theories of analogy. Rather than developing a ‘logic of analogy’, the objective here is to appreciate the central difficulties in formulating an account of analogical reasoning, and to understand possible directions in which a solution to those difficulties might lie.

The second portion of the course concentrates more generally on the topic of plausible reasoning. Using Judea Pearl’s book, Probabilistic Reasoning in Intelligent Systems, we study different models of plausible reasoning, including probabilistic systems and non-monotonic logic.

Texts: All materials will be photocopied and placed in the Philosophy Department office (E370). There will be selections from a wide variety of sources, with some emphasis placed on two texts:

Hesse, Mary. Models and Analogies in Science. University of Notre Dame Press, 1966.
Pearl, Judea. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.

Requirements: The final grade will be based upon the following scheme:


Class presentations/participation             20%
Short paper (2500 words/8-10 pages)    30%
Term paper (5000 words/16-20 pages)    50%

Web page:

Class Date Readings

Jan. 5         Introduction and examples

J. Priestley, The History and Present State of Electricity, 372-4
C. Maier, The Role of Spectroscopy in the acceptance of the internally structured atom 1860-1920, 47-62
F. Lembeck, Scientific Alternatives to Animal Experiments, 4-12 and 30-37
C. Darwin, On the origin of species, 62-68
S. Jones and D. Pilbeam, The Cambridge Encyclopedia of Human Evolution, 335-340
G. Polya, Mathematics and Plausible Reasoning, Vol. I: Induction and Analogy in Mathematics, 12-22

Jan. 12         Philosophical background on analogy

J.S. Mill, A System of Logic, 364-8
J.M. Keynes, A Treatise on Probability, 222-32, 251-64
J. Agassi, "Discussion: Analogies as Generalizations"
J. Weitzenfeld, "Valid Reasoning by Analogy"


S. Russell, "Analogy by Similarity"

Jan. 19                    M. Hesse, Models and Analogies in Science, chapters 1-2

Jan. 26               M. Hesse, Models and Analogies in Science, chapters 3 and 5


          M. Black, "Metaphor"

Feb. 2 Computational approaches I:

T. Evans, "A Program for the Solution of Geometric-Analogy Intelligence-Test Questions"
P. Winston, "Learning and Reasoning by Analogy"
D. Gentner, "Structure-Mapping: A Theoretical Framework for Analogy"

Feb. 9 Computational approaches II:

K. Holyoak and P. Thagard, "Analogical Mapping by Constraint Satisfaction"
J. Carbonell, "Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition"
K. Ashley, "Arguing by Analogy in Law: A Case-Based Model"

Feb. 16 Analogies in archaeology:

R. Gould and P. Watson, "A Dialogue on the Meaning and Use of Analogy in Ethnoarchaeological Reasoning"
A. Wylie, "An Analogy by Any Other Name is Just as Analogical"
C. Shelley, "Multiple Analogies in Archaeology"


Mar. 2 Confirmation and probabilistic reasoning:

        A. Shimony, "Scientific Inference"
        W. Salmon, "Tom Kuhn Meets Tom Bayes"

Mar. 9                    J. Pearl, Probabilistic Reasoning in Intelligent Systems, chapter 1

Mar. 16                  Pearl, chapter 2

Mar. 23              Pearl, chapter 2 and 8.1

Mar. 30              Pearl, chapter 9

Apr. 6              Pearl, chapter 10