David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Synthese 79 (3):515 - 542 (1989)
The logic of scientific discovery is now a concern of computer scientists, as well as philosophers. In the computational approach to inductive inference, theories are treated as algorithms (computer programs), and the goal is to find the simplest algorithm that can generate the given data. Both computer scientists and philosophers want a measure of simplicity, such that simple theories are more likely to be true than complex theories. I attempt to provide such a measure here. I define a measure of simplicity for directed graphs, inspired by Herbert Simon''s work. Many structures, including algorithms, can be naturally modelled by directed graphs. Furthermore, I adapt an argument of Simon''s to show that simple directed graphs are more stable and more resistant to damage than complex directed graphs. Thus we have a reason for pursuing simplicity, other than purely economical reasons.
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References found in this work BETA
George Boolos, John Burgess, Richard P. & C. Jeffrey (2007). Computability and Logic. Cambridge University Press.
Mario Augusto Bunge (1963). The Myth of Simplicity. Englewood Cliffs, N.J.,Prentice-Hall.
Nelson Goodman (1972). Problems and Projects. Indianapolis,Bobbs-Merrill.
L. Laudan (1977). Progress and its Problems: Toward a Theory of Scientific Growth. University of California Press.
Larry Laudan (1984). Science and Values: The Aims of Science and Their Role in Scientific Debate. University of California Press.
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