David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Philosophy of Science 56 (2):185-220 (1989)
One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call AE convergence, demands only that for every sentence in the theorist's language, there is a time at which the theorist settles the status of the sentence. The relative difficulties of these criteria are compared for effective and ineffective agents. We then examine in detail how the enrichment of an agent's hypothesis language makes the task of converging to the truth more difficult. In particular, we parametrize first-order languages by predicate and function symbol arity, presence or absence of identity, and quantifier prefix complexity. For nearly each choice of values of these parameters, we determine the senses in which effective and ineffective agents can converge to the complete truth on an arbitrary structure for the language. Finally, we sketch directions in which our learning theoretic setting can be generalized or made more realistic
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John Earman (1993). Underdetermination, Realism, and Reason. Midwest Studies in Philosophy 18 (1):19-38.
Gustavo Cevolani, Vincenzo Crupi & Roberto Festa (2011). Verisimilitude and Belief Change for Conjunctive Theories. Erkenntnis 75 (2):183-202.
Wolfgang Balzer, Bernhard Lauth & Gerhard Zoubek (1993). A Model for Science Kinematics. Studia Logica 52 (4):519 - 548.
Andrea Pozzali (2007). Can Tacit Knowledge Fit Into a Computer Model of Scientific Cognitive Processes? The Case of Biotechnology. Mind and Society 6 (2):211-224.
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