Online research in philosophy

: Errors in science range along a spectrum from those relatively local to the phenomenon (usually easily remedied in the laboratory) to those more conceptually derived (involving theory or cultural factors, sometimes quite long-term). One may classify error types broadly as material, observational, conceptual or discoursive. This framework bridges philosophical and sociological perspectives, offering a basis for interfield discourse. A repertoire of error types also supports error analytics, a program for deepening reliability through strategies for regulating and probing error.

We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest another approach for meta-methodology based on a conglomeration of tools and strategies (from statistical modeling, experimental design, and related fields) that affords forward looking procedures for learning from error and for controlling error. The resulting “error statistical” appraisal is empirical—methods are appraised by examining their capacities to control error. At the same time, this account is normative, in that the strategies that pass muster are claims about how actually to proceed in given contexts to reach reliable inferences from limited data.

: Bayesians and error statisticians have relied heavily upon examples from physics in developing their accounts of scientific inference. The present essay demonstrates it is possible to analyze H.B.D. Kettlewell's classic study of natural selection from Deborah Mayo's error statistical point of view (Mayo 1996). A comparison with a previous analysis of this episode from a Bayesian perspective (Rudge 1998) reveals that the error statistical account makes better sense of investigations such as Kettlewell's because it clarifies how core elements in the design of experiments are used to minimize erroneous inferences rather than dwelling on whether the strategies used are reasonable.

Many contemporary philosophers rate error theories poorly. We identify the arguments these philosophers invoke, and expose their deficiencies. We thereby show that the prospects for error theory have been systematically underestimated. By undermining general arguments against all error theories, we leave it open whether any more particular arguments against particular error theories are more successful. The merits of error theories need to be settled on a case-by-case basis: there is no good general argument against error theories.

New success criteria of inductive inference in computational learning theory are introduced which model learning total (not necessarily recursive) functions with (possibly everywhere) imprecise theories from (possibly always) inaccurate data. It is proved that for any level of error allowable by the new success criteria, there exists a class of recursive functions such that not all f are identifiable via the criterion at that level of error. Also, necessary and sufficient conditions on the error level are given for when more classes of functions may be identified.

In his paper ?The Error in the Error Theory?[this journal, 2008], Stephen Finlay attempts to show that the moral error theorist has not only failed to prove his case, but that the error theory is in fact false. This paper rebuts Finlay's arguments, criticizes his positive theory, and clarifies the error-theoretic position.

The error statistical account of testing uses statistical considerations, not to provide a measure of probability of hypotheses, but to model patterns of irregularity that are useful for controlling, distinguishing, and learning from errors. The aim of this paper is (1) to explain the main points of contrast between the error statistical and the subjective Bayesian approach and (2) to elucidate the key errors that underlie the central objection raised by Colin Howson at our PSA 96 Symposium.

: The purpose of this paper and its sister paper I (Farrell and Hooker, a) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning. The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape language research. Paper I examined the basic features of SDAL in relation to the early history of ape-language research. In this second paper we examine the reconceptualization of ape-language research following what many conceived to be Terrace's refutation of ape-language. We show that the apparent 'revolution' in our understanding of ape linguistic capacities was not based upon 'revolutionary' research different in kind from 'normal' research. The same processes of self-directed interactive exploration of possibility space, that enables a homing-in upon both error and success, is present in all phases of productive science. Moreover, conceiving science as an SDAL process explains how scientists learn how to learn about their research domain.

: The purpose of this paper and its sister paper (Farrell and Hooker, b) is to present, evaluate and elaborate a proposed new model for the process of scientific development: self-directed anticipative learning (SDAL). The vehicle for its evaluation is a new analysis of a well-known historical episode: the development of ape-language research. In this first paper we outline five prominent features of SDAL that will need to be realized in applying SDAL to science: 1) interactive exploration of possibility space; 2) self-directedness; 3) localization of success and error; 4) Synergistic increase in learning capacity; and 5) continuity of SDAL process across scientific change. In this paper we examine the first three features of SDAL in relation to the early history of ape-language research. We show that this history is readily explicated as a self-directed, ever-finer, delineation of possibility space that enables the localization of both success and error. Paper II examines the last two features against this history.