Online research in philosophy

Ex ante predicted outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. But error rates have error rates. We reapply measurements of uncertainty about the estimation errors of the estimation errors of an estimation treated as branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error, and lead to fatter tails in the projections than in past realizations. Counterfactuals of error rates always lead to fat tails, regardless of the probability distribution used. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) moments higher than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails).

This essay attempts to distinguish and discuss the importance and limitations of different ways of being wrong. At first it is argued that strictly falsifiable knowledge is concerned with simple (instrumental) mistakes only, and thus is incapable of understanding more complex errors (and truths). In order to gain a deeper understanding of mistakes (and to understand a deeper kind of mistake), it is argued that communicative aspects have to be taken into account. This is done in the theory of communicative action, which adds to our knowledge of errors the notion of communicative mistakes: mistakes as obstacles for sincere communication. However, to overcome this still purely negative judgement of errors, two processes are examined in which mistakes are best regarded as developmental steps, that is, steps not only meaningful in their own right (as containing some truth), but also as necessary preconditions for further progress. This would suggest that truth is born out of errors. But if so, one has to understand the wrongness of such errors; how is it that they are erroneous if they (somehow) contain the truth? At the end of this essay, a tentative answer to this question is given.

The types of errors that emerge in the development and maintenance of software are essentially different from the types of errors that emerge in the development and maintenance of engineered hardware products. There is a set of standard responses to actual and potential hardware errors, including: engineering ethics codes, engineering practices, corporate policies and laws. The essential characteristics of software errors require new ethical, policy, and legal approaches to the development of software in the global arena.

In his response to my paper ?The Error in the Error Theory? criticizing his and J. L. Mackie's moral error theory, Richard Joyce finds my treatment of his position inaccurate and my interpretation of morality implausible. In this reply I clarify my objection, showing that it retains its force against their error theory, and I clarify my interpretation of morality, showing that Joyce's objections miss their mark.

It is shown that the physical terms have links with philosophical categories. If scientists ignore these ties then epistemological errors arise. The examples of such errors are considered.

The authors attempt to show that certain forms of behavior of the human immune system are illuminatingly regarded as errors in that system's operation. Since error-ascription can occur only within the context of an intentional/teleological characterization of the system, it follows that such a characterization is illuminating. It is argued that error-ascription is objective, non-anthropomorphic, irreducible to any purely causal form of explanation of the same behavior, and further that it is wrong to regard all errors of the immune system as due to malfunction or maladaptation. <br>.

One of the questions that philosophers discuss is: How can we avoid, or at least reduce, errors when explaining the world? The skeptical answer to this question is: We cannot avoid errors since no statement is certain or even definitely plausible, but we can eliminate some past errors. This book advocates the skeptical position and discusses its practical applications in science, ethics, aesthetics, and politics. It brings philosophy down to earth and comprises an outline of a skeptical guide to the real world.

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.

This paper provides examples drawn from the author’s experience that support the conclusion that errors and deceptions in archival science are often not easily or quickly corrected. The difficulty in correcting errors and deceptions needs wider recognition if it is to be overcome. In addition, the paper discusses how subtle abuses introduce errors into the archival literature.

When observing or measuring phenomena, errors are inevitable, one can only aspire to reduce these errors as much as possible. An obvious strategy to achieve this reduction is by using more precise instruments. Another strategy was to develop a theory of these errors that could indicate how to take them into account. One of the greatest achievements of statistics in the beginning of the 19th century was such a theory of error. This theory told the practitioners that the best thing they could do is taking the arithmetical mean of their observations. This average would give them the most accurate estimate of the value they were searching for. Soon after its invention, this method made a triumphal march across various sciences. However, not in all sciences one stood waving aside. This method, namely, only worked well when the various observations were made under similar circumstances and when there were very many of them. And this was not the case for e.g. meteorology and actuarial science, the two sciences discussed in this paper.