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. (shrink)

In this paper we refer to a machine learning method that reveals all the if–then rules in the data, and on the basis of these rules issues predictions for new cases. When issuing predictions this method faces the problem of choosing from competing theories. We dealt with this problem by calculating the probability that the rule is accidental. The lower this probability, the more the rule can be `trusted' when issuing predictions. The method was tested empirically and found to be (...) accurate. On a broader scope this approach demonstrates how the dialog between researchers in machine learning and the philosophy of science can be beneficial for both sides. (shrink)

Agassi’s radical skepticism leads to the following practical suggestion: When investors consider the likelihood that a suggested innovation will materialize, they should refer to the question: does this idea (that this innovation materializes) raise or reduce the strangeness of the world? And to answer this question, they should examine if the suggested technology is consistent with accepted scientific theories, and if it explains unexpected phenomena. This recommended method is not based on Bayesian epistemology but on a psychological theory of beliefs (...) consistent with skepticism. (shrink)