tomorrow?” The learner would then survey his list, count the number of remaining in which it does come up, and report the ratio with the number remaining in which it does not. Doing this, the learner answers, indistinguishable from “50:50”, chance, just as it was before any observations had been made. What went wrong? The initial, bias-free assumption that all possible are equally probable in effect made each event probabilistically irrelevant to any other event. Thus, despite the obvious pattern of sunrises, no number of past observations of sunrises could change what one expects about the next sunrise. The same point can be put in logical terms, without reference to probability. No one particular atomic fact—the possession of a given property by a given entity at a given time—logically implies any other particular atomic fact. So, a perfectly logical, unbiased system of inference would leave the learner nothing to say about tomorrow’s sunrise. Carnap (1950) went on to show that if, instead, our learner had begun with certain notions about which combinations of entities and properties are more likely than others, then observations of individual facts could quickly displace one’s view about tomorrow’s sunrise away from chance. But what of it? Bias could be a self-fulfilling prophecy, a form of epistemic pre-destination in which one’s starting point distorts forever the future conduct of one’s inquiry. Any connection with overcoming error or gaining accuracy would be merest happenstance. A dilemma: either be free of bias, and so unable to learn, or be biased, and so unable to unlearn. It was subsequent development of a truly dynamical theory of learning that provided the way out of this seeming dilemma and laid the groundwork for
tomorrow?” The learner would then survey his list, count the number of remaining in which it does come up, and report the ratio with the number remaining in which it does not. Doing this, the learner answers, “50:50”, indistinguishable from chance, just as it was before any observations had been made. What went wrong? The initial, bias-free assumption that all possible are equally probable in effect made each event probabilistically irrelevant to any other event. Thus, despite the obvious pattern of sunrises, no number of past observations of sunrises could change what one expects about the next sunrise. The same point can be put in logical terms, without reference to probability. No one particular atomic fact—the possession of a given property by a given entity at a given time—logically implies any other particular atomic fact. So, a perfectly logical, unbiased system of inference would leave the learner nothing to say about tomorrow’s sunrise. Carnap (1950) went on to show that if, instead, our learner had begun with certain notions about which combinations of entities and properties are more likely than others, then observations of individual facts could quickly displace one’s view about tomorrow’s sunrise away from chance. But what of it? Bias could be a self-fulfilling prophecy, a form of epistemic pre-destination in which one’s starting point distorts forever the future conduct of one’s inquiry. Any connection with overcoming error or gaining accuracy would be merest happenstance. A dilemma: either be free of bias, and so unable to learn, or be biased, and so unable to unlearn. It was subsequent development of a truly dynamical theory of learning that provided the way out of this seeming dilemma and laid the groundwork for
Railton_Oct20_Seligmanetalinprep.docx