However, later breakthroughs by Leland et al.
In this case, averaging the network traffic over long time scales would smooth its burstiness. Such processes entailed the network traffic to be smoother as the aggregation scale increased, i.e. In the initial moldings of modeling and analyzing network traffic, it was thought that the aggregation of network traffic coming from several sources, in terms of the information per time unit, could be represented by memoryless compound Poisson or Markovian arrival processes. Inácio, in Modeling and Simulation of Computer Networks and Systems, 2015 3.1 Network traffic modeling and analysis So is, for example, a sudden fall of the barometer (together with suitable laws) sufficient for predicting a thunderstorm, but it does not explain it, since the fall of the barometer is not the cause of the thunderstorm, but rather a symptom of its arrival.ĭiogo A.B. Later he had to admit that, although every adequate explanation is (under pragmatically changed circumstances) also a prediction, not every adequate prediction yields also an adequate explanation, for symptoms are often adequate for a prediction, but not for the corresponding explanation. Originally Hempel claimed the structural identity of explanation and prediction.
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In case one deduces as explanandum a general or a statistical law of lesser generality than the one(s) in the explanandum one obtains the reduction of a law or theory in the first case (e.g., explaining the law of free fall by means of the law of gravitation), and a deductive-statistical or D-S explanation of a statistical law by a more comprehensive statistical law in the second. I-S explanations thus relate to the body of scientific knowledge at a given time, i.e., they become ‘epistemically relative.’ The postulate of maximum specificity avoids the ‘explanatory ambiguity,’ i.e., the possibility to give acceptable I-S explanations based on true premises for mutually exclusive explananda. Or, in other words, the explanandum must contain all pertinent statistical laws and those special facts that can be connected with the E-event by statistical laws. In the case of I-S explanation, one must further assume that the explanans is maximally specified, i.e., the explanans must contain all available information that is possibly relevant for the explanation of the explanandum E. In simplified form (one statistical law, one boundary condition) we thus obtain the following model of ‘inductive-statistical ( I-S) explanation’: In case the explanandum contains essentially a statistical law (e.g., for all x: the probability of G, if F is given, equals r and as boundary condition that F is given for some b) the explanatory argument does not deliver a deduction of the explanandum statement E but rather gives inductive support for E, i.e., the explanans shows that E was to be expected with a certain degree r of probability. We thus obtain the following model of ‘deductive-nomological ( D-N) explanation’ as logical deductions containing as premises (‘ explanans’) at least one (general) scientific law L I, as well as initial and boundary conditions C r, and as conclusion (‘ explanandum’) the statement E describing the event in question. Hempel reconstructs scientific explanations of concrete events in nature, history, and society as arguments, i.e.
Wolters, in International Encyclopedia of the Social & Behavioral Sciences, 2001 2.1 Scientific Explanation
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