Abstract
When Chapter 2 offers the characterization of scientific prediction and its kinds in economics, there is an initial issue: the relation between scientific explanation and scientific prediction. This relation leads to the problem of symmetry or asymmetry between them and to the question of the methodological weight, which concerns to accommodation and prediction. This discussion is followed by the characterization of scientific prediction: the concept of prediction and its two main uses regarding science. Thereafter, there is an analysis of the distinction between qualitative and quantitative predictions, which is considered in three steps: (i) the features of the qualitative prediction and the role of generic predictions; (ii) the traits of quantitative prediction and the existence of differences between sciences; and (iii) types of information used for prediction. Finally, there is a special emphasis on diversity of economic predictions, because there are types of economic predictions; but distinctions can be made also between “foresight,” “prediction,” “forecasting,” and “planning.”
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He was also influential through Hempel (1966).
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“As such, a prediction could not be an explanation, for an explanation, according to Peter [Carl G. Hempel] is an argument” (Salmon 1993, p. 232). His initial ideas on prediction are in Salmon (1953 and 1957).
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“In economics is well-known that the best model for explaining is not necessarily the best model for predicting the future,” José Ramón Cancelo, Personal Communication, January 2007.
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Reichenbach’s approach is analyzed in Gonzalez (1995), where his whole production is considered in this regard.
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He also offers us a heuristic account of this issue: “Predictivism now proclaims that, where E is evidence for T, E confirms T more strongly when T was not built to fit E” (Barnes 2008, p. 2).
Among the critics of predictivism in terms of novelty, based on his perspective on history of science, is Stephen Brush: “The predictivist thesis gains little empirical support from the history of science. Any attempt to rescue it by redefining novelty in terms of what the theorist knew, when he knew it, and what he did or could have done with the information puts the philosopher in the position of a Watergate investigator without Deep Throat” (Brush 1995, p. 41). His analysis of scientific prediction based on historical grounds can also be seen in Brush (1989).
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On Lakatos’ predictivism, see Sect. 4.4.2. An Overemphasis on the Role of Prediction?
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In this regard, some distinctions can be found in Lipton (1990, 1991). More recently, Barnes has proposed four “species” of predictivism: (1) “unvirtuous thin predictivism,” (2) “unvirtuous tempered predictivism,” (3) “virtuous thin predictivism,” and (4) “virtuous tempered predictivism” (2008), p. 81.
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There are more options in the context of “novel facts,” such as the heuristic approach and the individual perspective, as can be seen in Chap. 4.
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This should be understood as a conceptual difference with “retrodiction,” even though the notion of “novel fact” is clearly more sophisticated than the temporally new event. On the diversity of new facts, cf. Gonzalez (2001e), esp., pp. 505–508.
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Popper was manifestly interested in the problem of the interdependence between prediction and the predicted event in social sciences, cf. Chap. 3, Sect. 3.5.3.
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This distinction is drawn by Hausman in a personal communication (21 January 1996) and clarified afterwards (20 January 1998).
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Predicting the future is also a usage in papers related to physics, such as Hogarth (1993): “Predicting the future in relativistic spacetimes.”
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According to this characterization of prediction, to state that the rate of inflation in the first half of 1910 in USA was 3.5 % is not to make a prediction of a “novel fact”: that is not a future event.
The position held here is open to a Lakatosian sense of “novel fact”: it includes the possibility that “Newtonian scientists predicted the existence and exact motion of small planets which had never been observed before. Or … Einstein’s programme … made the stunning prediction that if one measures the distance between two stars in the night and if one measure the distance between them during the day (when they are visible during an eclipse of the sun), the two measurements will be different. Nobody had thought to make such an observation before Einstein’s programme” (Lakatos 1974b, p. 5).
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“The ‘predictions’ by which the validity of a hypothesis is tested need not be about phenomena that have not yet occurred, that is, need not be a forecast of future events; they may be about phenomena that have occurred but observations on which have not yet been made or are not known to the person making the prediction. For example, a hypothesis may imply that such and such must have happened in 1906, given some other known circumstances. If a search of the records reveals that such and such did happen, the prediction is confirmed; if it reveals that such and such did not happen, the prediction is contradicted” (Friedman 1953, p. 9).
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On the characteristics and types of scientific explanations, cf. Gonzalez (2002b, pp. 21–28).
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These traits are dealt in Gonzalez (1996c, p. 207).
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The interest in the relation between reason and prediction appears in Blackburn (1973).
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The dynamic status of scientific knowledge, which involves historicity, makes it more difficult to give timeless examples of unpredictable phenomena. A possible example is to state the impossibility of the full knowledge of the composition of the planets in the very distant galaxies.
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This distinction was suggested to me by Patrick Suppes at Stanford University. Personal communication, 18 November 1993.
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It is also the case that social predictions can have consequences in decision-making that are clearly described, cf. Simon (1954).
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“I should like to observe that there appear to be two quite distinct sorts of regularity in human affairs—the one represented by prediction of the arrival of a train, the other represented by prediction of the number of people who will die in auto accidents over Labor Day weekend. The latter depends, in one form of another, upon the law of large numbers; the former, upon a strict determination of behavior by program” (Simon 1958/1982, p. 390).
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This problem is connected with the topics of Chap. 5.
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The distinction “accuracy”–“precision” regarding economic prediction is analyzed in Chap. 9.
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His views on prediction are in Lawson (1985).
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In 2012 Alvin Roth was awarded with the Noble Prize in Economics.
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Even the notion of “law” in economics is under discussion, cf. Hausman (2000). The debate may be seen also in the context of the social sciences as a whole, where it is discussed if there can be laws of social science. In addition, it is commonly assumed that theories in the social sciences are less advanced than those of physics.
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This affects the features of “causality” in social sciences, an issue discussed by philosophers as well as economists.
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“Subjectual” expresses what is objective in the subjects, i.e., the factors that appear in the individual agents but they are not reducible to pure idiosyncratic elements of each one taking in isolation. In this sense, the need of decision-making by individual agents regarding the future is “subjectual,” whereas the decisions as such of each one of the agents could be subjective (i.e., the decisions might be taken through biographical motivations or under specific circumstances).
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See also Pulido (1989).
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According to Clive Granger (Nobel Prize in 2003), one of the controversies concerning the modeling process in economics is directly related to the use of economic theory: “Should the model specification be based on economic theory? Should the model be based on some well-founded theory, such as an optimizing typical agent theory?… What if there are several competing theories?” (Granger 1990b, p. 14).
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In his analysis, he considers the words “prediction” and “forecasting” as completely interchangeable, cf. Granger (1989), p. 2, note.
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On this issue of the distinction “accuracy”–“precision,” see Chap. 9, Sect. 9.5. From a different point of view, there is an interesting analysis on the theoretical virtues of parsimoniousness, unification, and non ad hocness, on the dispute about Bayesianism, and on empiricism and scientific realism, cf. Foster and Sober (1994).
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It seems rather obvious that there is no unanimity in this terminology, because very frequently the use of “prediction” and “forecasting” is completely interchangeable. On the use of “forecasting,” see, for example, Anderson (1979), Irvine and Martin (1984), Klein (1984), Shim and Siegel (1988), and Woods and Fildes (1976).
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The question of level of error is always a crucial one, not only for social sciences but also for natural sciences: “we regard the total absence of error as radically implausible. Even if nature were completely deterministic, there still would be observational errors” (Foster and Sober 1994, p. 10).
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“Forecasting will be limited to the extrapolations based on empirical models or data exploration, whereas prediction will be formed from theoretical model” (Granger 2012, p. 312).
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Ontologically, the reality itself which is predicted do not require to have eo ipso posterior existence to the predictive statement, because it is legitimate to say in advance a social or economical event which, strictly speaking, is already going on (as was the case in astronomy with the prediction of Neptune or in quantum mechanics with the existence of neutrino). In human contexts, when a person is qualified as “predictable” could be seen as a “reliable person” and that means that the person is well known.
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“Typically there will be an infinite array of generalisations which are compatible with the available observational evidence, and which are therefore, as yet, unrefuted. If we were free to choose arbitrarily from among all the unrefuted alternatives, we could predict anything whatever. If there were no rational basis for choosing from among all the unrefuted alternatives, then, as I think Popper would agree, there would be no such thing as rational prediction” (Salmon 1981, p. 117).
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Gonzalez, W. (2015). Characterization of Scientific Prediction and its Kinds in Economics. In: Philosophico-Methodological Analysis of Prediction and its Role in Economics. Theory and Decision Library A:, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-08885-3_2
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