Douven (in press) observes that Schurz's meta-inductive justification of induction cannot explain the great empirical success of induction, and offers an explanation based on computer simulations of the social and evolutionary development of our inductive practices. In this paper, I argue that Douven's account does not address the explanatory question that Schurz's argument leaves open, and that the assumption of the environment's induction-friendliness that is inherent to Douven's simulations is not justified by Schurz's argument.

The inaugural title in the new, Open Access series BSPS Open, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single (...) formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it. Which that is, and its extent, is determined by the facts prevailing in that domain. Paying close attention to how inductive inference is conducted in science and copiously illustrated with real-world examples, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. (shrink)

In the methodology of scientific research programs (MSRP) there are important features on the problem of prediction, especially regarding novel facts. In his approach, Imre Lakatos proposed three different levels on prediction: aim, process, and assessment. Chapter 5 pays attention to the characterization of prediction in the methodology of research programs. Thus, it takes into account several features: (1) its pragmatic characterization, (2) the logical perspective as a proposition, (3) the epistemological component, (4) its role in the appraisal of research (...) programs, and (5) its place as a value for scientific research. -/- The notion of “novel facts” is highly relevant in his conception, where several aspects are involved: the directions of novel facts, the different kinds of novelty, and the transition from six possible options of “novel facts” to four choices. Thereafter, the prediction of novel facts as the criterion of appraisal is considered. On the one hand, this requires analyzing the theoretical, empirical, and heuristic possibilities of appraisal; and, on the other, whether there is an overemphasis on the role of prediction in methodology of scientific research programs. As a consequence, there is an analysis of Lakatos’ criterion of appraisal in MSRP and economics. (shrink)

Reichenbach emphasizes the central importance of prediction, which is—for him—the principal aim of science. This paper offers a critical reconstruction of his concept of prediction, taking into account the different periods of his thought. First, prediction is studied as a key factor in rejecting the positivism of the Vienna Circle. This part of the discussion concentres on the general features of prediction before Experience and Prediction (EP) (section 1). Second, prediction is considered in the context of Reichenbach's disagreements with his (...) contemporaries—Carnap and Popper—(section 2). Pointing out these differences gives an additional basis for understanding how Reichenbach saw “prediction” in the period when EP was written. Third, Reichenbach's theoretical framework of prediction is analysed following EP. This analysis studies the semantical, logical, epistemological and methodological bases of his concept of prediction (section 3). Fourth, Reichenbach's conception of prediction, based on an objectivist interpretation of probabilities, is compared with the perspective on prediction of subjective Bayesians (the present personalists). This comparison (section 4) illustrates Reichenbach's views regarding the links of prediction with probability. Fifth, innovations and elements of continuity after EP are noted which give a more complete picture of Reichenbach's thought on prediction (section 5). This contributes to a comprehensive characterization of his concept of prediction. Finally, there is an assessment of his whole view of the matter and a presentation of the ingredients for a satisfactory alternative (section 6). (shrink)

The extent of harm and suffering caused by the coronavirus pandemic has prompted a debate about whether the epidemic could have been contained, had the gravity of the crisis been predicted earlier. In this paper, the philosophical debate on predictive reasoning is framed by Hume’s problem of induction. Hume argued that it is rationally unjustified to move from the finite observations of past incidences to the predictions of future events. Philosophy has offered two major responses to the problem of induction: (...) the pragmatic induction of Peirce and the critical rationalism of Popper. It is argued that of these two, Popper’s critical rationalism provides a more potent tool for preparing for unanticipated events such as the Covid-19 pandemic. Popper’s notion of risky predictions equips strategic foresight with clear hypotheticals regarding potential crisis scenarios. Peirce’s pragmatic induction, instead, leans on probabilities that are slower to be amended as unexpected events start unfolding. The difference between the two approaches is demonstrated through a case study of the patterns of reasoning within the World Health Organization in the early stages of the coronavirus pandemic. (shrink)

The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have an inherent inductive bias, that wants justification. We argue that many standard learning algorithms should rather (...) be understood as model-dependent: in each application they also require for input a model, representing a bias. Generic algorithms themselves, they can be given a model-relative justification. (shrink)

One of the first to criticize the verifiability theory of meaning embraced by logical empiricists, Reichenbach ties the significance of scientific statements to their predictive character, which offers the condition for their testability. While identifying prediction as the task of scientific knowledge, Reichenbach assigns induction a pivotal role, and regards the theory of knowledge as a theory of prediction based on induction. Reichenbach's inductivism is grounded on the frequency notion of probability, of which he prompts a more flexible version than (...) that of Richard von Mises. Unlike von Mises, Reichenbach attempts to account for single case probabilities, and entertains a restricted notion of randomness, more suitable for practical purposes. Moreover, Reichenbach developed a theory of induction, absent from von Mises's perspective, and argued for the justification of induction. This article outlines the main traits of Reichenbach's inductivism, with special reference to his book Experience and prediction. (shrink)

Reichenbach emphasizes the central importance of prediction, which is—for him—the principal aim of science. This paper offers a critical reconstruction of his concept of prediction, taking into account the different periods of his thought. First, prediction is studied as a key factor in rejecting the positivism of the Vienna Circle. This part of the discussion concentres on the general features of prediction before Experience and Prediction (EP) (section 1). Second, prediction is considered in the context of Reichenbach's disagreements with his (...) contemporaries—Carnap and Popper—(section 2). Pointing out these differences gives an additional basis for understanding how Reichenbach saw “prediction” in the period when EP was written. Third, Reichenbach's theoretical framework of prediction is analysed following EP. This analysis studies the semantical, logical, epistemological and methodological bases of his concept of prediction (section 3). Fourth, Reichenbach's conception of prediction, based on an objectivist interpretation of probabilities, is compared with the perspective on prediction of subjective Bayesians (the present personalists). This comparison (section 4) illustrates Reichenbach's views regarding the links of prediction with probability. Fifth, innovations and elements of continuity after EP are noted which give a more complete picture of Reichenbach's thought on prediction (section 5). This contributes to a comprehensive characterization of his concept of prediction. Finally, there is an assessment of his whole view of the matter and a presentation of the ingredients for a satisfactory alternative (section 6). (shrink)

One of the first to criticize the verifiability theory of meaning embraced by logical empiricists, Reichenbach ties the significance of scientific statements to their predictive character, which offers the condition for their testability. While identifying prediction as the task of scientific knowledge, Reichenbach assigns induction a pivotal role, and regards the theory of knowledge as a theory of prediction based on induction. Reichenbach’s inductivism is grounded on the frequency notion of probability, of which he prompts a more flexible version than (...) that of Richard von Mises. Unlike von Mises, Reichenbach attempts to account for single case probabilities, and entertains a restricted notion of randomness, more suitable for practical purposes. Moreover, Reichenbach developed a theory of induction, absent from von Mises’s perspective, and argued for the justification of induction. This article outlines the main traits of Reichenbach’s inductivism, with special reference to his book Experience and prediction. (shrink)

Many arguments that show p to be enthymematic (in an argument for q) rely on claims like “if one did not believe that p, one would not have a reason for believing that q.” Such arguments are susceptible to the neg-raising fallacy. We tend to interpret claims like “X does not believe that p” as statements of disbelief (X's belief that not-p) rather than as statements of withholding the belief that p. This article argues that there is a tendency to (...) equivocate in arguments for the enthymematicity of arguments (e.g., Lewis Carroll's paradox, Hume's problem) as well as in arguments for the enthymematicity of action explanations (e.g., arguments for psychologism and for explanatory individualism). The article concludes with a warning, because the equivocation is often helpful in teaching and because neg-raising verbs include philosophically vital verbs: desire, want, intend, think, suppose, imagine, expect, feel, seem, appear. (shrink)