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Provides a comment on Reiss’ “Mathematics in economics: Schmoller, Menger and Jevons”.

In the 19th century, John Stuart Mill argued that economics could not be an inductive science because it lacks that which is the essence of inductive science, viz. the experiment. Since there is no experiment, economic claims cannot be established inductively but must instead be justified deductively on the basis of antecedently accepted theoretical claims about human behaviour.

Investigates the economic methodologies of Carl Menger, William Stanley Jevons and Gustav Schmoller with respect to the issue of whether mathematics is or is not an adequate language to express economic relationships. First, Menger’s and Jevons’s respective methodologies are identified as Aristotelian which means, inter alia, that economic properties are real, are naturally related to each other, exist as part of the observable world and can be separated (in thought or otherwise) from other properties. Second, it is shown how this general Aristotelian outlook has very different implications for Menger’s and Jevons’s thinking about mathematics. Third, these two “monogenetic” views are contrasted with Gustav Schmoller’s “polygenetic” approach which holds that a purely deductive economics, based on a small number of self‐evident principles, is inadequate for social purposes.

During the last decade or so, philosophers of science have shown increasing interest in scientific models and modeling. The primary impetus seems to have come from the philosophy of biology, but increasingly the philosophy of economics has been drawn into the discussion. This paper will focus on the particular subset of this literature that emphasizes the difference between a scientific model being explanatory and one that provides explanations of specific events. The main differences are in the structure of the models and the characteristics of the explanatory target. Traditionally, scientific explanations have been framed in terms of explaining particular events, but many scientific models have targets that are hypothetical patterns: “patterns of macroscopic behavior across systems that are heterogeneous at smaller scales” (Batterman & Rice, 2014, p. 349). The models with this characteristic are often highly idealized, and have complex and heterogeneous targets; such models are “central to a kind of modeling that is widely used in biology and economics” (Rohwer & Rice, 2013, p. 335). This paper has three main goals: (i) to discuss the literature on such models in the philosophy of biology, (ii) to show that certain economic phenomena possess the same degree of heterogeneity and complexity often encountered in biology (and thus, that hypothetical pattern explanations may be appropriate in certain areas of economics), and (iii) to demonstrate that Hayek’s arguments about “pattern predictions” and “explanations of the principle” are essentially arguments for the importance of this type of modeling in economics.

A heated debate surrounds the significance of reproducibility as an indicator for research quality and reliability, with many commentators linking a “crisis of reproducibility” to the rise of fraudulent, careless, and unreliable practices of knowledge production. Through the analysis of discourse and practices across research fields, I point out that reproducibility is not only interpreted in different ways, but also serves a variety of epistemic functions depending on the research at hand. Given such variation, I argue that the uncritical pursuit of reproducibility as an overarching epistemic value is misleading and potentially damaging to scientific advancement. Requirements for reproducibility, however they are interpreted, are one of many available means to secure reliable research outcomes. Furthermore, there are cases where the focus on enhancing reproducibility turns out not to foster high-quality research. Scientific communities and Open Science advocates should learn from inferential reasoning from irreproducible data, and promote incentives for all researchers to explicitly and publicly discuss (1) their methodological commitments, (2) the ways in which they learn from mistakes and problems in everyday practice, and (3) the strategies they use to choose which research components of any project need to be preserved in the long term, and how.

This introduction to the Symposium “Curiosity, Imagination, and Surprise” discusses some of the characteristics of Mary Morgan’s approach to study science, which she labels as “naturalized philosophy of science.” One of these characteristics is the usage of a carefully chosen vocabulary. These concepts are usually unconventional and open-ended with the aim of illuminating the practice under study. Another characteristic of her approach is that it is curiosity-driven, which becomes clear by the kind of typical questions she asks. A third characteristic is that her approach is case-study based, with its typical features, such as the investigation of a bounded “real-life” whole, its attitude of open-endedness, the usage of multiple research methods and its complex, often-narrated outcome.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.