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Science discovering a divinely designed world is fundamentally different from science investigating a “dappled world” (Cartwright, 1999). The first world is written in elegant mathematics which describes the simple laws that rule them, the other world is too complex and messy – a “patchwork of laws” – for any unified treatment. While the first world is lighted by itself and principally transparent, the second world is much darker, so we need lanterns to have a look at it:The effort of the economist is to see, to picture the interplay of economic elements. The more clearly cut these elements appear in vision, the better; the more elements he can grasp and hold in mind at once, the better. The economic world is a misty region. The first explorers used unaided vision. Mathematics is the lantern by which what before was dimply visible now looms up in firm, bold outlines. The old phantasmagoria disappear. We see better. We see also further. (Fisher, 1925, p. 119)Today, these mathematical pictures are called models.

Reflections on this special issue draw three themes out of the papers: the problem of fashioning the tools of science; the variability of life that poses challenges to scientific investigation; and the importance of space in myriad different ways to how we think about life. Although these themes may appear disconnected, they are in fact finely woven in various ways into the volume’s discussions, which range over science studies (broadly conceived) as well as economic history.

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.

The model-based enquiry depends on the form in which models are formulated and represented. When economists select a model as an efficient reasoning tool, they may first consider the type of model whose inherited epistemic virtue and reasoning rules best fits their needs. This chapter studies the dependence between the different forms of models and scientific knowledge by considering a particular form of model, that is, the diagram. This chapter draws from the history of location theory, which provides us with an example of how economists reasoned with diagrams, how their particular geometric shapes became an idealized landscape, and how they reasoned into them to account for actual spatial patterns of economic activity providing the opportunity for policy advice. Three different diagrams are examined: Johann Heinrich von Thünen’s concentric rings of agricultural land use, Alfred Weber’s triangles of industrial locations, and Walter Christaller’s hexagons of market area.

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.

Samuels always saw the history of economics as intellectual history (Samuels, 1974). The history of economics could not be confined to a record of doctrines economists have advanced, particularly as found in a handful of key written texts, since these doctrines had to be understood in their historical context along with many other types of historical evidence and materials. That is, economic doctrines do not stand on their own, however plausible and well-argued they may appear to be, but have their meaning and credibility according to how well they along with many other historical materials speak to the issues of their time. While this seems an obvious and correct point to make about how to understand the history of economics, Samuels argued that it turns out to be more far-reaching than it initially appears when one considers the character of the world which economic doctrines must somehow explain together with the problem of representation.First, social reality is itself heterogeneous and ambiguous … [which] permits a complex and heterogeneous body of knowledge thereof. Second, our modes of intellectualizing and interpreting social reality are complex and heterogeneous: there are diverse epistemological credentials, diverse modes of thought, and diverse systems of rationality. (Samuels, 1974, p. 306)