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Economics is dominated by model building, therefore a comprehension of how such models work is vital to understanding the discipline. This book provides a critical analysis of the economist's favourite tool, and as such will be an enlightening read for some, and an intriguing one for others

Abstract
This article shows that Friedman's experiences with the research conducted at the Statistical Research Group (SRG) significantly shaped his 1953 methodology. These experiences gave him the ideas for a methodology for statistical analysis when dealing with "lousy data," that is, when the conditions under which they are generated are not clear, when the data are "uncontrolled experiences." Two views emerged from his SRG work. One is his view on theoretical assumptions: they only specify and do not determine under which conditions a model or device is expected to work. The other is his view on testing: the working of a device should be tested in comparison with a standard. Assessing the performance of a model with a benchmark model makes irrelevant the uncontrollable, only partly known, complex mass of circumstances under which both models perform. So both views were two sides of the same coin.

AbstractTemplates travel because they offer a tractable format that can be used for model-building in a variety of domains. It is often because of this quality that a particular template is chosen. But one cannot assume that there are always templates ready to model a new phenomenon, and moreover, templates have also been designed at some point. A critical aspect of this designing process is the choice of the mathematical objects with which one hopes to capture this phenomenon. This means that one has to assess which of the properties of different kinds of mathematical objects and which combinations of them are most appropriate. The selection of these objects is based on whether their properties enable the model to perform its function. This selection of mathematical objects is similar to the selection of materials in mechanical design.

I wrote this address during the first weeks of the murderous and devastating invasion of Russia in Ukraine, in a fluctuating mood of anger and sadness. My aim was addressing the issue of the use of history to legitimize ideology, and I was contemplating how I could convince you of the urgency of this issue, and then this war broke out.

This essay discusses Francis Galton's method of inductive inference where the data are photographs of human faces. His aim of induction was to determine the typical characteristics of the natural class to which the individuals belong by composing the relevant photographs in a specific photographic way. The three populations studied by Galton were people suffering tuberculosis, Jews, and criminals. This essay argues that despite the fact that Galton aimed at mechanical objectivity, subjective judgements nevertheless appear to be a necessary part of this kind of inductive inference. At first sight, this seems very much in the line of Lorraine Das-ton and Peter Galison's account of objectivity. They argue that in the twentieth century the awareness arose that mechanical-objective pictures still could contain errors that should be erased by trained judgement. Gal-ton's case of inductive reasoning, however, departs from this account by showing that the correct composites were achieved by a combination of mechanical procedures and untrained judgements. To arrive at the typical characterizations one first has to familiarize oneself with the data, but the familiarization should be done by someone who is not an expert on the cases under study.

Since the February 2020 publication of the article 'Flattening the curve' in The Economist, political leaders worldwide have used this expression to legitimize the introduction of social distancing measures in fighting Covid-19. In fact, this expression represents a complex combination of three components: the shape of the epidemic curve, the social distancing measures and the reproduction number R. Each component has its own history, each with a different history of control. Presenting the control of the epidemic as flattening the curve is in fact flattening the underlying natural-social complexity. The curve that needs to be flattened is presented as a bell-shaped curve, implicitly suggesting that the pathogen's spread is subject only to natural laws. The R value, however, is, fundamentally, a metric of how a pathogen behaves within a social context, namely its numerical value is affected by sociopolitical influences. The jagged and erratic empirical curve of Covid-19 illustrates this. Although the virus has most likely infected only a small portion of the total susceptible population, it is clear its shape has changed drastically. This changing shape is largely due to sociopolitical factors. These include shifting formal laws and policies, shifting individual behaviors as well as shifting various other social norms and practices. This makes the course of Covid-19 curve both erratic and unpredictable.

Since the February 2020 publication of the article 'Flattening the curve' in The Economist, political leaders worldwide have used this expression to legitimize the introduction of social distancing measures in fighting Covid-19. In fact, this expression represents a complex combination of three components: the shape of the epidemic curve, the social distancing measures and the reproduction number [Formula: see text] . Each component has its own history, each with a different history of control. Presenting the control of the epidemic as flattening the curve is in fact flattening the underlying natural-social complexity. The curve that needs to be flattened is presented as a bell-shaped curve, implicitly suggesting that the pathogen's spread is subject only to natural laws. The [Formula: see text] value, however, is, fundamentally, a metric of how a pathogen behaves within a social context, namely its numerical value is affected by sociopolitical influences. The jagged and erratic empirical curve of Covid-19 illustrates this. Although the virus has most likely infected only a small portion of the total susceptible population, it is clear its shape has changed drastically. This changing shape is largely due to sociopolitical factors. These include shifting formal laws and policies, shifting individual behaviors as well as shifting various other social norms and practices. This makes the course of Covid-19 curve both erratic and unpredictable.

The rational expectations revolution was not based only on the introduction of Muth's idea of rational expectations to macroeconomics; this introduction alone cannot explain the more drastic changes to the mathematical toolbox, concepts, and research strategies since the 1980s. The shift from "Keynesian economics" to "new classical economics" is based on a shift from a control engineering approach to an information engineering methodology. This "revolution" was even more radical; it also changed the epistemology and ontology of macroeconomics. The ontology of information engineering, and hence of new classical economics, is a world populated by machines that communicate by the exchange of noisy information. The decisions these machines make are conditioned on the (noisy) information they have about the current state of the world but which at the same time will affect future states. Policy in this world therefore means tracing an optimal trajectory taking all these issues into account. To show this shift in epistemology and ontology, the history of economics is interwoven with the history of mathematics, which cannot be detangled from the emergence of the digital computer. The computer changed the nature of mathematics in a specific way by adopting a new concept of solution, namely the algorithm.

This survey is a witness report of what is happening in the community of historians of econometrics. This survey is a follow-up of an earlier survey which investigated who was active in the history of econometrics. The conclusion of the earlier survey was that interest in the history of econometrics has arisen primarily from within econometrics itself and that its stories have been written mainly by econometricians. The conclusion of the current survey is that econometrics as a discipline remains of interest only to the econometricians but that the artifacts created by econometricians have caught the attention of historians of science.