Around the globe, economists affect markets by saying what markets are doing, what they should do, and what they will do. Increasingly, experimental economists are even designing real-world markets. But, despite these facts, economists are still largely thought of as scientists who merely observe markets from the outside, like astronomers look at the stars. Do Economists Make Markets? boldly challenges this view. It is the first book dedicated to the controversial question of whether economics is performative--of whether, in some cases, (...) economics actually produces the phenomena it analyzes.The book's case studies--including financial derivatives markets, telecommunications-frequency auctions, and individual transferable quotas in fisheries--give substance to the notion of the performativity of economics in an accessible, nontechnical way. Some chapters defend the notion; others attack it vigorously. The book ends with an extended chapter in which Michel Callon, the idea's main formulator, reflects upon the debate and asks what it means to say economics is performative.The book's insights and strong claims about the ways economics is entangled with the markets it studies should interest--and provoke--economic sociologists, economists, and other social scientists.In addition to the editors and Callon, the contributors include Marie-France Garcia-Parpet, Francesco Guala, Emmanuel Didier, Philip Mirowski, Edward Nik-Khah, Petter Holm, Vincent-Antonin Lépinay, and Timothy Mitchell. (shrink)

Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets (...) drawn from seven domains, each of which is considered in the literature to constitute Big Data. The results demonstrate that only a handful of datasets possess all seven traits, and some do not possess either volume and/or variety. Instead, there are multiple forms of Big Data. Our analysis reveals that the key definitional boundary markers are the traits of velocity and exhaustivity. We contend that Big Data as an analytical category needs to be unpacked, with the genus of Big Data further delineated and its various species identified. It is only through such ontological work that we will gain conceptual clarity about what constitutes Big Data, formulate how best to make sense of it, and identify how it might be best used to make sense of the world. (shrink)

This paper examines one of the historical antecedents of Big Data, the social physics movement. Its origins are in the scientific revolution of the 17th century in Western Europe. But it is not named as such until the middle of the 19th century, and not formally institutionalized until another hundred years later when it is associated with work by George Zipf and John Stewart. Social physics is marked by the belief that large-scale statistical measurement of social variables reveals underlying relational (...) patterns that can be explained by theories and laws found in natural science, and physics in particular. This larger epistemological position is known as monism, the idea that there is only one set of principles that applies to the explanation of both natural and social worlds. Social physics entered geography through the work of the mid-20th-century geographer William Warntz, who developed his own spatial version called “macrogeography.” It involved the computation of large data sets, made ever easier with the contemporaneous development of the computer, joined with the gravitational potential model. Our argument is that Warntz's concerns with numeracy, large data sets, machine-based computing power, relatively simple mathematical formulas drawn from natural science, and an isomorphism between natural and social worlds became grounds on which Big Data later staked its claim to knowledge; it is a past that has not yet passed. (shrink)