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In policies targeting environmental and health hazards, an effort is frequently made to anticipate and avert more or less probable adverse events. In this context, computerized models are frequently portrayed as superior knowledge tools, for their capacity to extrapolate from existing data and predict hazards. This paper looks at the historical development and use of such models in regulation, with the specific example of structure-activity relationships (SARs) in the regulation of new industrial chemicals at the US Environmental Protection Agency (EPA). It asks how evidential culture(s) in a regulatory organization change, in particular how new methods and forms of knowledge find their place alongside others to forge regulatory decisions. The development and application of, first, a qualitative approach to structure-activity relationships, and then of quantitative models, show that the EPA had the necessary autonomy to imagine and adjust a method emerging in the research environment to respond to regulatory needs. This can be understood from a coproductionist perspective, if adjusted to take into account the bureaucratic knowledge that mediates the imagining and application of prediction in regulatory practice.

In this paper we discuss the kind of expert judgement demanded by the development of a particular class of models known as “Quantitative Structure-Activities Relationship” (QSAR) models, used to predict the toxicity of chemical substances, for regulatory and other purposes. We analyse the production of these models, and attempts at standardizing them. We show that neither a technical nor a procedural standardization is possible. As a consequence, QSAR models cannot ground a production of knowledge along the lines of “mechanical objectivity” or “regulatory objectivity”. Instead, QSAR models imply that expert judgement is situated, re-worked for each new case, and implies an active intervention of the individual expert. This has important consequences for risk governance based on models. It makes transparency a central concern. It also means that new asymmetries emerge, between companies developing sophisticated models and individual experts in regulatory agencies in charge of assessing these models.

In recent years, the social sciences have increasingly investigated ways in which futures are anticipated, fostered, and pre-empted. However, less attention has been given to how various predictive approaches inform different ways of acting in the present. Our article presents the results of an investigation into the current practices and agendas of forest scientists and managers in France. We first suggest how an anticipation of environmental futures is coming to the fore as an emerging field of expertise and practices in forest sciences, including predicting but also monitoring, preparing and adapting to projected futures. We then account for the co-existence of three “micro-regimes” of anticipation combining a certain approach to the forest, a certain vision of the future, and a certain type of scientific predictive approach, including different anticipatory objectives, different modelling practices, and different interactions between research and management: i/ Adapting forestry to future climates; ii/ Predicting Future Tree Biology; iii/ Monitoring forests as indicators of climate change.

Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in order to try to understand the predictions of the company’s algorithm. Using the same method of calculation on the same data, the seismologist arrived at a different, more cautious interpretation of the algorithm's capacity to predict crime. How were these predictive analyses formed on the two sides of the Atlantic? How do predictive algorithms come to exist differently in these different contexts? How and why is it that predictive machines can foretell a crime that is yet to be committed in a California laboratory, and yet no longer work in another laboratory in Chambéry? In answering these questions, I found that machine learning researchers have a moral vision of their own activity that can be understood by analyzing the values and material consequences involved in the evaluation tests that are used to create the predictions.

Participants in Belize’s water sector encounter challenges in identifying and living within shifting environments, and in conducting the work of expectation given ambiguities in rainfall patterns, historical records, institutional resources and political interests. Policymakers, scientists and practitioners generate and organise different kinds of foreknowledge as they anticipate future quantities, qualities and distribution of water, amid questions about the patterning of expertise and the nature of water as a resource. I present three ethnographic vignettes to address: the navigation of nonknowledge in water policy implementation; the frictions that arise in modelling workshops where trainees generate data-driven maps of future environments; and the situated sensing of environmental change. Building on a concept of ‘reckoning’ that highlights cross-cutting technical, relational, political and affective dimensions of meaning-making, I situate these foreknowledge practices in the socio-material contexts of environmental perception, socio-economic development, and the political lives of anticipation.

Ongoing debates about the need to deeply transform energy systems worldwide have spurred renewed scholarly interest in the role of future-visions and foreknowledge in energy policy. Forecasts and scenarios are in fact ubiquitous in energy debates: commonly calculated using energy models, they are employed by governments, administrations and civil society actors to identify problems, choose between potential solutions, and justify specific forms of political intervention. This article contributes to these debates through a historic study of foreknowledge-making – modelling, forecasting, and scenario-building – and its relationship to the structuring of ‘energy policy’ as an autonomous policy domain in France and Germany. It brings together two strands of literature: work in the anthropology of politics on ‘policy assemblages’, and STS research on the ‘performative’ effects of foreknowledge. The main argument is that new ways of assembling energy systems in energy modelling, and of bringing together policy networks in scenario-building and forecasting exercises, can contribute to policy change. To analyse the conditions under which such change occurs, the article focuses on two periods: the making of national energy policies as ‘energy supply policies’ in the post-war decades; and challenges to dominant approaches to energy policy and energy modelling in the 1970s and 1980s. It concludes by arguing that further research should not only focus on the effects of foreknowledge on expectations and beliefs (‘discursive performativity’), but also take into account how new models ‘equip’ political, administrative and market actors (‘material performativity’), and how forecasting practices recompose and shape wider policy worlds (‘social performativity’).

Greenhouse gas emission scenarios are key in analyses of human interference with the climate system. They are mainly produced by one category of computer models: Integrated Assessment Models (IAMs). We analyse how IAM research organised into a community around the production of socio-economic scenarios during the preparation of the IPCC AR5 (2005-2014). We seek to describe the co-emergence of a research community, its instruments, and its domain of applicability. We highlight the role of the IPCC process in the making of the IAM community, showing how IAMs worked their way to an influent position. We then survey three elements of the repertoire that served to organise collective work on scenarios in interaction with the IPCC and the European Union, and which now frames the community and its epistemic practices. This repertoire needs to articulate epistemic practices with the pursuit of policy relevance, which shows how epistemic communities and patterns of co-production materialise in practical arrangements.

Global models of agriculture act as the epistemic basis for quantitative foresight, which guides international policymaking and research on agriculture. With the new political sociology of science as a backdrop, this article studies the actors who develop and use these models through the lens of field theory. Contributing to the dialogue between the neo-institutionalist field theory and its Bourdieusian version, it describes the structure and the dynamics of the strategic action field of modelling organizations, using the Bourdieusian notions of “succession” and “subversion” to refine the characterization of challengers. It also discusses the insights of the Bourdieusian concept of “homology” to analyse the relations between the field of model producers and the field of model users. Whereas Bourdieu provides a primarily descriptive account of homologies, which are close to a “social magic without magicians” for Roueff, the present text describes magicians doing the work of producing homologies. Some modellers use intercomparison to reduce competition and to have their models used in the field of global governance, thus strategically producing homologies, while resolving the main modelling conflict of the field. These actors benefit from the recent change in the modelling field under the influence of climate change, to behave as what Fligstein and McAdam have called “institutional entrepreneurs”. The article concludes that this amended version of field theory makes it possible to describe the co-construction of a range of models developed by competing organizations and the controversial making of global agricultural governance. Doing so, it complements the co-production framework, which often focuses on a given site of expertise production and a site of global governance.