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This article explores the role of the so-called in silico experiments used in molecular biology. It is based on the analysis of some papers that present scientific applications which rely on in silico experiments. By means of this study I found two basic ways of viewing them. According to the first view, the in silico experiment is a computer program that realizes some specific operations: it constitutes some particular experimental conditions, which allow us to investigate biological phenomena, and which complement those present in in vivo and in vitro experiments. According to the second view, in silico experimentation has a different meaning, which corresponds more closely to the meaning of "simulation": its identity is linked to that of the "model" used to construct such simulation. The authors of the analysed papers never express an intention to standardize a model, so its meaning remains contingent, and cannot be turned into a technical object. [ABSTRACT FROM AUTHOR]

This paper investigates the role of models for policy by drawing on and exploring the tensions between the notions of boundary objects and performativity. The notion of boundary object has proven to be useful in gaining a better understanding both of the hybrid character of science-based models and their role in the coordination between different social worlds. However, by assuming that these worlds remain stable, an investigation into the performative nature of models tends to be overlooked. Therefore, this paper investigates how models are constituted by negotiations between scientists and policymakers and at the same time constitute social worlds. It examines two simulation models developed in different Dutch policy contexts: a macroeconomic model for the health care system and an ecological landscape planning and assessment model. The paper shows that models not only actively coordinate social worlds but also contribute to changing them. The performativity of models ranges from generic (instrumental) to substantial as they influence social worlds to the extent that these worlds start behaving the way models describe. [ABSTRACT FROM AUTHOR]

This paper discusses computer simulation modelling in the context of environmental risk management. Approaching computer simulation as practice, performed in networks of heterogeneous elements, we examine the modelling undertaken by engineering consultants commissioned to provide knowledge about local flood risk to the Environment Agency of England and Wales (EA), the public body responsible for flood risk management. We propose that this simulation modelling is best understood as a form of engineering, work geared to solving the problems of clients. It is also a 'virtual' activity, articulating risks and possibilities in the digital space of the computer. We find that this 'virtual engineering' is shaped by the demands and protocols of the EA, first, by the establishment of long-term contractual agreements for delivering knowledge and second, by an EA requirement to use particular software packages. Fashioned between long-term contracts and black-boxed software virtual engineering becomes stabilised as 'the' way in which knowledge about flood risk in actual localities is generated and, consequently, becomes 'hard-wired' into flood risk management in England. [ABSTRACT FROM AUTHOR]

This article studies the relation between two specialised practices in meteorology, modelling and field measurements. This relation is embodied in a number of joint practices of which model evaluation is one of them. The relationship between theory, model and observation has been of concern for many philosophers of science whereas the relationship between the working practices that underlie theories, models and observations, has received less attention. This paper describes and compares the practices that generate observation data and model output and the way different roles that fieldworkers and modellers have in this process are established. Next, different practice-oriented perspectives on the status of models, data and experiments are analysed and the discussion then moves on to the different trouble-shooting strategies scientists deploy, depending on their position within the discipline. It is shown that these strategies are often based on the ideas and judgements that modellers and experimentalists express about each other, and by implication, about their own role in the meteorological enterprise. [ABSTRACT FROM AUTHOR]