. Through examining a case study of a major fluids modelling code, this paper charts two key properties of software as a material for building models. Scientific software development is characterized by piecemeal growth, and as a code expands, it begins to manifest frustrating properties that provide an important axis of motivation in the laboratory. The first such feature is a tendency towards brittleness. The second is an accumulation of supporting technologies that sometimes cause scientists to express a frustration with (...) the bureaucracy of highly regulated working practices. Both these features are important conditions for the pursuit of research through simulation. (shrink)

Simulation software used for modeling has become as ubiquitous as computers themselves. Despite growing reliance on simulation in educational and workplace settings, users encounter frustration in using simulation software programs. The authors conducted a study with 26 engineering students and interviewed them about their experience learning the simulation software Arena for optimization modeling. These students experienced frustration with the process of learning to “think” like the simulation software. Students explained their difficulty with learning the software in a way that implied (...) human dialogue with a machine and negotiation with Arena in an attempt to overcome this frustration. Rather than being gender specific, frustrations were experienced by most male and female students equally, specifically those who held a perception of difference between human and machine “thinking.” The authors contend that communication difficulty with the embedded assumptions of the software is the source of frustration rather than deficiencies of users. (shrink)

ArgumentThis paper challenges three assumptions common in the literature on expertise: that expertise is linearly derived from scientific knowledge; that experts always align with the established institutional order; and that expertise is a property acquired by individuals. We criticize these ideas by juxtaposing three distinct expert practices involved with flood risk management in England. Virtual engineering is associated with commercial consultancy and relies on standardized software packages to assess local flood inundation. Mathematical experimentation refers to academic scientists creating new digital (...) renderings of the physical dynamics of flooding. Participatory modeling denotes research projects that aim to transform the relationships between experts and local communities. Focusing on different modes of modeling we contribute an analysis of how particular models articulate with specific politics of knowledge as experts form relationships with flood risk management actors. Our empirical study also shows how models can contribute to re-distribution of expertise in local flood risk management. (shrink)

Various geoengineering technologies that would deliberately alter the climate system have been proposed as a way to alleviate risks of global warming. Technologies that would shield incoming sunlight to cool the planet, so called solar radiation management, are particularly controversial. Considering insights from social studies of simulation modeling and research on expectations in science and technology, I argue that climate modeling has a central role in producing visions of SRM. I draw upon an empirical analysis of scientific research on SRM (...) to examine how a creative play with technological ideas becomes possible through climate modeling. This enables scientists to project and study environmental impacts of speculative SRM methods in virtual experiments and to develop and refine ideas for adjusting sunlight. Hence, while climate models are used to improve scientific understandings of climate system behavior and to anticipate possible environmental impacts of SRM, they also become inventive tools, allowing scientists to envision novel ways of climate control and optimization. Given the importance of simulation studies to knowledge production on SRM, I critically reflect on the challenges that arise when visions about an engineered climate future are first and foremost produced in climate simulations. (shrink)

Numerical simulations have come to be widely used in scientific work. Like experiments, simulations generate large quantities of numbers that require analysis and constant concern with uncertainty and error. How do simulationists convince themselves, and others, about the credibility of output? The present analysis reconstructs the perspectives related to performing numerical simulations, in general, and the situations in which simulationists deal with uncertain output, in particular. Starting from a distinction between idealized and realistic simulations, the paper presents the principal methods (...) of evaluation in relation to these practices and how different audiences expect different methods. One major challenge in interpreting output data is to distinguish between “real” and “numerical” effects. Within the practice of idealized simulations, simulationists hold the underlying model accountable for results that manifest “real” effects, but because “numerical” and “real” effects cannot be distinguished on the basis of what they derive from, attempted causal explanations are rather justifications for their conclusions. At the same time, simulationists’ explanations are part and parcel of their contradictory perspectives, according to which they believe in simulations largely due to the underlying model, while painfully recognizing everything they have to add to make computations doable on the basis of this model. (shrink)

It has been convincingly argued that computer simulation modeling differs from traditional science. If we understand simulation modeling as a new way of doing science, the manner in which scientists learn about the world through models must also be considered differently. This article examines how researchers learn about environmental processes through computer simulation modeling. Suggesting a conceptual framework anchored in a performative philosophical approach, we examine two modeling projects undertaken by research teams in England, both aiming to inform flood risk (...) management. One of the modeling teams operated in the research wing of a consultancy firm, the other were university scientists taking part in an interdisciplinary project experimenting with public engagement. We found that in the first context the use of standardized software was critical to the process of improvisation, the obstacles emerging in the process concerned data and were resolved through exploiting affordances for generating, organizing, and combining scientific information in new ways. In the second context, an environmental competency group, obstacles were related to the computer program and affordances emerged in the combination of experience-based knowledge with the scientists’ skill enabling a reconfiguration of the mathematical structure of the model, allowing the group to learn about local flooding. (shrink)