20 May 2010 12:00pm - 3:00pm CRASSH, 17 Mill Lane, Cambridge


A mini-workshop organised by the CRASSH Postdoctoral Research Seminar and the Mellon Sawyer Seminar Series – Modelling Futures: Understanding Risk and Uncertainty.


Using models as evidence for policies is of increasing significance. When vaccination strategies are reformed or reliable future scenarios about the effects of climate change are needed, modelling techniques allow exploring possible futures. What kind of tools are computational models and the algorithms they embody? How do we communicate the restrictions of model-based evidence, the uncertainties built into the models to further along in the decision-making chain? This mini-workshop focuses on these questions and discusses two topical issues: the use of algorithms in climate research and the predictive capacity of epidemiological modelling.


Dr Gabriele Gramelsberger (FU Berlin)
'Algorithms as Epistemic Actors in Policy Contexts (Climate Research)'

Dr Erika Mansnerus (British Academy Postdoc Fellow, CRASSH)
'Governance of Public Health Risks by Modelling'


A light buffet lunch will be provided. Please contact Dr Anne Alexander to reserve a place. Reading materials will be available before the seminar.

Abstracts and speaker biographies

Dr Gabriele Gramelsberger works as a philosopher of science at the Institute of Philosophy at the Freie Universität Berlin. Her research is focussed on the restructuring of science as computational sciences, particularly in the field of climate research and biology; epistemological shifts in scientific knowledge production and evaluation; and the study of the history of computing and numerical simulation. Since 2009 she is Principal Investigator of the collaborative research project “Embodied Information“. Further information: http://userpage.fu-berlin.de/~gab/

Algorithms as epistemic actors in policy contexts: Case climate research

‘'Climate' is not based on the direct perception of causal interdependencies between mankind and a natural phenomenon, but on mankind and a statistical outcome called climate that is usually defined as the averaged weather, or more narrowly as the globally averaged surface air temperature, of a period of at least thirty years. In other words: Climate can’t be experienced and directly measured in the way we perceive weather phenomena like rain, heat, and wind. What we ‘experience’ is the flickering of a curve with an averaged tendency towards higher globally averaged temperatures. Thus, the relatively new branch of climate change and policy not only deals with an abstract, statistical phenomenon, which has to be retranslated into local and actual events. It refers to the difficult business of reasoning about the change of change–the anthropogenic change of natural climate change. In order to minimize anthropogenic climate change policy draws on the idea of predicting possible future changes of climate change to prevent them and to ‘stabilize’ the climate. But preventing change from change and predicting future scenarios are challenging tasks.

For both tasks climate models are indispensable scientific instruments. But these models are based on a purely mathematical and physical understanding of 'climate which is entirely different to the socio-political requirements. Therefore the interweaving of both worlds, the scientific world on the one side and the socio-political and economical on the other side, requires a good deal of translation work. Against this backdrop the questions will be posed and discussed, how communication between humans and algorithms is shaped and how algorithms are employed as epistemic actors in policy contexts.

Dr Erika Mansnerus is a British Academy Postdoctoral Fellow. Her current project 'Life-cycles of models: a sociological study of modelling techniques in public health policy' focuses on how modelling techniques function at the interface of public health research and policy making. Her interest is to understand how modelling techniques help public health policy makers and researchers to overcome ethical and financial restrictions when evaluating effective measures to protect and promote public health. Further information:http://www.erikamansnerus.org/

Governance of public health risks by modelling

Epidemiological modelling functions as a form of ‘risk calculation’. These calculations help direct and design preventive actions towards the health outcomes of populations. The interest is to analyse how modelling becomes a part of the ‘knowledge regimes’, within which government acts on the perceived public health risks. Estimates from modelling exemplify the population level reasoning, whereas these estimates may remain incapable of communicating the individual level risks of a particular infection. The main research questions informing this study are: How do various actors – public health officials, epidemiologists, and lay individuals – perceive risks? How does governance of risk emerge through various preventive acts, such as, national or international health protocols, and recommendations to vaccinate? What is the role of technologies of calculation in the governance of risks from infectious diseases? This study analyses the emergence and functions of predictive technologies (i.e. modelling) in the governance of public health risks. How is risk understood in this context and what kinds of modelled ‘encounters with public health risks’ take place?

The study compares two cases in which modelling and simulation techniques were used as predictive tools in the UK: the prediction of measles outbreaks and the policy recommendation of a MMR (measles, mumps and rubella) booster vaccination campaign in 1993-1994 and the pandemic preparedness planning prior to 2009 (H1N1)v pandemic.



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