Rafaela Hillerbrand

Some risks have extremely high stakes. For example, a worldwide pandemic or asteroid impact could potentially kill more than a billion people. Comfortingly, scientific calculations often put very low probabilities on the occurrence of such catastrophes. In this paper, we argue that there are important new methodological problems which arise when assessing global catastrophic risks and we focus on a problem regarding probability estimation. When an expert provides a calculation of the probability of an outcome, they are really providing the probability of the outcome occurring, given that their argument is watertight. However, their argument may fail for a number of reasons such as a flaw in the underlying theory, a flaw in the modeling of the problem, or a mistake in the calculations. If the probability estimate given by an argument is dwarfed by the chance that the argument itself is flawed, then the estimate is suspect. We develop this idea formally, explaining how it differs from the related distinctions of model and parameter uncertainty. Using the risk estimates from the Large Hadron Collider as a test case, we show how serious the problem can be when it comes to catastrophic risks and how best to address it.

In the applied sciences and in engineering there is often a significant overlap between work at universities and in industry. For the individual scholar, this may lead to serious conflicts when working on joint university–industry projects. Differences in goals, such as the university’s aim to disseminate knowledge while industry aims to appropriate knowledge, might lead to complicated situations and conflicts of interest. The detailed cases of two electrical engineers and two architects working at two different universities of technology illustrate the kinds of problems individual scholars face in university–business collaborations. These cases are based on qualitative interviews and additional data and demonstrate that, while value conflicts emerge on the organizational level, it is primarily the individual researcher who must deal with such conflicts. This analysis adds to existing studies in two ways: first, it explicitly addresses normative issues framed in terms of ethical and social values, thereby going beyond the common social-science perspective of university–business collaboration. Secondly, it provides qualitative insights, thereby identifying details and issues not apparent in quantitative studies. In particular, it is evident that university–industry collaborations are prone to value conflicts not only in research but also in education and job training.

In recent years, computational sciences such as computational hydrodynamics or computational field theory have supplemented theoretical and experimental investigations in many scientific fields. Often, there is a seemingly fruitful overlap between theory, experiment, and numerics. The computational sciences are highly dynamic and seem a fairly successful endeavor---at least if success is measured in terms of publications or engineering applications. However, for theories, success in application and correctness are two very different things; and just the same may hold for "methodologies" like computer simulations. A lively debate on the epistemic status of computer simulations has thus emerged within the philosophy of science. This paper discusses possible problems when computer simulation and laboratory experiment are intertwined. In present experiments, stochastic methods in the form of Monte Carlo simulations are often involved in generating experimental data. It is questioned as to how far a realistic stance can be maintained when such stochastic elements are involved. Taking experiments in high energy physics as a study case, this paper contends that using these types of entangled material and numerical experiments as a source of new phenomena or for theory testing must presuppose a certain understanding of causality and thus binds us at least to a weak form of realism

This paper aims to show that modeling complex systems inevitably involves non-propositional knowledge and thus the uncertainties associated with the corresponding model predictions cannot be fully quantified. This is exemplified by means of the climate system and climate modeling. The climate system is considered as a paradigm for a complex system, whereby the notion of complexity adopted in this paper is epistemic in nature and does not equate with the technical definition of a complex system as for example used within physics or complexity theory. The epistemic notion of complexity allows to view the climate system as complex with respect to some features, while simple with respect to others. This distinction is of practical significance for political decision making as it allows to treat some climate predictions as (fairly) certain, while acknowledging high uncertainties with others.

Between the fourth and the recent fifth IPCC report, science as well as policy making have made great advances in dealing with uncertainties in global climate models. However, the uncertainties public decision making has to deal with go well beyond what is currently addressed by policy makers and climatologists alike. It is shown in this paper that within an anthropocentric framework, a whole hierarchy of models from various scientific disciplines is needed for political decisions as regards climate change. Via what is sometimes referred to as ‘uncertainty cascade’ in the technical literature, the uncertainties of various models accumulate in the input to political decision making. This paper aims to chart the uncertainties in the multi-disciplinary enterprise of current climate modeling in broad strokes. It is shown that the uncertainty cascade makes it impossible to quantify uncertainties in the form of probability estimates. Moreover the paper highlights how global climate models fail to provide probability estimates. A better treatment of the uncertainties of climate predictions on the political level would require an overhaul of the current IPCC practice that separates the various scientific fields in various working groups

Most scientists agree that; at least for the time being; unquantified uncertainties are inevitably connected to predictions of climate models. Uncertainties; however; do not justify political inaction. This paper addresses the question of how epistemic uncertainties are of relevance for practical decision making. It is shown how common decision approaches based on the precautionary principle fail to adequately deal with uncertainties as they arise in climate modeling. I argue that with regards to climate change; unquantified uncertainties can neither be ignored in decision making nor be reduced to quantified ones by assigning subjective probabilities. This distinguishes the ethical problems associated with climate change from other problems regarding energy supply and demand like; for example; those associated with nuclear power.

The debate over the civilian use of nuclear power is highly polarised. We argue that a reasonable response to this deep disagreement is to maintain that advocates of both camps should modify their positions. According to the analysis we propose, nuclear power is neither entirely right nor entirely wrong, but rather right and wrong to some degree. We are aware that this non-binary analysis of nuclear power is controversial from a theoretical point of view. Utilitarians, Kantians, and other moral theorists make sharp, binary distinctions between right and wrong acts. However, an important argument speaking in favour of our non-binary analysis is that it better reflects our considered intuitions about the ethical trade-offs we face in discussions of nuclear power. The aim of this article is to make this argument sharp by explaining how it can be rendered compatible with, and supported by, the Capability Approach, which is quickly becoming one of the most influential frameworks for thinking about human development