Johannes Persson

Why resilience is unappealing to social science : Theoretical and empirical investigations of the scientific use of resilience

Pluralism drawing on core social scientific concepts would facilitate integrated sustainability research. Resilience is often promoted as a boundary concept to integrate the social and natural dimensions of sustainability. However, it is a troubled dialogue from which social scientists may feel detached. To explain this, we first scrutinize the meanings, attributes, and uses of resilience in ecology and elsewhere to construct a typology of definitions. Second, we analyze core concepts and principles in resilience theory that cause disciplinary tensions between the social and natural sciences (system ontology, system boundary, equilibria and thresholds, feedback mechanisms, self-organization, and function). Third, we provide empirical evidence of the asymmetry in the use of resilience theory in ecology and environmental sciences compared to five relevant social science disciplines. Fourth, we contrast the unification ambition in resilience theory with methodological pluralism. Throughout, we develop the argument that incommensurability and unification constrain the interdisciplinary dialogue, whereas pluralism drawing on core social scientific concepts would better facilitate integrated sustainability research.

Climate Change: Believing and Seeing Implies Adapting

Knowledge of factors that trigger human response to climate change is crucial for effective climate change policy communication. Climate change has been claimed to have low salience as a risk issue because it cannot be directly experienced. Still, personal factors such as strength of belief in local effects of climate change have been shown to correlate strongly with responses to climate change and there is a growing literature on the hypothesis that personal experience of climate change (and/or its effects) explains responses to climate change. Here we provide, using survey data from 845 private forest owners operating in a wide range of bio-climatic as well as economic-social-political structures in a latitudinal gradient across Europe, the first evidence that the personal strength of belief and perception of local effects of climate change, highly significantly explain human responses to climate change. A logistic regression model was fitted to the two variables, estimating expected probabilities ranging from 0.07 (SD ±0.01) to 0.81 (SD ±0.03) for self-reported adaptive measures taken. Adding socio-demographic variables improved the fit, estimating expected probabilities ranging from 0.022 (SD ±0.008) to 0.91 (SD ±0.02). We conclude that to explain and predict adaptation to climate change, the combination of personal experience and belief must be considered.

Epistemic risk: The significance of knowing what one does not know

It is a well-established psychological result that the unknown and unwanted in particular scare us.1 A thinner ozone-layer is felt to be a considerable risk. On a more mundane level, situating a gas-tank in a suburban neighbourhood or eating fish with an unknown level of dioxin provokes a similar feeling of risk-taking.

Decision science: from Ramsey to dual process theories

The hypothesis that human reasoning and decision-making can be roughly modeled by Expected Utility Theory has been at the core of decision science. Accumulating evidence has led researchers to modify the hypothesis. One of the latest additions to the field is Dual Process theory, which attempts to explain variance between participants and tasks when it comes to deviations from Expected Utility Theory. It is argued that Dual Process theories at this point cannot replace previous theories, since they, among other things, lack a firm conceptual framework, and have no means of producing independent evidence for their case.

Why separate risk assessors and risk managers? Further external values affecting the risk assessor qua risk assessor

The functional separation of risk assessment and risk management has long been at the heart of risk analysis structures. Equally long it has been criticized for creating technocratic risk management due to valuations being done in the risk assessment to which the stakeholders do not have access. The criticism has mostly been of an ethical nature. Arguably, in separating risk assessment and risk management, one hopes to fulfil two requirements: (1) Social requirement: we (citizens) want risk management to meet the goals and needs of society. (Therefore, there is an obvious reason to have publicly elected risk managers.) (2) Scientific requirement: we (citizens) do not want political views to influence the assessment of facts. (Therefore, there is an equally obvious reason to have risk assessors who are not publicly elected.) We ask in this paper whether it is, in principle, possible to separate risk assessment from risk management. The crucial distinction between risk assessment and risk management we take to be between what kinds of values are involved in them and that separation is meant to shield risk assessment from risk management values and vice versa. Risk assessment is judged to be a scientific activity that should only involve scientific values. We go through a paradigmatic example of good science to see what those scientific values are and whether they are the only ones influencing science. We also present an example of a risk assessment in order to compare it to science. We conclude that the values involved in both science and risk assessment are of the same kind and that they both involve extra‐scientific values. The paper ends with a short discussion of whether the above requirements can be met even though risk assessment and risk management are interdependent.

Semmelweis’s methodology from the modern stand-point: intervention studies and causal ontology

Semmelweiss work predates the discovery of the power of randomization in medicine by almost a century. Although Semmelweis would not have consciously used a randomized controlled trial (RCT), some features of his material-the allocation of patients to the first and second clinics-did involve what was in fact a randomization, though this was not realised at the time. This article begins by explaining why Semmelweiss methodology, nevertheless, did not amount to the use of a RCT. It then shows why it is descriptively and normatively interesting to compare what he did with the modern approach using RCTs. The argumentation centres on causal inferences and the contrast between Semmelweiss causal concept and that deployed by many advocates of RCTs. It is argued that Semmelweiss approach has implications for matters of explanation and medical practice.

Three Conceptions of Explaining How Possibly—and One Reductive Account

Philosophers of science have often favoured reductive approaches to how-possibly explanation. This chapter identifies three varieties of how-possibly explanation and, in so doing, helps to show that this form of explanation is a rich and interesting phenomenon in its own right.

Mechanistic Explanation in Social Contexts: Elster and the Problem of Local Scientific Growth

Jon Elster worries about the explanatory power of the social sciences. His main concern is that they have so few well-established laws. Elster develops an interesting substitute: a special kind of mechanism designed to fill the explanatory gap between laws and mere description. However, his mechanisms suffer from a characteristic problem that I will explore in this article. As our causal knowledge of a specific problem grows we might come to know too much to make use of an Elsterian mechanism but still lack a law. We might then find ourselves in the paradoxical position of knowing more relevant causal truths about the phenomenon we are interested in than we did before, but being able to explain less. If this possibility is realized in social science settings, as I argue it might well be, Elster’s mechanistic account is threatened. Moreover, even if the possibility is rarely realized in that way, it raises, simply as a possibility, a conceptual problem with Elster’s mechanistic framework.

A philosophical account of interventions and causal representation in nursing research: A discussion paper.

BACKGROUND Representing is about theories and theory formation. Philosophy of science has a long-standing interest in representing. At least since Ian Hackings modern classic Representing and Intervening (1983) analytical philosophers have struggled to combine that interest with a study of the roles of intervention studies. With few exceptions this focus of philosophy of science has been on physics and other natural sciences. In particular, there have been few attempts to analyse the use of the notion of intervention in other disciplines where intervention studies are important, such as in nursing research. One unintended consequence of this is that the relations between representing and intervening tend to be less understood outside the natural sciences. OBJECTIVES AND DESIGN This article highlights a number of possible topics on which nursing science and analytic philosophy of science can fruitfully interact. The basic idea is simple. Building on a characterisation of interventions in terms of (i) what is intervened on and (ii) with respect to what, we suggest that interventions in nursing research typically are a blend of varieties belonging to the three dimensions of agency, epistemology, and ontology. The details of the blend determine the relation of the particular intervention study to traditional representational categories such as inductivism and hypothetico-deductive method, and have a bearing on its explanatory power and other more theory independent features of research as well. The framework we suggest should be relevant for nurse researchers who want to adopt a more general and analytically entrenched perspective on representing and intervening than the methodological boundaries in nursing research typically allow.

Unruhe und Ungewissheit: Stem Cells and Risks

This paper focuses on the risk of unknown and uncertain long-term effects of stem cell research and its applications. Research on human embryonic stem cells and induced pluripotent stem cells are used as examples. We discuss some problems that such uncertain knowledge creates for decision makers, and describe how difficult decision making in this context really is. A method for handling this type of hard choice situations is presented and discussed.