Introduction: Severe Uncertainty in Science, Medicine, and Technology

Abstract

This Special Issue titled “Severe Uncertainty in Science, Medicine and Technology” aims to shed new light on the understanding of severe uncertainty and its multifaceted implications. The main idea of the papers of this collection is that, despite possible sophisticated statistical judgments towards future risks in science, medicine, and technology, severe forms of uncertainty still exist.While ignorance is usually assumed to be a total absence of knowledge, uncertainty often refers to the incompleteness of knowledge or information. In its extreme form, this is called “severe uncertainty” but is also known as “fundamental,” “radical,” “deep,” “great,” or “genuine” uncertainty. A common characteristic of these notions is that it may be difficult to meaningfully conceptualize uncertainties in probabilistic terms (Knight 1921; Ellsberg 1961, Shackle 1961; Keynes 1973; Langlois 1994; Chiffi and Pietarinen 2017; Kay and King 2020). When uncertainties are mainly shaped by normative facets, these are referred to as normative uncertainties (Taebi et al. 2020).With severe uncertainty in this special issue, we refer to situations in which the following issues are unknown, unclear or undefined:(i) the adequate models to describe the relations between system’s variables;(ii) the probability distribution to represent uncertainty about relevant parameters and variables; and/or(iii) the correct theory of rational choice and the correct theory of epistemology to handle uncertainty.(iv) the ethical dimensions that situations of uncertainty give rise to.This means that severe uncertainty encompasses factual, methodological, and normative aspects of decision-making.1 A variety of qualitative and quantitative methods are available to identify and deal with severe uncertainties. Some of these methods are philosophical in nature. As such, philosophy has much to add to our understanding of future risks in science and technology and, more specifically, the role of uncertainties.Classically, those forms of uncertainty that can be probabilistically quantified—as they are in many medical and engineering fields—are labeled as “risks” (Royal Society 1983). Admittedly, the distinction between risk and uncertainty is not always so sharp, and the two terms are often used interchangeably by experts and laypeople. While probabilistic risks are fairly well investigated in theories of risk, discussion on methodological tools and strategies regarding identifying and dealing with severe uncertainty has received less attention. Even though emerging research has contributed to reshaping the field, many scientific and technological decisions about future events occur under conditions of severe uncertainty rather than probabilistic risk. Thus far, a family of mathematical and argumentative methodologies have been proposed to provide rational (though not strictly probabilistic) approaches to decisions under fundamental uncertainty; the most relevant of these are potential surprise theory (Shackle 1961), scenario planning (van der Heijden 1996), possibility theory (Zadeh 1978), the Dempster-Shafer theory of belief functions (Shafer 1976), and hypothetical retrospection (Hansson 2007). A comprehensive introduction to different methodologies that cope with uncertainty is provided by Hansson (2018).Let us expand on several aspects of severe uncertainty by reviewing an extract taken from a speech given by former US Secretary of Defense Donald Rumsfeld during the invasion of Iraq.Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns, the ones we don’t know we don’t know. (U.S. Department of Defense 2002)Rumsfeld’s statement organized knowledge, ignorance, and uncertainty into categories. Known unknowns exemplify those contexts in which uncer