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Uncertainty, Risk, and Decision-Making:

Concepts, Guidelines, and Educational Implications

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Abstract

The ability to handle decision problems in conditions of uncertainty and risk is an important skill for contemporary societies and ought to be an aspect of the scientific literacy sought in science education. In this article we present an overview and synthesis of the basic concepts and accounts of risk-related research on the topic of risk and decision-making under uncertainty, with a view to supplementing the science education literature and to contributing to the development of a sound, comprehensive framework for handling these topics in science education. We first describe and compare the reasoning, possibilities, and limitations of the basic risk management strategies — risk-based, precaution-based, and discourse-based — and assess their usefulness to decision-making in differing degrees of uncertainty. We then discuss kinds of discourse (epistemological, reflective, and participatory) needed to deal with disagreements that result from uncertainties and ambiguities of risk-related scientific knowledge and from different socio-political views. Based on these analyses we consider that the analytic-deliberative framework proposed in risk research literature offers a sound and comprehensive basis for risk evaluation and management and for teaching these topics. We also address two topics that we consider of special importance for reaching appropriate decisions under uncertainty. The first concerns criteria for assessing the credibility and severity of alleged risks, so as to avoid over- or underestimating them; the second concerns views, attitudes, responses, and proposals relating to radical modern technological innovations and the uncertainty that characterizes the assessment and management of their risks. Finally, we discuss some implications of the present analysis for promoting epistemologies and the reasoning and decision-making abilities needed for dealing with contemporary social concerns and global risks.

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Notes

  1. Risk analysis and decision theory were established as disciplines in the mid twentieth century (after WWII) and are clearly interrelated. Risk analysis is mainly about concepts, principles, and methods for the assessment, communication, and management of risk, while decision theory focuses primarily on making correct, i.e., rational, decisions in view of uncertainty and risk. Risk assessment seeks to identify sources of risk, danger, and threats and to estimate their severity. Risk communication concerns the ‘exchanging or sharing of risk-related data, information, and knowledge between and among different target groups (such as regulators, stakeholders, consumers, media, and general public)’ (Aven 2018, p. 882). Risk management is about the decisions, actions, and measures that are advisable in order to avoid risks or minimize their effects (see Aven 2018, 2020; Aven & Flage 2020).

  2. There is also a stream in science education research which argues that science learning should be focused on science’s social role and responsibility of science and therefore on supporting students’ abilities for constructive participation and action in social problems and needs. This research has strong connections to the topics of this paper and uses similar arguments in its thinking, and we shall discuss these in due course. Here, we examined some studies that deal primarily with risk analysis and its treatment in education.

  3. Although risk refers generally to both gains and losses, it is more frequently associated with possible danger or harm, especially in the context of major contemporary challenges like climate change (Klinke&Renn 2002). In Beck 1992, also, risk is meant as a negative, as a danger, in relation to new technological developments, and is defined basically as acting, as assessments and decision-makings for dealing with insecurities and potential dangers.

  4. Concerning the debate on scientific realism and social constructivism regarding the nature of scientific knowledge more generally, see e.g. Develaki 2019, 2020, 2022.

  5. Unlike conventional risks, systemic risks are characterized ‘by high complexity, multiple uncertainties, major ambiguities, and transgressive effects on other systems outside of the system of origin’ (Renn et al., 2022, p. 1902). This means that they cannot easily be assessed and managed using only numerical estimations and conventional management strategies, but require novel methods, tools, and processes, such as simulation modeling, interdisciplinary co-operation, and social participation. Systemic risks arise from complex phenomena such as the global financial system, climate change, biological diversity, and the breakdown of technical and organizational infrastructures (see e.g. Renn et al., 2022).

  6. It should be noted that while the category of decision-making under risk serves theoretical research goals it does not correspond to real-world decision problems, because even when the probabilities are treated as known, based on scientific data and expert estimates, they are still not absolutely certain, due to uncertainties and limitations of the scientific knowledge underlying their calculation. It is rare than the probabilities are known with certainty (e.g., in the case of devices such as dice or coins). This means that almost all decisions are taken under uncertainty (see e.g. Hansson 2018; Resnik 2003).

  7. Resnik (2003, p. 332) gives an example of a calculation of the expected utilities for the options concerning the approval or banning of a drug. Based on the facts, e.g., that the drug has a 50% probability of saving 1000 lives (curing 1000 people) and a 10% probability of taking 50 lives through side effects, the expected utility for the first option (approval) is: (0.5)(1000) + (0.5)(0) + (0.1)(− 50) + (0.9)(0) = 450, and for the second: (0)(1000) + (1)(50) = 50. By the expected utility model, the drug should be approved in this case, but the result would change if the data, e.g., the probabilities, were different.

  8. In general CBA adds the probable overall good consequences of a decision option, subtracts its probable overall bad consequences, and recommends the option with the highest net result, regardless of how the risks and benefits are distributed. Choosing a course of action by this criterion therefore permits various distributional combinations, e.g., a benefit (great or small) for few or many at the expense of few or many, and so on (see e.g. Lewens 2007; Kinouchi 2018). Defenders of risk–benefit analysis have countered that these values are just technical constructs representing what society is willing to pay in order to save a human life (Hansson 2018, p. 13), in other words that ‘cost–benefit analysis permits estimating how much should be spent in actions of risk prevention and risk management, with the intention of allocating finite resources to minimize harms of different sorts and magnitudes’ (Kinouchi 2018, p. 238).

  9. The arguments for a Version III of scientific literacy draw on the ‘Bildung’ concept (similar to education but of broader import), which originated in the middle of the eighteenth century in the German philosophy of education (see Elmose & Roth 2005. Elmose & Roth (2005) argue that Allgemeinbildung ‘encompasses exactly the kind of competencies that are required by risk society’ (p. 1), that is, competence for critical-deliberative discourse and participating in collective decision-making and action.

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  1. Educational Advice for Secondary Science Education, Hellenic Ministry of Education, Athens, Greece

    Maria Develaki

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Develaki, M. Uncertainty, Risk, and Decision-Making:. Sci & Educ (2024). https://doi.org/10.1007/s11191-024-00544-w

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