epiphenomenal…The evidence points, instead, to a combination of brokering and ‘educative’ functions, as envisaged in cognitive
3. A third rule of thumb deviates from the statistical analysis of expressing uncertainty in risk studies The distinction that experts perform when
conducting a probabilistic risk assessment (PRA) between a probability distribution and the associated degrees of remaining uncertainties (expressed in confidence intervals or in other forms of uncertainty characterisation) is not echoed in most risk perception studies (Frewer et al., 2002; Sparks & Shepherd, 1994). There is a tendency to judge a situation as either safe or unsafe, healthy or unhealthy, secure or insecure (B. Fischhoff, Slovic, & Lichtenstein, 1977). The open space between safe and unsafe is perceived as an indication of bad or incomplete science rather than an indication of (genuine) probability distributions (Renn, 2008, p. 102). The more people associate uncertainties with a scientific statement, the more they believe that there has not been enough research and that more investigations would reduce these uncertainties (De Jonge et al., 2007; Frewer et al., 2002; Sparks & Shepherd, 1994). For example, in the case of climate change, many observers are unwilling to accept the claim of an anthropogenic cause for this phenomenon since the scientists are still not 100% certain about the cause-effect relationships (E. U. Weber, 2016). The stochastic nature of relationships in the natural as well as social world remains alien to them. Notably, construal level theory (Trope & Liberman, 2010) and research on climate change (Spence, Poortinga, & Pidgeon, 2012) suggest that objects or events that are temporarily, socially and geographically distant, and uncertain (i.e. hypothetical), are evaluated as less risky and elicit less concern.
The literature includes ample evidence for the effectiveness of these biases (and others) in decision-making bodies (Festinger, 1957; Kahneman & Tversky, 1979; L. Ross, 1977; H. A. Simon, 1976, 1987) (reviews in Boholm, 1998; Breakwell, 2007, p. 78; Covello, 1983; Jungermann, Pfister, & Fischer, 2005; Kahneman, 2011). These biases are summarised in Table 2.
Bias Description Example
Availability Events that come immediately to people’s minds are rated as more probable than events that are of less personal importance.
Crimes by a refugee or a asylum seeker are regarded as much more prominent and problematic than done by a native citizen.
Anchoring effect Probabilities are estimated according to the plausibility of contextual links between cause and effect, but not according to knowledge about statistical frequencies or distributions (people will ‘anchor’ the information that is of personal significance to them).
Toxic substances such as arsenic or mercury tend to be overrated in their potential for harm as most people associate this substance with crime and murder.
Representation Singular events experienced in person or associated with the properties of an event are regarded as more typical than information based on frequency of occurrence.
People who have experienced a stroke of lightning tend to estimate the frequency of damage by lightning much higher than those who did not have such an experience.
Confirmation Evidence is searched for in ways that are partial to existing beliefs, expectations, or desirable outcomes.
People sometimes select only positive information about a political candidate that one wants to support.
Motivated reasoning Information, evidence or arguments are reframed in ways conducive to an individual’s desires, needs, or goals.
Correlations between frequency of gun ownership and crime rates in different areas of the US are wrongly interpreted as evidence that gun control would not reduce crime rates.
Avoidance of cognitive dissonance
Information that challenges perceived probabilities that are already part of a belief system will be either ignored or downplayed.
People who believe that non- ionising radiation from cellular phones may cause cancer are more likely to look for sources online that confirm their view than people who do not share this belief.
Many of these biases and rules of thumb have also been detected and empirically confirmed in policymaking arenas (Bellé, Cantarelli, & Belardinelli, 2018; Vis, 2011). However, there are also clear indications that deviations from expert advice are less a product of ignorance or irrationality than an indication of one or several intervening context variables that often make perfect sense if seen in the light of the original context in which the individual decision-maker has learned to use them (Brehmer, 1987; Gigerenzer, 1991, 2000; Lee, 1981). Based on this situational understanding of heuristics, a different perspective on heuristics has evolved that emphasises their adaptive function to link judgement and cues from the environment (Gigerenzer, 2008). They help individuals to find quick and satisfying solutions to different and distinct environmental challenges. In this view, the information search and judgement process vary according to the structure of the concrete environment. The environment is part of the decision-maker’s rationale for drawing inferences (Todd & Gigerenzer, 2012, p. 18). Uncertainty in this understanding is not a property of the knowledge system of individual decision-makers but instead emerges as a property of the mind–environment system (Kozyreva, Pleskac, Pachur, & Hertwig, 2019). Uncertainty includes both environmental unpredictability and uncertainties that stem from the mind’s boundaries, such as limits in available knowledge and cognitive capabilities. This insight from cognitive psychology is particularly relevant for scientific advice for policymakers. The more universal knowledge claims that scientific advisers are likely to bring to policymaking arenas may be incongruent with the tacit knowledge of experienced policymakers who have learned to adjust their judgements to the socio- political environment in which they operate (Jungermann, 1986). They still may benefit from knowing the scientific evidence about the topic in question, yet they may have good reasons to deviate from the implications of this evidence if they cannot apply it to their familiar environment (Woodhouse & Nieusma, 1997).
4.3.2 Technical and issue biases
Beyond the cognitive and affective heuristics that govern the processing of scientific information, the literature addresses other forms of biases that are related to interest and values that colour or even shape the evidence presented to decision-makers. It is often assumed that scientific evidence could serve as a neutral input to policymaking, as it provides unbiased and impartial factual knowledge to the decision-makers (Chalmers, 2003; Coalition for Evidence-Based Policy, 2014; Nutley et al., 2007). However, as several authors have pointed out, the implications of evidence are less straightforward when applied to wicked and contested political problems (Parkhurst, 2016, 2017). The main line of argument here is that evidence can be interpreted in different ways, depending on what parts of evidence are selected and highlighted, how ambiguity (interpretative and normative) is being addressed and selectively used, and how various interest groups will produce their own evidence to serve their own needs and positions (Parkhurst, 2016; Russell, Greenhalgh, Byrne, & McDonnell, 2008; Schön & Rein, 1994).
Parkhurst has labelled these biases as technical and issue-related: