Encyclopedia of Personality and Individual Differences

Living Edition
| Editors: Virgil Zeigler-Hill, Todd K. Shackelford

Uncertainty Avoidance

  • Bradley R. DeWeesEmail author
  • Jennifer S. Lerner
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-28099-8_806-1

Individual differences in uncertainty avoidance can predispose serenity or terror, tolerance or intolerance, and innovation or stagnation. We here define uncertainty avoidance and review key research findings on the topic.

Uncertainty Avoidance Defined

Norton (1975) defined uncertainty avoidance as “a tendency to perceive or interpret information marked by vague, incomplete, fragmented, multiple, probably, unstructured, uncertain, inconsistent, contrary, contradictory, or unclear meanings as actual or potential sources of psychological discomfort or threat” (p. 608). Later researchers expanded on this by identifying two key dimensions of uncertainty avoidance.

First, individuals can differ in what they initially classify as uncertain (Hirsch et al. 2016). Second, once an individual classifies a stimulus as uncertain, she/he can differ in the extent to which she/he believes the uncertainty is worth avoiding (Matsumoto et al. 2008). Stopa and Clark (2000) found that individuals high in uncertainty avoidance were more likely to make extremely negative interpretations of only mildly negative scenarios and thus escalating avoidance behaviors. Most work, especially work in clinical psychology, focuses on the latter dimension of the construct, examining for whom and why uncertainty is inherently threatening or desirable (Budner 1962; Dugas and Robichaud 2007; Carleton et al. 2012).

Affect and Uncertainty Avoidance

One of the key factors determining whether a stimulus is deemed threatening is an individual’s affective state. Specific emotional states mediate uncertainty avoidance by triggering cognitive dispositions to perceive more or less risk (Lerner et al. 2015). According to the Appraisal Tendency Framework, discrete emotions differ along a specific number of cognitive appraisal dimensions (Lerner and Keltner 2000, 2001). These appraisal dimensions form perceptual lenses that shape how individuals perceive themselves and their environment. Anxiety, with its key appraisal dimensions of low certainty and low individual control (Lerner et al. 2015), is most relevant to individual differences in uncertainty avoidance.

Lerner and Keltner (2000, 2001) found that individuals high in both trait and state anxiety made more pessimistic judgments about the future. Ma-Kellams et al. (2016) found a similar conceptual pattern when individuals were asked to remember events about the past – individuals high in state anxiety were more likely to recall their heart rate in a stressful task as having been faster than it actually was, even after controlling for the participant’s heart rate during the stressful task itself. In short, the appraisal dimensions of anxiety influence what individuals see in their overly pessimistic simulations. Specifically, the emotions of fear and anxiety contribute to the pessimism of a simulation by increasing perceived lack of certainty and control. Emotions high in appraisals of control and certainty, by contrast, have the opposite effect, reducing perceived risks (Lerner and Keltner 2000, 2001; Lerner et al. 2003, 2007; Gerend and Maner 2011; Rydell et al. 2008; Carver and Harmon-Jones 2009; Moons et al. 2010; Lerner and Tiedens 2006; Han et al. 2007; Fischhoff et al. 2005).

Uncertainty Avoidance Across Levels of Analysis and the Life Span

Researchers spanning the social sciences invoke uncertainty avoidance as a key explanatory factor (e.g., economics: Bremer et al. 2017; public administration, Baekkeskov 2016; and corporate finance, Li et al. 2013). Research that directly examines uncertainty avoidance itself, however, is concentrated primarily in three fields. The most influential is clinical psychology, which studies the role of uncertainty avoidance in anxiety disorders, depression, and addictive behavior (Carleton et al. 2012; Oglesby and Schmidt 2017; Radell et al. 2016). Social psychologists primarily study uncertainty avoidance as a means of understanding the mechanisms of toleration of different races, genders, sexual orientations, or political ideologies (Jost et al. 2009). Finally, scholars in organizational behavior study uncertainty avoidance as a means of understanding openness to new ideas, creativity, and innovation (Hofstede 1980). The construct of uncertainty avoidance, in sum, manifests not only in personality research but also at multiple levels of analysis in social science.

Uncertainty Avoidance at the Individual Level

The corresponsive principle of personality development holds that individuals select into the social environments to which they are best suited and that those social environments then reinforce individual personality traits (Caspi et al. 2005). The corresponsive principle is complemented by the cumulative-continuity principle, which holds that an individual’s personality traits tend to become more consistent throughout the life span (Roberts and DelVecchio 2000). Together, the two principles predict a lifelong interplay between individual-level differences in uncertainty avoidance and social-level environments that differ in levels of uncertainty. We begin by examining the individual level.

At the individual level of analysis, uncertainty avoidance is associated with what clinical psychologists call a “negative interpretation bias” (Hirsch et al. 2016). As mentioned earlier, this pattern describes the tendency to perceive ambiguous stimuli as threatening. Such negative interpretations are presumed to contribute to social anxiety disorders (SAD) and generalized anxiety disorders (GAD; Mathews and MacLeod 2005). Individuals with these anxiety disorders are more likely to interpret feedback (Voncken and Bögels 2008), conversations (Stopa and Clark 1993), and their own self-image (Hirsch et al. 2006) in a negative light.

Individuals with anxiety disorders tend to imagine only the worst of what will happen after encountering uncertain stimuli. Treatment of uncertainty avoidance thus focuses on changing such mental simulations (Dugas and Koerner 2005; Anderson et al. 2012). The treatment can take the form of “in vivo exposure (e.g., asking a patient with harm obsessions to hold a knife),” which allows individuals to observe for themselves that the uncertainty they simulate is more manageable than they initially believed. It should be noted, however, that some in the literature disagree on the efficacy of such treatments and even on the underlying connection between intolerance of ambiguity and anxiety disorders (Su et al. 2016).

Uncertainty Avoidance in Societies and Organizations

Individual perceptions typically take place in social and organizational environments, which can have their own effects on whether ambiguity is inherently a threat. Within social psychology, uncertainty avoidance is not treated as a personality disorder but as a normal reaction to certain social pressures (Pratto et al. 1994). In turn, uncertainty avoidance is something that can be altered by the social environment. Research on accountability – the expectation that one will be called on to justify one’s views – has shown that socially anxious individuals show greater sensitivity to uncertainty when they feel accountable for their decisions (Tetlock et al. 1989).

Social psychologists have also discovered an association between individual preferences toward uncertainty and preferences toward outsiders and change more generally. Uncertainty avoidance is positively associated with ethnocentrism and social dominance orientation (SDO); it is negatively associated with attitude flexibility (Pratto et al. 1994). Jost et al. (2003) argue that individual differences in uncertainty avoidance account for some of the variation in who claims politically conservative ideologies, reporting a positive correlation between uncertainty avoidance and conservatism. However, Jost et al. (2003) also caution that analyzing individual differences in uncertainty avoidance alone is insufficient for identifying individual political ideologies. Situational factors such as system instability (i.e., change in social order or governance) and motivational factors such as self-interest also have important effects (Jost et al. 2009).

Within the organizational behavior literature, uncertainty avoidance results from the organizational or national culture of which the individual is a part. Hofstede (1980) argued that cultural differences toward uncertainty partly explained national-level variance in idea generation and subsequent economic innovation – that is, he offered uncertainty avoidance as a cultural-level personality difference. According to Hofstede (1994), “One way of describing countries where uncertainty avoidance is strong, is to say that in these countries a feeling prevails of ‘what is different, is dangerous.’ In weak uncertainty avoidance societies, the feeling would rather be ‘what is different, is curious’” (p. 6). An analysis by Rieger et al. (2014) showed that differences in attitude toward risk could not be explained by economic conditions alone and that Hofstede’s dimension of uncertainty avoidance was a critical factor.

With evidence that uncertainty avoidance is associated with such profoundly important outcomes as depression, social intolerance, and economic growth, motivation for further study and application is self-evident. The efficacy of such future work will depend in large part on the ability to measure the construct, the topic to which the next section turns.

Methods of Measurement

The test would not become a standard measure, but it launched the search for better options. Social psychologists and organizational theorists typically rely on self-report measures that can be applied across different demographic groups and cultures. Examples include the intolerance of ambiguity scale (Budner 1962), the measure of ambiguity tolerance (MAT-50; Norton 1975), and Hofstede (1980)’s cultural dimensions.

Clinical psychologists tend to employ more individualized measures (Hirsch et al. 2016). Clinicians employ two broad categories of measures: offline and online tasks. Examples of the former are open-ended responses to ambiguous statements (e.g., “You wake with a start in the middle of the night thinking you heard a noise, but all is quiet… What do you think woke you up?”) and sentence completion tasks. Examples of the latter (online measurements) are lexical decision tasks, in which individuals under time pressure associate words with possible meanings (e.g., “Punch” can be associated with either “attack” or “drink”; Hirsch et al. 2016, Supplementary Materials), or self-report measurements such as the influential Intolerance of Uncertainty Scale – Short Version (IUS-12; Carleton et al. 2012; Oglesby et al. 2017; Dekkers et al. 2017).

Each type of measure has strengths and weaknesses in terms of the goals clinicians try to accomplish. Clinicians deal with a particularly intense form of demand effect – clinicians and patients meet in close quarters, and patients especially may want to impress their clinicians. Offline measurements are more flexible and can allow for highly individualized responses; however, they are also subject to demand effects if the individual being examined believes one response is more acceptable than another. Online measurements preclude the possibility of demand effects, but they are more difficult to produce and do not allow for reflection. An additional advantage of online measurements such as the IUS-12 is that they can be subjected to rigorous psychometric analysis (e.g., Oglesby et al. 2017; Ruscio et al. 2001; Dekkers et al. 2017) (Oglesby et al. (2017) note that the IUS-12 is composed of three distinct latent factors that separate individuals into high, medium, and low levels of IU (earlier work had implied that the construct was made up of a single, continuous factor; Ruscio et al. 2001). McEvoy and Mahoney (2011) argue that the construct is made of a “prospective” and an “inhibitory” factor – the prospective factor refers to a desire for predictability, and the inhibitory factor refers to the behavioral reticence that results from uncertainty. Dekkers et al. (2017) found support for this factor structure across age groups and genders).

Apart from direct measurement, uncertainty avoidance can be measured by its correlation with observable behavior. It is, for example, associated with how a person orients him- or herself to the social environment. Triandis et al. (1965) found that individuals who scored low in uncertainty avoidance preferred more social distance. The results are held in three distinct cultures: Germany, Japan, and the United States. Orientations toward uncertainty can also affect the extent to which people search out information on their own abilities (Roney and Sorrentino 1995). Individuals high in uncertainty orientation are more likely to look for information that confirms their relative level of ability in a task than individuals with a certainty orientation.

Conclusion

The study of personality and individual differences in uncertainty avoidance is important for understanding human behavior. Differences in anxiety disorders, addiction, racism, and innovation can partly trace their roots to individual difference in uncertainty avoidance. This entry has summarized some of the most important effects, how these effects manifest at different levels of analysis, and how researchers measure the construct.

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.Harvard Kennedy SchoolHarvard UniversityCambridgeUSA

Section editors and affiliations

  • Patrizia Velotti
    • 1
    • 2
  1. 1.Department of Educational SciencesUniversity of GenoaGenoaItaly
  2. 2.Sapienza University of RomeRomeItaly