Zero-risk bias

Zero-risk bias is a tendency to prefer the complete elimination of a risk even when alternative options produce a greater reduction in risk (overall). This effect on decision making has been observed in surveys presenting hypothetical scenarios and certain real-world policies (e.g. war against terrorism as opposed to reducing the risk of traffic accidents or gun violence) have been interpreted as being influenced by it.

Explanation

Scientists identified a zero-risk bias in responses to a questionnaire about a hypothetical cleanup scenario involving two hazardous sites X and Y, with X causing 8 cases of cancer annually and Y causing 4 cases annually. The respondents ranked three cleanup approaches: two options each reduced the total number of cancer cases by 6, while the third reduced the number by 5 and completely eliminated the cases at site Y. While the latter option featured the worst reduction overall, 42% of the respondents ranked it better than at least one of the other options. This conclusion resembled one from an earlier economics study that found people were willing to pay high costs to completely eliminate a risk.^[1]^[2]

Multiple real-world policies have been said to be affected by this bias. In American federal policy, the Delaney clause outlawing cancer-causing additives from foods (regardless of actual risk) and the desire for perfect cleanup of Superfund sites have been alleged to be overly focused on complete elimination. Furthermore, the effort needed to implement zero-risk laws grew as technological advances enabled the detection of smaller quantities of hazardous substances. Limited resources were increasingly being devoted to low-risk issues.^[3]

Causes

Other biases might underlie the zero-risk bias. One is a tendency to think in terms of proportions rather than differences. A greater reduction in proportion of deaths is valued higher than a greater reduction in actual deaths. The zero-risk bias could then be seen as the extreme end of a broad bias about quantities as applied to risk. Framing effects can enhance the bias, for example, by emphasizing a large proportion in a small set or can attempt to mitigate the bias by emphasizing total quantities.^[4]

References

↑ Baron, Jonathan; Gowda, Rajeev; Kunreuther, Howard (1993). "Attitudes toward managing hazardous waste: What should be cleaned up and who should pay for it?". Risk Analysis. 13: 183–192. doi:10.1111/j.1539-6924.1993.tb01068.x.
↑ Viscusi, W. K.; Magat, W. A.; Huber, J. (1987). "An investigation of the rationality of consumer valuation of multiple health risks". Rand Journal of Economics. 18: 465–479. doi:10.2307/2555636.
↑ Kunreuther, Howard (1991). "Managing hazardous waste: past, present and future" (PDF). Risk Analysis. 11: 19–26. doi:10.1111/j.1539-6924.1991.tb00561.x.
↑ Baron, Jonathan (2003). "Value analysis of political behavior - self-interested : moralistic :: altruistic : moral". University of Pennsylvania Law Review. 151: 1135–1167. doi:10.2307/3312887.

Biases

Lists of biases	General Memory

Cognitive biases	Acquiescence Anchoring Attentional Attribution Authority Automation Belief Blind spot Choice-supportive Confirmation Congruence Cultural Distinction Egocentric Emotional Extrinsic incentives Fading affect Correspondence Halo effect Hindsight Hostile attribution Impact In-group Negativity Normalcy Optimism Outcome Precision Pro-innovation Response Restraint Self-serving Social comparison Status quo Time-saving Trait ascription Zero-risk In animals

Statistical biases	Estimator Forecast Healthy user Information Lead time Length time Non-response Omitted-variable Participation Recall Sampling Selection Self-selection Social desirability Spectrum Survivorship Systematic error Systemic Verification Wet

Other biases	Academic Exponent Funding FUTON Inductive Infrastructure Inherent in education Media Net Publication Reporting White hat

Bias reduction	Cognitive bias mitigation Debiasing Heuristics in judgment and decision-making

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