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Zero-risk Bias: No Risk, No Reward

Problem

Alberto is bobbing for apples at the carnival. There are two barrels with five apples each, but only one designated apple in each barrel is considered a winner. Luckily for Alberto, he has a voucher that allows him to remove any five apples from the barrels to increase his odds of winning. From which barrel or barrels should Alberto remove five apples?

Answer

In Alberto’s position, many people would choose to remove all five apples from one barrel, leaving only one barrel of five apples to bob from instead of two. The reasoning here is that by completely removing one barrel, you have eliminated the 4-out-of-5 chance that you choose a losing apple from that barrel. Now you only have to contend with the 1-in-5 chance of grabbing the winning apple from the other barrel. However, this is biased thinking. There is another option that is statistically more conducive to selecting a winning apple from either barrel. The better option would be to remove three apples from one barrel and two from the other. This way, there is a 1-in-2 chance of pulling the winner from the first barrel, and a 1-in-3 chance of pulling the winner from the second barrel. Both of these odds are better than the 1-in-5 chance created by completely removing one whole barrel. The misleading urge to completely eliminate the risk from one subpart of a problem as opposed to decreasing overall risk is known as zero-risk bias.

Zero-risk Bias

When given several options to reduce risk in a situation with multiple facets, zero-risk bias is the tendency to choose the option that completely eliminates one element of risk, rather than an alternative option that reduces the most overall risk.

How It Works

Zero-risk bias is heavily influenced by framing effects, cognitive biases stemming from whether an issue is viewed in a subjectively positive or negative light. In cases of zero-risk bias, completely eliminating one element of risk appears favorable, while the alternative option of leaving some risk in two or more elements appears less favorable. Therefore, many people tend to choose the first option since it is framed more positively in their minds. In reality, though, the alternative choice would be better.

Why Care?

The zero-risk bias has much larger implications outside of bobbing for apples. For example, one study investigated a hypothetical scenario in which two dangerous hazardous waste sites needed cleanup. Site X, left untreated, would lead to eight cases of cancer in nearby residents, and Site Y would lead to four cases. In a questionnaire regarding the cleanup options, there were two options that decreased total cancer cases by six across both Sites X and Y. There was also a third option that decreased total cases by only five across both sites, but completely eliminated the four cases from Site Y. 42% of respondents to the survey ranked the third option higher than at least one of the other two, even though it resulted in the smallest reduction in total cancer cases. The zero-risk bias led 42% of the survey respondents to select an option that was statistically worse than the alternatives.

Zero-risk bias is an important factor to consider in public policy decisions, as seen in the hazardous waste site example. Economists and business owners must also be wary of zero-risk bias when analyzing the cost/benefit ratios of various business decisions. Submitting to zero-risk bias may lead to the misallocation of resources, like funneling money into one risk reduction plan when another would yield greater overall benefits. While some options may seem favorable in the short term, acknowledging zero-risk bias and making careful decisions will be more conducive to success in the long term.

Think Further

  1. Have you ever fallen victim to zero-risk bias? Did you recognize it after the fact?
  2. How might someone overcome zero-risk bias?
  3. Can you think of a situation in which completely eliminating one element of risk is actually preferable to decreasing overall risk from multiple elements?

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  1. Baron, J., Gowda, R., & Kunreuther, H. (1993). Attitudes toward managing hazardous waste: What should be cleaned up and who should pay for it? Risk Analysis, 13(2), 183–192. https://doi.org/10.1111/j.1539-6924.1993.tb01068.x.
  2. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.
  3. Krockow, E. (2020, March 19). Is a zero-risk bias impairing your crisis response? Psychology Today. https://www.psychologytoday.com/blog/stretching-theory/202003/is-zero-risk-bias-impairing-your-crisis-response.
  4. Schneider, E., Streicher, B., Lermer, E., Sachs, R., & Frey, D. (2017). Measuring the zero-risk bias: Methodological artefact or decision-making strategy? Zeitschrift Für Psychologie, 225(1), 31–44. https://doi.org/10.1027/2151-2604/a000284.