Why do we seek certainty in risky situations?

Zero Risk Bias

, explained.

What is the Zero Risk Bias?

Zero risk bias relates to our preference for absolute certainty. We tend to opt for situations where we can completely eliminate risk, seeking solace in the figure of 0%, over alternatives that may actually offer greater risk reduction.

Where it occurs

Have you ever bought an insurance policy for something that you felt was close to impossible? We know that these hypotheticals are highly unlikely, such as needing surgery in a foreign country, but the thought of such an event can be deeply unsettling. Although the policy might not be worth the premium we pay, part of what we’re buying is the peace of mind in knowing we’ve eliminated the potential risk.

People are not calculators, and most do not consciously deliberate the exact probabilities of events. Instead, they often gauge a prospect by how they feel about it. Even a 1% chance of disaster can loom over our conscience, and eschewing such a minute probability and securing that 0% can be a favorable outcome.

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Related Biases

Individual effects

This favorability in eliminating risk can directly impact decision-making in regard to probabilistic events. In one study, when people were asked if they prefer the option to decrease a given risk from 5% to 0% or to decrease a risk from 50% to 25%, they opted for the former, despite the drop from 50% to 25% being a far greater reduction in risk.1

The seduction of certainty can impact a decision where we erroneously opt for a suboptimal choice. Much of the research surrounding zero risk bias involves presenting participants with hypothetical scenarios, often labeled with probabilities in terms of risk. It is worth mentioning, however, that decisions in the real world are often not as clear cut and defined as they are in experimental settings. But while the trade-offs that are often front in center in zero risk bias might not be applicable to everyone, the concept of having a bias towards a certain, risk-free option is still pertinent to many.

Systemic effects

Zero risk bias can have significant effects in the court of public opinion. Some of the hypotheticals in the literature include preferences over policies towards terrorism, gun violence, and traffic accidents. Public pressures to strive for zero risk may push policymakers away from prioritizing overall risk reduction.

On a business level, management decisions may be influenced by the zero risk bias. A lot of success in business, especially in regard to start-ups and small firms, comes from taking risks. With a proclivity to exhibit a bias towards eliminating risk, these companies may be missing out on major opportunities for growth.

Why it happens

Like many cognitive biases, the zero risk bias is a mental shortcut. The common narrative behind these shortcuts is that they reduce cognitive strain. Instead of having to calculate the optimal solution, something that would require a lot of time and energy, we opt for the choice with less effort and uncertainty.

A classic behavioral science concept that can help explain the lure of 0% is loss aversion. Kahneman and Tversky’s Prospect Theory,2 which suggests that losses loom larger than gains, provides a more detailed explanation. By completely eschewing risk, we are eliminating the possibility of a loss, which can be reassuring enough to become more valuable than an increased probability of a gain.

Consistent with this line of thinking is the “risk-as-feelings” hypothesis from the behavioral economist George Lowenstein and colleagues.3 They suggest that in uncertain situations, the possibility is more emotionally salient than the probability of an outcome. These emotional cues are relied upon when making decisions regarding uncertainty, and manifest in mental shortcuts such as the zero risk bias.

Why it is important

Decision-making under risk and uncertainty is a vast arena present in public health, financial markets, political strategy, security, and business management. Decisions within these domains can have serious consequences, so balancing a public desire for certainty with the real optimal option can be a delicate act. Not only is acknowledging the zero risk bias relevant to an individual’s thought process, but also to how we might expect such decisions to be perceived by others.

How to avoid it

While it’s not always easy to stop and think about what a rational actor would do, it helps to assess your decision-making process and see how much fear or the emotional salience of a potential loss is guiding your preference towards a particular choice. Probe yourself on how much the allure of zero risk is influencing your preferences and whether it’s really more important than a greater reduction in risk.

How it all started

Although there are earlier findings showing a preference for certainty over overall risk reduction such as Prospect Theory, the zero risk bias as a unique cognitive phenomenon is often attributed to a 1987 paper published by Kip Viscusi, Wesley Magat, and Joel Hubert.4 The researcher’s found evidence for “certainty premiums” in eliminating risk by asking participants how much they would pay to reduce the possible risk of side effects from cleaning products (insecticide and toilet bowl cleaner). Viscusi and colleagues found people were willing to pay up to three times as much to reduce the risk of side effects from 5/15,000 cases to 0/15,000, as they were for a risk reduction from 15/15,000 to 10/15,000, despite the reductions in risk essentially being statistically negligible.

Example 1 - Allais Paradox

One of the most famous examples in behavioral economics is the Allais Paradox, a choice set included in Maurice Allais’ 1953 paper published in Econometrica.5 The problem rests on a hypothetical choice between two options.


Option A: Win $100 million for sure

Option B: 10% chance of winning $500 million

89% chance of winning $100 million

1% chance of not winning anything at all


People routinely express a preference for Option A, despite the expected value for Option B being much greater. Although Option B’s trade-off of having a 10% chance of earning an additional $400 million in exchange for the remote 1% of not collecting anything, is well worth the gamble from a rational perspective, people are deterred by the sliver of risk and opt for the option with zero risk despite it being the suboptimal choice.

Example 2 - The money back guarantee

Have you ever been watching an infomercial and just when you think they can’t sweeten the deal any more than they already have (call now, and they’ll quadruple the offer!), they say that if you’re not happy with the product, you can get your money back - guaranteed.

The money back guarantee is not just a tactic used on the shopping channel but across a number of consumer goods. It’s a widespread marketing tool that is largely successful due to its ability in leveraging the zero risk bias. The money back guarantee seduces consumers by eliminating the risk of a prospective purchase decision. Much of the risk in buying a product comes from the probability that you won’t be satisfied with it, but with the option of receiving a full refund if a buyer is unsatisfied, then the risk is no longer a worry.

The evidence on the subject offers support for the efficacy of the money back guarantee, with research having found that retailers using the money back guarantee see a boost in sales and profit,6 as well as increases in customer’s purchase satisfaction.7


What it is

Zero risk bias refers to our tendency to opt for complete risk elimination, sometimes over an alternative that actually offers greater overall predicted outcomes.

Why it happens

Choices with zero risk offer certainty, which the brain seeks to maximize in order to reduce cognitive strain. Additionally, losses loom larger than gains, and the prospect of a loss drives us to eschew that possibility even if it leads to a suboptimal choice.

Example 1 - Allais Paradox

Maurice Allais presented participants with two choices: the first option was a guaranteed jackpot while the second, though it had a greater expected value, carried a 1% of not winning anything at all. When presented with this choice set, people tend to opt for the first option with zero risk, despite it having a lower expected value.

Example 2 - The money back guarantee

This all too familiar marketing hook plays to consumer worries over the risk of not being satisfied with a product. By eliminating such a risk, consumers are more likely to buy.

How to avoid it

It can help to review your goal in making a decision. Is your goal overall risk reduction? Or does completely eliminating risk carry more value? Monitoring your emotion to see if it’s leading you astray from your decision-making goals can sometimes help mitigate the zero risk bias.

Related TDL articles

What Does China Approaching Epidemic Peak Mean For Us? Communicating Risk in the Age of Social Media

This article talks about the importance of adequately communicating risk to the public during the COVID-19 pandemic. As the zero risk bias shows, our perceptions of risk are not always “rational”.

CO2 Out of Sight, Not Out of Mind: Carbon Capture and Storage Risks

This article references the zero risk bias as a potential barrier to carbon capture. In the future, people may oppose a carbon capture facility as a way to eliminate the risk of possible leakage, rather than opt for the facility which can help reduce the greater risk of climate change.


  1. Aimé, P., & Grünbeck, J. (2019) Smart Persuasion: How Elite Marketers Influence Consumers (and Persuade Them to Take Action). Independently published
  2. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263. doi:10.2307/1914185
  3. Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological bulletin, 127(2), 267.
  4. Viscusi, W. K., Magat, W. A., & Huber, J. (1987). An investigation of the rationality of consumer valuations of multiple health risks. The RAND journal of economics, 465-479.
  5. Allais, M. (1953). Le comportement de l'homme rationnel devant le risque: critique des postulats et axiomes de l'école américaine. Econometrica: Journal of the Econometric Society, 503-546.
  6. Akçay, Y., Boyacı, T., & Zhang, D. (2013). Selling with money‐back guarantees: The impact on prices, quantities, and retail profitability. Production and Operations Management, 22(4), 777-791.
  7. Schmidt, S. L., & Kernan, J. B. (1985). The many meanings (and implications) of" satisfaction guaranteed.". Journal of Retailing.

About the Authors

Dan Pilat's portrait

Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Sekoul Krastev's portrait

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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