Editorial: Decision making under risk and ambiguity

Since at least the time of the 1713 Bernoulli Paradox, investigations of risk and uncertainty have been central to economic inquiry (Seidl, 2013). And despite this tenure, they still feel relevant novel and exciting. This is perhaps because uncertainty is inherent in the questions that plague us as individuals and as a society: Will I have a job? Will my house fall down? Will my children be okay? What will happen to the planet?

Indeed, risk and uncertainty are ubiquitous constructs in our lives. Potential parents must assess the risk of genetic abnormalities, businesses must expose themselves to possible losses, governments must assess the threats of conflict and climate crises. Importantly, though the risks present in each of these examples can be modeled by the same process or foundational theory, the behavioral response varies meaningfully across domains.

To that end, and as we see in this collected research topic, the act of decision making under risk and uncertainty is still very much an active area of research. The four papers herein highlight diverse and interdisciplinary research approaches for such a dynamic and varied field.

In “Risk and ambiguity…” the authors explore preferences for mitigation (as opposed to abatement) strategies to combat climate risk by means of a modified public goods game. Importantly, the paper highlights the differences between group and individual decision making, and furthermore the importance of heterogeneous subject types at that. The authors find that while introducing an ambiguous information environment has no effect average contributions to mitigation, it inhibits mitigation contributions among ambiguity averse subjects. Further, these subjects are often willing to pay for information in order to eliminate ambiguity. More than half of the time, subjects wish to have access to information and they actually use it when they obtain it.

Unsurprisingly, this speaks to the work conducted in “Beyond average preparedness…” where Nkouaga uses online polling data to explore earthquake preparedness among homeowners in the New Madrid Seismic Zone. Much like the ambiguity discussed in the previous paper, Earthquakes cannot be predicted (USGS). However, Nkouaga finds that “Community Engagement” which includes (but is not limited to) discussions with family and friends, participation in community events, and interactions with neighbors regarding earthquake hazards is the strongest predictor of earthquake preparedness. Importantly, Nkouaga's findings are robust to both OLS and quantile regression approaches at all quantiles suggesting that while the main results aren't merely being driven by an extreme portion of the distribution, some variables, such as household income, have important threshold values to consider, which could yield key insights as we look back at the costs to reveal information discussed above.

Similarly, in “Exposure to climate-change…” the authors explore whether long-term exposure to climate-change-related extreme weather events is associated with farmers' risk preferences. They use a mixed-methods study and find that farmers in risky locations are less risk-averse. While at first blush this may seem an obvious result, it is actually deeply profound, and speaks to not only the efficacy of their approach, but also a long line of literature on rational decision-making in unique environments (cf. Cox, 1997; Leeson et al., 2026). Further, understanding the behavioral response to climate events in the developing world can help us to better propose policy solutions for mitigation and abatement such as index insurance (cf. Harrison et al. 2025) discussed in “Risk and ambiguity…”.

And finally, in “Internal security…” explores these decisions in an intra-office setting. By presenting a fully mediated model, this paper is able to discuss the impacts of employability on (un)ethical behavior in favor of a home company's bottom line. In doing so, it speaks to the risk inherent in a real options framework when considering exits and next steps.

Throughout, we see discussions that remain important to us as behavioralists. For instance, “Risk and ambiguity…” highlights the importance of contextual (as opposed to neutral) framing. In doing so, the paper is reminiscent of Levitt and List (2007) which encourages us as experimentalists to think deeply about which parameters we are actually eliciting, while “Exposure to climate-change” blends methods using both an artefactual field experiment, as well as a household survey and utilizes a non-WEIRD sample, all lending credence to extant validity debates.

However, there is (thankfully) still more work to do. And while these papers highlight several disciplines, future work will be more interdisciplinary tying together frameworks from multiple fields (e.g., Lee, 2017; Lee and Salvatore, 2022). Further, the above papers together highlight an even more insidious thought: given incomplete data and opaque and complex models, we are constantly exposing ourselves to new forms of risk and uncertainty. And through our increased interactions with AI, machine learning, and algorithmic interfaces, a more thoroughly developed understanding of the human approach to risk and uncertainty will only better serve to navigate these new forms and beyond.

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Footnotes

Edited and Reviewed by: Subhasish M. Chowdhury, The University of Sheffield, United Kingdom