The editors of the collection, Daniel Lee of the University of Delaware and Indu Khurana of Hampden-Sydney College, open their editorial with a observation that resonates far beyond academia: uncertainty is woven into the questions that plague individuals and societies alike. Will I have a job? Will my house withstand the next disaster? Will my children be okay? What will happen to the planet? Potential parents weigh the risk of genetic abnormalities, businesses expose themselves to possible losses, and governments assess threats ranging from armed conflict to climate crises. Crucially, the editors note, although the risks in each of these domains can be modeled by the same foundational theory, the behavioral response varies meaningfully across contexts. It is precisely this variation that the collected papers set out to illuminate.

The first study in the collection tackles one of the defining policy challenges of the century: how to persuade people to fund climate risk mitigation. Using a modified public goods game, the authors of “Risk and ambiguity” explore preferences for mitigation strategies, as opposed to abatement strategies, in combating climate risk. The experimental design distinguishes between group and individual decision making and, importantly, accounts for heterogeneous subject types. The central finding is subtle but consequential: introducing an ambiguous information environment has no effect on average contributions to mitigation, yet it significantly inhibits mitigation contributions among ambiguity-averse subjects. In other words, the population-level average conceals a meaningful behavioral divide that only becomes visible when subject heterogeneity is taken seriously.

Perhaps the most striking result from this climate game concerns what people are willing to pay to escape ambiguity. More than half the time, participants wished to have access to information that would resolve the ambiguous environment, and they actually used that information once they obtained it. This willingness to pay for information carries direct policy implications. If ambiguity itself suppresses contributions to public mitigation efforts, then investments in clearer communication about climate risks, better probabilistic forecasts, and more transparent data could pay for themselves by unlocking cooperative behavior that uncertainty currently suppresses.

A second paper, “Beyond average preparedness,” brings the analysis of ambiguity into the seismic zone. Author Nkouaga uses online polling data to explore earthquake preparedness among homeowners in the New Madrid Seismic Zone, the intraplate fault system responsible for some of the largest earthquakes in United States history. Much like the ambiguity confronting climate game participants, earthquakes cannot be predicted, a point the United States Geological Survey emphasizes in its public guidance. Against this backdrop of irreducible uncertainty, Nkouaga finds that what the study terms “Community Engagement,” including discussions with family and friends, participation in community events, and interactions with neighbors regarding earthquake hazards, is the strongest predictor of earthquake preparedness. The finding suggests that preparedness is not merely a function of individual risk perception but of social embeddedness.

The methodological rigor of the preparedness study deserves particular attention. Nkouaga’s results hold under both ordinary least squares and quantile regression approaches at all quantiles, indicating that the main findings are not driven by an extreme portion of the preparedness distribution. Yet the quantile analysis also reveals something the average would hide: variables such as household income have important threshold values, meaning their effect on preparedness is not uniform across the distribution. This nuance connects back to the information-purchasing behavior observed in the climate game. If income thresholds determine who can act on risk information, then the costs of revealing information may fall unevenly across a population, and policies that subsidize information access could have disproportionate effects at specific points in the income distribution.

The third paper, “Exposure to climate-change,” asks whether long-term exposure to climate-change-related extreme weather events is associated with farmers’ risk preferences. Using a mixed-methods design that combines an artefactual field experiment with a household survey, the authors find that farmers in risky locations are less risk-averse. At first blush, the result may seem obvious: people who live with frequent hazards should get used to them. But the editors argue the finding is deeply profound, connecting to a long line of literature on rational decision-making in unique environments. Farmers who repeatedly experience extreme weather may rationally update their beliefs about the true distribution of risks, or they may sort into risky locations precisely because they tolerate risk. Either interpretation implies that risk preferences are not fixed traits but responses to lived experience, a conclusion with major implications for how researchers should elicit and interpret preference parameters.

The study’s use of a non-WEIRD sample, meaning participants drawn from outside the Western, Educated, Industrialized, Rich, and Democratic populations that dominate experimental economics, also lends credibility to ongoing debates about the external validity of laboratory findings. Understanding behavioral responses to climate events in the developing world, the editors note, can help policymakers design better solutions for mitigation and abatement, such as index insurance, a financial instrument that pays out based on observable triggers like rainfall rather than individually verified losses. Recent work on the welfare consequences of compound risks in index insurance underscores that the design of such instruments depends critically on how beneficiaries perceive and respond to layered uncertainty.

The fourth paper shifts the setting from fields and fault lines to the office. “Internal security” examines decisions in an intra-office setting, presenting a fully mediated model of the impacts of employability on unethical behavior undertaken in favor of a home company’s bottom line. The paper speaks to the risk inherent in a real options framework when employees and firms consider exits and next steps. In plain terms, workers who perceive their outside employment options as secure may behave differently, and potentially less ethically, toward their current employer than those who feel trapped. The result extends the study of decision under uncertainty from markets and disasters into the microfoundations of organizational behavior, where the stakes are reputational and legal rather than seismic.

Across all four papers, the editors identify methodological themes that will be familiar to behavioral scientists. The climate game highlights the importance of contextual, as opposed to neutral, framing, echoing calls for experimentalists to think deeply about which parameters they are actually eliciting. The farmers study blends methods and samples in ways that speak directly to validity debates. Yet the editors are careful to emphasize that there is still more work to do. Future research, they argue, will be more interdisciplinary, tying together frameworks from multiple fields. And the collected papers point toward an even more insidious conclusion: given incomplete data and opaque, complex models, we are constantly exposing ourselves to new forms of risk and uncertainty. As human interactions with artificial intelligence, machine learning, and algorithmic interfaces proliferate, a more thoroughly developed understanding of how humans approach risk and ambiguity will only become more essential for navigating the uncertainties of the digital age, and whatever lies beyond.

Subject of Research: Behavioral economics of decision making under risk and ambiguity

Article Title: Editorial: Decision making under risk and ambiguity

Article References: Lee, D., & Khurana, I. (2026). Editorial: Decision making under risk and ambiguity. Frontiers in Behavioral Economics, 5, Article 1960515. https://doi.org/10.3389/frbhe.2026.1960515

Image Credits: AI Generated

DOI: 10.3389/frbhe.2026.1960515

Keywords: risk, ambiguity, behavioral economics, decision making, climate risk, earthquake preparedness, public goods game, risk preferences, index insurance, uncertainty, experimental economics, real options

The research, conducted by Yucheng Liang of Carnegie Mellon University’s Tepper School of Business, Tony Q. Fan of Lehigh University’s College of Business, and Cameron Peng of the London School of Economics and Political Science, compared two belief-updating tasks that, on paper, look almost identical. In the first, known as an inference problem, a person observes informative signals about some hidden state of the world—say, whether a company is fundamentally healthy—and revises their beliefs about that state. In the second, a forecast-revision problem, the person sees exactly the same signals but must update their beliefs about a future outcome that depends on the underlying state, such as next quarter’s profits. If people processed information consistently, the two tasks should produce the same updating behavior. They do not.

Across the experiments, participants systematically underreacted to signals when they were inferring the underlying state, but overreacted to the very same signals when they were revising forecasts about future outcomes. The authors call this divergence the inference-forecast gap, and its implications ripple far beyond the laboratory. It suggests that the long-standing debate about whether humans are conservative information processors or impressionable extrapolators may be misframed: people can be both, depending on which mental category a problem falls into. The direction of bias is not a fixed personality trait but a consequence of how the belief-updating problem is represented.

“Economists have long used notions of underreaction and overreaction to explain puzzles in macroeconomics and finance, but have never reached consensus on why people underreact in some environments and overreact in others,” Liang notes. That lack of consensus has real consequences. Models of financial markets, macroeconomic expectations, and consumer behavior often bake in an assumption about how people respond to news, and picking the wrong assumption can distort predictions about asset prices, inflation expectations, and demand. The new findings suggest that the missing ingredient is not a deeper parameter of human psychology but a more careful accounting of the task itself.

The mechanism behind the gap, according to the authors, lies in the rules of thumb—simplifying heuristics—that people deploy in the two settings. When the problem is framed as inference about a hidden state, participants tend to behave conservatively, treating each signal as noisy and insufficiently diagnostic, which produces the dampened, underreacting response long documented in the literature on probability revision. When the same information is framed as a forecast revision, participants appear to lean on extrapolative shortcuts, letting the recent signals carry more weight than they statistically deserve, which generates overreaction. The signals are identical; the cognitive machinery applied to them is not. The gap, in other words, is largely driven by different simplifying rules used in the two tasks rather than by any difference in the information itself.

“Our findings suggest that this discrepancy may arise because people consider some problems inference tasks and others forecast-revision tasks, leading them to approach the two in fundamentally different ways,” says Fan, who led the study. “This implies that to understand biased reactions to information in the field, it is essential to first understand how people mentally represent the belief-updating problem they face.” That framing shifts the research agenda. Instead of asking whether people, in general, overreact or underreact to news, researchers and practitioners should first ask which representation a person is likely to adopt when confronted with a particular information environment. A stock analyst reading a earnings surprise, a voter hearing an economic statistic, and a doctor interpreting a test result may each be running a different mental program on the same data.

The study connects to a vigorous experimental literature that has documented context-dependent belief updating for years. Laboratory studies have found that people underreact when updating beliefs about binary states, yet overreact when forming expectations about series that appear to trend, such as stock prices or sports streaks. Field evidence mirrors the split: survey-based measures of professional forecasters often show sluggish revision of long-run views even as short-term predictions overshoot. What the new paper contributes is a controlled comparison that holds the information constant and varies only the elicitation—the type of belief being measured—thereby isolating the task representation as the driver of the bias. “We show that the direction of belief-updating biases depends on the type of belief elicited and the nature of the question,” Peng explains, “and by connecting the inference-forecast gap to the use of different simplifying rules of thumb, we highlight the role of complexity and incorrect mental models in explaining belief-updating biases.”

The emphasis on complexity and incorrect mental models is important because it moves the explanation away from motivated reasoning or emotional noise and toward the architecture of everyday cognition. Real-world belief-updating problems are rarely stated as cleanly as Bayes’ rule requires. People must decide what the signal means, how it relates to the outcome they care about, and how much of their prior view to retain. Each of those steps invites a shortcut, and the shortcuts that feel natural for one framing can be badly miscalibrated for another. An investor who treats a strong quarterly report as merely one noisy data point about firm quality is underreacting in the inference sense; the same investor who extrapolates that report into a rosy five-year forecast is overreacting in the forecast sense. Both behaviors can coexist in one person on the same afternoon, which is precisely what the inference-forecast gap predicts.

For practitioners, the findings carry practical weight. Financial regulators and market designers care about overreaction because it fuels bubbles and crashes, and about underreaction because it delays the incorporation of information into prices. Communication strategists, from public health officials to central bankers, want to know whether their audiences will absorb or exaggerate a given announcement. The study suggests that the answer depends less on the content of the message than on whether recipients mentally code it as evidence about a state of the world or as a cue about what happens next. Designing information releases, survey questions, or forecasting protocols with that distinction in mind could reduce systematic bias, or at least anticipate its direction.

The work also sharpens the theoretical conversation. Classic models of noisy expectation formation often assume a single, stable updating distortion. The inference-forecast gap implies that any such model is incomplete unless it specifies the belief being formed. Future research, the authors suggest, can map which environments push people toward the conservative inference mode and which push them toward extrapolative forecast mode, and can test whether training, experience, or better problem framing closes the gap. For now, the study offers a memorable lesson: the bias in our beliefs is not written solely in the news we receive, but in the question we silently ask ourselves when the news arrives. Same signal, different frame, opposite error—and that single insight may finally explain why humans manage to be too skeptical and too credulous at the same time.

Subject of Research: Belief updating biases in inference versus forecast-revision tasks

Article Title: Economists examine how people infer, revise beliefs in face of new information

Article References: Economists examine how people infer, revise beliefs in face of new information. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: belief updating, behavioral economics, inference-forecast gap, underreaction, overreaction, Econometrica, decision making, heuristics, forecasting, experimental economics, Carnegie Mellon University, financial expectations