Prescriptions Under Uncertainty
My most recent line of research examines prescriptions under uncertainty, namely how people believe that other people should estimate, think, and feel about uncertain outcomes. More specifically, when faced when an uncertain outcome, what do people believe is the best outlook—an optimistic, pessimistic, or realistic one—that others should employ to make the best judgment?
My dissertation, titled "The Optimism Gap", consisted of three empirical papers investigating this and related questions. Read about the specific papers below.
Generally, in my dissertation, I explained how different ways of asking people their recommended levels of optimism can have a dramatic influence on whether people seem to support having optimistically-biased expectations for uncertain events. I found that people do not generally recommend being overly optimistic, meaning they do not recommend that people overestimate the likelihood of desirable events. I also examined if people supposed having optimistically or pessimistically-biased expectations about future COVID-19 and weather-related events. For those events, people did not systematically prescribe optimistic estimations of key events. In fact, for both estimating likelihoods about potential, immediate problems such as contracting COVID-19 and distant, in the future problems, such as a sea-level rise, people thought other people should be pessimistic about crisis-related outcomes.
Abstract
Past work has suggested that people prescribe optimism—believing it is better to be optimistic, instead of accurate or pessimistic, about uncertain future events. Here, we identified and addressed an important ambiguity about whether those findings reflect an endorsement of biased beliefs—i.e., whether people prescribe likelihood estimates that reflect overoptimism. In three studies, participants (total N = 663 U.S. university students) read scenarios about protagonists facing uncertain events with a desired outcome. Results replicated prescriptions of optimism when using the same solicitations as in past work. However, we found quite different prescriptions when using alternative solicitations that asked about potential bias in likelihood estimations and that did not involve vague terms like “optimistic.” Participants generally prescribed being optimistic, feeling optimistic, and even thinking optimistically about the events, but they did not prescribe overestimating the likelihood of those events.
Abstract
During a global crisis, does the desire for good news also mean an endorsement of an optimistic bias? Five pre-registered studies, conducted at the start of the COVID pandemic, examined people’s lay prescriptions for thinking about uncertainty—specifically whether they thought forecasters should be optimistic, realistic, or pessimistic in how they estimated key likelihoods. Participants gave prescriptions for forecasters with different roles (e.g., self, family member, public official) and for several key outcomes (e.g., contracting COVID, vaccine development). Overall, prescribed optimism was not the norm. In fact, for negative outcomes that were of high concern, participants generally wanted others to have a pessimistic bias in how they estimated likelihoods. For positive outcomes, people favored more accurate estimation. These patterns held regardless of the assumed forecaster’s role. A common justification for advocating for a pessimistic bias in forecasts was to increase others' engagement in protective or preventative behaviors.
Do People Strategically Prescribe Pessimism for Threatening Weather Events?
Paper forthcoming
Summary
The last paper presented in this thesis examined how people prescribe that other people estimate uncertain weather events in the new context of climate uncertainty. Two preregistered studies (N = 419 Mturkers) built on the first two chapters with two primary goals in mind: to extend the investigation of prescriptions of biased estimation into new context of climate uncertainty, as well as sought to find a reason underlying why people prescribe biased estimation, even when given the potential of being accurate. Study 1 tested if people’s prescriptions are systematically sensitive to the severity of event outcomes and found participants would alter prescriptions of biased estimation depending on outcome severity. However, Study 2 found that the influence of severity did not depend on the hypothetical protagonist’s ability to prepare for the outcome. Together, the two studies showed that people uniformly prescribed pessimistically-biased estimation of weather events.