In two studies of attitudes and beliefs of American adults (N = 482), there is a substantial positive correlation between pro-natural and pro-sustainability, a weak negative correlation between pro-technology and pro-natural, and a moderate positive or negative correlation between pro-technology and pro-sustainability, depending on the way technology is framed. Participants also believe strongly that natural policies are ‘good for’ sustainability. Liberals are substantially more pro-sustainable than conservatives, but both political groups are strongly pro-natural. Most respondents explicitly believe that organic farming is ‘good for sustainability’, and many believe that genetically engineered foods are not good for sustainability. On open-ended explanations for their beliefs about positive linkages between natural and sustainability, respondents’ reasons are frequently vague, very generic, factually incorrect, or irrelevant to sustainability (e.g., claims that genetically modified organisms are unnatural or bad for human health). Respondents are inclined to believe in a positive association, often a causal link, between natural and sustainable, both concepts they favor. Additional results from a British adult sample show that 65% agree with the statement: ‘Generally, more natural things cause more sustainable outcomes.’ This may be an example of causal inference supported by congruent valences.

Many journals ask authors to report confidence intervals (to quantify estimation precision or uncertainty) and measures of effect size (to quantify a factor’s explanatory power). Arguments for such practices focus on benefits to interpreting and applying scientific findings that go beyond merely detecting effects, thereby implying that effect sizes and confidence intervals should be reported and discussed. Accordingly, we examined 150 recent articles from 6 journals that publish research on Judgment and Decision Making (JDM) to survey current practices for reporting and discussing results. We recorded which of those articles report p-values, standardized effect sizes, and confidence/credibility intervals in their Results sections. We examined the articles’ narrative sections (Abstract, Discussion/Conclusion) for explicit reference to the presence/absence of an effect, an effect’s size, and the precision or range associated with an estimate. Ninety-one percent of articles reported p-values, CI0.95 [85%, 95%], and all discussed the presence or absence of effects. Most articles gave effect size information, with 73%, CI0.95 [65%, 79%], reporting standardized effect sizes, and 63%, CI0.95 [55%, 71%], reporting confidence/credibility intervals or graphical SE bars. However, an estimation perspective was less apparent in the articles’ Discussion sections, wherein 59%, CI0.95 [51%, 66%], discussed effect size information—though often with limited detail—and only 3%, CI0.95 [1%, 6%], discussed interval estimates. Mostly, it seems, JDM researchers follow guidelines for reporting effect size and the uncertainty and precision for effect estimates. Yet, one might ask whether this impacts researchers’ interpretation and communication of those effects as it should.

Our decisions frequently involve combinations of gains and losses occurring at different points in time, such as enduring early losses for future gains (investments) or enjoying immediate gains at the expense of future losses (loans). This research introduces novel experiments that examine how binary intertemporal payment options, framed as either investments or loans, influence decision-making. Each option comprised two payment components: common payments, which are identical between the options, and focal payments, which vary between the options. Through strategic manipulation of these payments, the research explores how Investment or Loan frames affect time preferences. Our three studies consistently indicate that the common payments tend to be disregarded and thus the preferences are affected by framing. Notably, this remained true even when common payments were substantial (Study 2), and the framing effect was also found in scenarios where decisions carried real financial consequences (Study 3).

People with stronger conspiracy beliefs tend to trust others less, show more antisocial tendencies, and behave more self-centeredly. We investigated whether they are also less likely to act generously. In Study 1 (N = 850; UK), conspiracy beliefs were negatively correlated with charitable donations, though effect sizes were small. In Study 2 (N = 323; US), conspiracy beliefs did not predict sharing in a Dictator Game. In Study 3 (N = 830; US), higher conspiracy beliefs were related to more generosity, but only when donations went directly to the recipient without intermediaries. Overall, people with higher conspiracy beliefs are not less generous per se, but their generosity may be constrained by distrust in institutions or intermediaries.

The combination of human forecasters’ subjective probability estimates usually improves upon the estimates provided by individual forecasters. In order to combine the probability estimates in a Bayes optimal way, prior work proposed a normative Bayesian fusion model that models the estimates with a beta distribution conditioned on their truth value. However, this model assumes conditionally independent probability estimates, although estimates provided by different forecasters are usually correlated. Here, we introduce a Bayesian model for combining subjective probability estimates that explicitly considers their correlation. We assume that an estimate provided by a forecaster for a given query depends on both the forecaster’s skill and the query’s difficulty. The correlation between probability estimates provided by different forecasters is assumed to be caused by the queries that make the forecasters provide similar estimates, for example, correct and highly confident estimates for very easy queries. Our model represents the probability estimates with a beta distribution conditioned on their truth value. It explicitly models the forecasters’ skills and the queries’ difficulties with skill parameters specific for each forecaster and difficulty parameters specific for each query. In this way, it can model the correlations between probability estimates and consider it when combining the estimates. Evaluations on a data set consisting of the subjective probability estimates of 85 human forecasters for 180 queries show improved fusion performance in terms of Brier score compared to related Bayesian fusion models. In particular, it outperforms independent fusion models that suffer from overconfidence.

The COVID-19 pandemic has illustrated the importance of public support for non-pharmaceutical public health interventions and the perils of rampant spread of misinformed and conspiratorial beliefs. Open-minded epistemic attitudes may be associated with adherence to public health recommendations and protect against holding false beliefs. In a large (N = 46,745 from 68 countries) global sample collected during the first months of the COVID-19 pandemic, we find that a six-item self-report measure of open-mindedness predicts decreased belief in COVID-19 conspiracy theories, increased physical distancing, increased engagement in recommended hygienic behaviors, and increased support for public health policies that aimed at decreasing COVID-19 transmission. In fact, out of 17 individual difference measures that we examined, open-mindedness proved to be the strongest or among the strongest predictors of rejecting conspiracy beliefs, of supporting physical distancing and public health policies, and of engaging in physical hygiene behaviors. In exploratory analyses of the open-mindedness measure, we found that public health support is associated with a learning-orientedfactor while conspiratorial beliefs were associated with a threat-oriented factor. These results suggest that it will be important to investigate whether open-mindedness can be cultivated or encouraged through educational or other interventions to ensure that public health is protected and that conspiracy theories do not spread.

The goal of this paper is to study individual variation in participants’ adherence to conflicting moral views. To do this, we elicit participants’ reflective attitudes in an argumentative task and introduce a new Conflict model of moral decision-making. This Conflict model builds on the widely used CNI model of moral judgments (Gawronski et al. [2017, Journal of Personality and Social Psychology, 113, 343–376]) but improves it in several respects. First, we follow Skovgaard-Olsen and Klauer (2024, Personality and Social Psychology Bulletin, 50(9), 1348–1367) in extending the model to investigate invariance violations of the models’ parameters. Second, we model cases in which participants are conflicted between utilitarian and deontological response tendencies. In Experiment 1, we employ an argumentative paradigm to elicit commitments for moral views from participants to estimate latent classes in participants’ moral views. We then measure a range of egoistic and altruistic covariates used in Kahane et al. (2015, Cognition, 134, 193–209) and Conway et al. (2018, Cognition, 179, 241–265) to investigate whether participants’ acceptance of instrumental harm is associated with a genuine concern for the greater good or whether it is rather driven by antisocial character traits (Bartels and Pizarro [2011, Cognition, 121, 154–161]). Next, we report two validation studies of our new Conflict model. In a preregistered experiment, the discriminant validity of the conflict detection/resolution path of the Conflict model and the construct validity of its conflict parameter are tested. Finally, in a second validation study, we contrast response formats of dilemma judgments and find evidence in favor of using a format in which participants can opt out of difficult moral dilemmas when they feel conflicted, over the traditional format in moral psychology that lacks this possibility. We show that the CNI model is challenged by the finding of asymmetries in experienced conflict across conditions.

Personality tests are commonly used to hire suitable employees but this process is susceptible to strategic misrepresentation by job-seekers. This article uses a lab experiment as an analogy of such a hiring process by using a repeated public goods game (PGG) as a proxy for a cooperative work environment. Participants first complete a Big Five personality test, focusing on the trait of ‘Agreeableness’, which some previous studies have associated with prosocial cooperation in the PGG. Two groups are formed: a high Agreeableness group and a low Agreeableness group. The experiment manipulates the timing of revealing the group formation rule, as knowing the rule before the personality test allows for misrepresentation of Agreeableness. I find no evidence of substantial misrepresentation when the group formation rule is revealed before the personality test. I do find that Agreeableness group formation increases contributions for both high and low groups, but only when it is described to participants before the PGG. I find no evidence that Agreeableness is related to contributions in the PGG.

This manuscript examines how growing up with a sibling relates to prosociality and how knowledge of a partner’s sibling background may serve as a behavioral cue. In a series of experimental games, we found that individuals with siblings were significantly more likely to cooperate in stag hunt and contribute more in public goods and dictator games than only children (OC) on average. In two treatments where a sibling status cue is exogenously revealed, only-child pairs exhibited reduced prosociality. OC exhibit different empirical expectations of behavior compared to those with siblings, while generally sharing the same normative beliefs. Language AI analysis of subjects’ written perspectives on the games corroborates these patterns. We conclude that OC exhibit more context-dependent prosociality, with behavior more closely aligned with empirical expectations than normative beliefs, a pattern not observed in those with siblings.

People’s expectations about the outcomes of elections often match their preferences, suggesting that people engage in wishful thinking. This often-documented link between people’s preferences and expectations is particularly pervasive and difficult to debias. One recent exception was a study by Rose and Aspiras (2020, Journal of Behavioral Decision Making, 33(4), 411–426), where participants who went through a brief perspective-taking intervention showed a reduced preference–expectation link when making predictions about the 2016 U.S. presidential election. We used a similar intervention and extended their research to the 2020 U.S. presidential election. In contrast to Rose and Aspiras, the link between people’s preferences and their expectations was not affected by the perspective-taking intervention. Regardless of whether participants took the perspective of another person or not, they exhibited a strong tendency to predict that their preferred candidate would win. Differences between our study and the study by Rose and Aspiras are discussed, as are the implications of our findings.

Managers confronting important strategic decisions often receive diagnostic information sequentially over time. As new information becomes available, they may need to update their understanding of the situation and possibly revise their preferences. During a decision, as a preference develops for one alternative course of action, a nonconscious goal of maintaining consistency between that preference, however tentative, and the new information can lead to an interpretation of that information so as to support the current preference. This tendency to bias or distort information to support the currently preferred option can in turn lead to even greater confidence in that leading option, despite the increase in confidence being unwarranted by the information itself. The result of such a biased decision process can be overconfidence in the chosen course of action. To show this, in the current work, experienced managers engaged in a realistic business decision task with their levels of information distortion and confidence tracked throughout the decision. Over the course of the decision, confidence in the leading action increased as a function of distortion. The results confirmed that distortion-driven confidence can develop even when decision makers have no prior preference for one of the outcomes.

We tested whether people engage in proportional thinking when comparing the value of the lives of people in different countries, specifically, whether people consider a certain number of lost lives in a smaller country to be equivalent to the loss of a larger number of lives in a country with a larger population. We found evidence for this form of proportional thinking in Study 1, and in Studies 2–3 we further observed that it is modulated by motivated reasoning: In Study 2, there was more proportional thinking when it benefited the ingroup (1 ingroup life equals 4 outgroup lives) than when it benefited the outgroup (1 outgroup life equals 4 ingroup lives). In Study 3, there was more proportional thinking when it benefited the victim in a war (1 victim life equals 4 aggressor lives) than when both countries were victims. Study 3 also showed that this form of proportional thinking is more prevalent when thinking about collectives (1,000 lives in the smaller country are equivalent to 4,000 lives in the larger country) versus individuals (1 life in the smaller country is equivalent to 4 lives in the larger country).

Willingness to help a needy person may depend on whether the person is perceived as responsible for their predicament. However, information regarding the cause is not always available, and people often ‘look the other way’ when it is. The present research explores whether potential donors choose to obtain information about the cause of the other’s need and, more importantly, how this choice to pursue information is affected by the donors’ feelings of entitlement. Across four studies, we find that decision makers who pursue information about why others are in need are more likely to offer help. Yet we also measure and manipulate the feelings of entitlement and find that those who are high in entitlement are more likely to seek the information regarding the person in need. Their higher tendency to pursue more information makes them more likely to help than they would otherwise.