When deciding for others based on explicitly described odds and outcomes, people often have different risk preferences for others than for themselves. In two pre-registered experiments, we examine risk preference for others where people learn about the odds and outcomes by experiencing them through sampling. In both experiments, on average, people were more risk averse for others than for themselves, but only when the risky option had a higher expected value. Furthermore, based on a separate set of choices, we classified people as pro- or anti-social. Only those people classified as anti-social were more risk averse for others, whereas those classified as prosocial chose similarly for themselves and others. When the uncertainty was removed, however, all participants exhibited less anti-social behavior. Together, these results suggest that anti-social motives contribute to the observed limited risk taking for others and that outcome uncertainty facilitates the expression of these motives.

Earlier frameworks have indicated that older adults tend to experience decline intheir deliberative decisional capacity, while their affective abilities tend toremain intact (Peters, Hess, Västfjäll, & Auman, 2007). Thepresent study applied this framework to the study of risky decision-makingacross the lifespan. Two versions of the Columbia Card Task (CCT) were used totrigger either affective decision-making (i.e., the “warm” CCT) ordeliberative decision-making (i.e., the “cold” CCT) in a sample of158 individuals across the lifespan. Overall there were no age differences inrisk seeking. However, there was a significant interaction between age andcondition, such that older adults were relatively more risk seeking in the coldcondition only. In terms of everyday decision-making, context matters and riskpropensity may shift within older adults depending upon the context.

When there are multiple competing objectives in a decision-making process, Multi-Attribute Choice scoring models are excellent tools, permitting the incorporation of both subjective and objective attributes. However, their accuracy depends upon the subjective techniques used to construct the attribute scales and their concomitant weights. Conventional techniques using local scales tend to overemphasize small differences in attribute measures, which may yield erroneous conclusions. The Range Sensitivity Principle (RSP) is often invoked to adjust attribute weights when local scales are used. In practice, however, decision makers often do not follow the prescriptions of the Range Sensitivity Principle and under-adjust the weights, resulting in potentially poor decisions. Examples are discussed as is a proposed solution: the use of global scales instead of local scales.

We show how to elicit the beliefs of an expert in the form of a “most likely interval”, a set of future outcomes that are deemed more likely than any other outcome. Our method, called the Most Likely Interval elicitation rule (MLI), asks the expert for an interval and pays according to how well the answer compares to the actual outcome. We show that the MLI performs well in economic experiments, and satisfies a number of desirable theoretical properties such as robustness to the risk preferences of the expert.

We consider the recently-developed “surprisingly popular” method for aggregating decisions across a group of people (Prelec, Seung and McCoy, 2017). The method has shown impressive performance in a range of decision-making situations, but typically for situations in which the correct answer is already established. We consider the ability of the surprisingly popular method to make predictions in a situation where the correct answer does not exist at the time people are asked to make decisions. Specifically, we tested its ability to predict the winners of the 256 US National Football League (NFL) games in the 2017–2018 season. Each of these predictions used participants who self-rated as “extremely knowledgeable” about the NFL, drawn from a set of 100 participants recruited through Amazon Mechanical Turk (AMT). We compare the accuracy and calibration of the surprisingly popular method to a variety of alternatives: the mode and confidence-weighted predictions of the expert AMT participants, the individual and aggregated predictions of media experts, and a statistical Elo method based on the performance histories of the NFL teams. Our results are exploratory, and need replication, but we find that the surprisingly popular method outperforms all of these alternatives, and has reasonable calibration properties relating the confidence of its predictions to the accuracy of those predictions.

In three studies, we examined factors that may temporarily attenuate information search. People are generally curious and dislike uncertainty, which typically encourages them to look for relevant information. Despite these strong forces that promote information search, people sometimes deliberately delay obtaining valuable information. We find they may do so when they are concerned that the information might interfere with future pleasurable activities. Interestingly, the decision to search or to postpone searching for information is influenced not only by the value and importance of the information itself but also by well-being maintenance goals related to possible detrimental effects that negative knowledge may have on unrelated future plans.

Recent research has highlighted a tendency for more rational and deliberative decision-making in individuals with autism. We tested this hypothesis by using eye-tracking to investigate the information processing strategies that underpin multi-attribute choice in a sample of adults diagnosed with autism spectrum condition. We found that, as the number of attributes defining each option increased, autistic decision-makers were speedier, examined less of the available information, and spent a greater proportion of their time examining the option they eventually chose. Rather than indicating a more deliberative style, our results are consistent with a tendency for individuals with autism to narrow down the decision-space more quickly than does the neurotypical population.

Pie charts are often used to communicate risk, such as the risk of driving. In the foreground-background salience effect (FBSE), foreground (probability of bad event) has greater salience than background (no bad event) in such a chart. Experiment 1 confirmed that the displays format of pie charts showed a typical FBSE. Experiment 2 showed that the FBSE resulted from a difference in cognitive efforts in processing the messages and that a foreground-emphasizing display was easier to process. Experiment 3 manipulated subjects’ information processing mindset and explored the interaction between displays format and information processing mindset. In the default mindset, careless subjects displayed a typical FBSE, while those who were instructed to be careful reported similar risk-avoidant behavior preference reading both charts. Suggestions for improving risk communication are discussed.

Affective forecasting skills have important implications for decision making. However, recent research suggests that immune neglect—the tendency to overlook coping strategies that reduce future distress—may lead to affective forecasting problems. Prior evidence for immune neglect has been indirect. More direct evidence and a deeper understanding of immune neglect are vital to informing the design of future decision-support interventions. In the current study, young adults (N = 325) supplied predicted, actual, and recollected reactions to an emotionally-evocative interpersonal event, Valentine’s Day. Based on participants’ qualitative descriptions of the holiday, a team of raters reliably coded the effectiveness of their coping strategies. Supporting the immune neglect hypothesis, participants overlooked the powerful role of coping strategies when predicting their emotional reactions. Immune neglect was present not only for those experiencing the holiday negatively (non-daters) but also for those experiencing it positively (daters), suggesting that the bias may be more robust than originally theorized. Immune neglect was greater for immediate emotional reactions than more enduring reactions. Further, immune neglect was conspicuously absent from recollected emotional reactions. Implications for decision-support interventions are discussed.

Shame leads to devaluation of the social self, and thus to a desire to improve self-esteem. Money, which is related to the notion of one’s ability, may help people demonstrate competence and gain self-esteem and respect from others. Based on the perspectives of feelings-as-information and threatened ego, we tested the hypothesis that a sense of shame heightens the desire for money, prompting self-interested behaviors as reflected by monetary donations and social value orientation. The results showed that subjects in the shame condition donated less money (Experiment 1) and exhibited more self-interested choices in the modified decomposed game (Experiment 2). The desire for money as reflected in overestimated coin sizes mediated the effect of shame on self-interested behavior. Our findings suggest that shame elicits the desire to acquire money to amend the threatened social self and improve self-esteem; however, it may induce a self-interested inclination that could harm social relationships.

Consumers often face prices that are the sum of two components, for example, an online purchase that includes a stated price and shipping costs. In such cases consumer behavior may be influenced by framing, i.e., how the components are bifurcated. Previous studies have demonstrated the effects of framing and anchoring in auctions. This study examines bidding patterns in a series of first-price sealed-bid experimental money auctions (where the commodity being auctioned is money itself). We hypothesize that bidders’ behavior is affected by the framing of the potential monetary payoff into “monetary prize” and “winner’s bonus” components. We find strong evidence of an anchoring effect that influences the strategic behavior of bidders.

Individual differences in cognitive abilities and skills can predict normatively superior and logically consistent judgments and decisions. The current experiment investigates the processes that mediate individual differences in risky choices. We assessed working memory span, numeracy, and cognitive impulsivity and conducted a protocol analysis to trace variations in conscious deliberative processes. People higher in cognitive abilities made more choices consistent with expected values; however, expected-value choices rarely resulted from expected-value calculations. Instead, the cognitive ability and choice relationship was mediated by the number of simple considerations made during decision making — e.g., transforming probabilities and considering the relative size of gains. Results imply that, even in simple lotteries, superior risky decisions associated with cognitive abilities and controlled cognition can reflect metacognitive dynamics and elaborative heuristic search processes, rather than normative calculations. Modes of cognitive control (e.g., dual process dynamics) and implications for process models of risky decision-making (e.g., priority heuristic) are discussed.

Previous studies have found that the proportions of people who endorsed utilitarian decisions varied across different variants of the trolley dilemma. In this paper, we explored whether moral choices were associated with beliefs about outcome probabilities in different moral dilemmas. Results of two experiments showed that participants’ perceptions of outcome probabilities were different between two dilemmas that were similar to the classical switch case and footbridge case. Participants’ judgments of the outcome probabilities were associated with their moral choices. The results suggested that participants might not accept task instructions and thus did not perceive the outcomes in the dilemmas as certain. We argued that researchers who endorse descriptive tasks in moral reasoning research should be cautious about the findings and should take participants’ beliefs in the outcomes into account.

The present research addresses advice taking from a holistic perspective covering both advice seeking and weighting. We build on previous theorizing that assumes that underweighting of advice results from biased samples of information. That is, decision makers have more knowledge supporting their own judgment than that of another person and thus weight the former stronger than the latter. In the present approach, we assume that participants reduce this informational asymmetry by the sampling of advice and that sampling frequency depends on the information ecology. Advice that is distant from the decision maker’s initial estimate should lead to a higher frequency of advice sampling than close advice. Moreover, we assume that advice distant from the decision maker’s initial estimate and advice that is supported by larger samples of advisory estimates are weighted more strongly in the final judgment. We expand the classical research paradigm with a sampling phase that allows participants to sample any number of advisory estimates before revising their judgments. Three experiments strongly support these hypotheses, thereby advancing our understanding of advice taking as an adaptive process.

Revealed preference is the dominant approach for inferring preferences, but it is limited in that it relies solely on discrete choice data. When a person chooses one alternative over another, we cannot infer the strength of their preference or predict how likely they will be to make the same choice again. However, the choice process also produces response times (RTs), which are continuous and easily observable. It has been shown that RTs often decrease with strength-of-preference. This is a basic property of sequential sampling models such as the drift diffusion model. What remains unclear is whether this relationship is sufficiently strong, relative to the other factors that affect RTs, to allow us to reliably infer strength-of-preference across individuals. Using several experiments, we show that even when every subject chooses the same alternative, we can still rank them based on their RTs and predict their behavior on other choice problems. We can also use RTs to predict whether a subject will repeat or reverse their decision when presented with the same choice problem a second time. Finally, as a proof-of-concept, we demonstrate that it is also possible to recover individual preference parameters from RTs alone. These results demonstrate that it is indeed possible to use RTs to infer preferences.

Research on the processing of recognition information has focused on testing the recognition heuristic (RH). On the aggregate, the noncompensatory use of recognition information postulated by the RH was rejected in several studies, while RH could still account for a considerable proportion of choices. These results can be explained if either a) a part of the subjects used RH or b) nobody used it but its choice predictions were accidentally in line with predictions of the strategy used. In the current study, which exemplifies a new approach to model testing, we determined individuals’ decision strategies based on a maximum-likelihood classification method, taking into account choices, response times and confidence ratings simultaneously. Unlike most previous studies of the RH, our study tested the RH under conditions in which we provided information about cue values of unrecognized objects (which we argue is fairly common and thus of some interest). For 77.5% of the subjects, overall behavior was best explained by a compensatory parallel constraint satisfaction (PCS) strategy. The proportion of subjects using an enhanced RH heuristic (RHe) was negligible (up to 7.5%); 15% of the subjects seemed to use a take the best strategy (TTB). A more-fine grained analysis of the supplemental behavioral parameters conditional on strategy use supports PCS but calls into question process assumptions for apparent users of RH, RHe, and TTB within our experimental context. Our results are consistent with previous literature highlighting the importance of individual strategy classification as compared to aggregated analyses.

The DOSPERT scale has been used extensively to understand individual differences in risk attitudes across varying decision domains since 2002. The present study reports a reliability generalization meta-analysis to summarize the internal consistency of both the initial and the revised versions of DOSPERT. It also examined factors that can influence the reliability of the DOSPERT and its subscales. A total of 104 samples (N = 30,109) that reported 465 coefficient alphas were analyzed. Results of meta-regression models showed that the overall coefficient alpha of the DOSPERT total scores was satisfactory, regardless of the scale and study characteristics. Coefficient alphas varied significantly across domain subscales, with values ranging from .68 for the social domain to .80 for the recreational domain. In addition, the alpha coefficients of subscales varied significantly depending on various study characteristics. Finally, we report the meta-analysis of the intercorrelations among DOSPERT subscales and reveal that intercorrelations among the subscales are heterogeneous. We discuss the theoretical implications of the present findings.

Recent research has shown that risk and reward are positively correlated in many environments, and that people have internalized this association as a “risk-reward heuristic”: when making choices based on incomplete information, people infer probabilities from payoffs and vice-versa, and these inferences shape their decisions. We extend this work by examining people’s expectations about another fundamental trade-off — that between monetary reward and delay. In 2 experiments (total N = 670), we adapted a paradigm previously used to demonstrate the risk-reward heuristic. We presented participants with intertemporal choice tasks in which either the delayed reward or the length of the delay was obscured. Participants inferred larger rewards for longer stated delays, and longer delays for larger stated rewards; these inferences also predicted people’s willingness to take the delayed option. In exploratory analyses, we found that older participants inferred longer delays and smaller rewards than did younger ones. All of these results replicated in 2 large-scale pre-registered studies with participants from a different population (total N = 2138). Our results suggest that people expect intertemporal choice tasks to offer a trade-off between delay and reward, and differ in their expectations about this trade-off. This “delay-reward heuristic” offers a new perspective on existing models of intertemporal choice and provides new insights into unexplained and systematic individual differences in the willingness to delay gratification.

When participants in psychophysical experiments are asked to estimate or identify stimuli which differ on a single physical dimension, their judgments are influenced by the local experimental context — the item presented and judgment made on the previous trial. It has been suggested that similar sequential effects occur in more naturalistic, real-world judgments. In three experiments we asked participants to judge the prices of a sequence of items. In Experiment 1, judgments were biased towards the previous response (assimilation) but away from the true value of the previous item (contrast), a pattern which matches that found in psychophysical research. In Experiments 2A and 2B, we manipulated the provision of feedback and the expertise of the participants, and found that feedback reduced the effect of the previous judgment and shifted the effect of the previous item's true price from contrast to assimilation. Finally, in all three experiments we found that judgments were biased towards the centre of the range, a phenomenon known as the “regression effect” in psychophysics. These results suggest that the most recently-presented item is a point of reference for the current judgment. The findings inform our understanding of the judgment process, constrain the explanations for local context effects put forward by psychophysicists, and carry practical importance for real-world situations in which contextual bias may degrade the accuracy of judgments.

Granting a short-term loan is a critical decision. A great deal of research hasconcerned the prediction of credit default, notably through Machine Learning(ML) algorithms. However, given that their black-box nature has sometimes led tounwanted outcomes, comprehensibility in ML guided decision-making strategies hasbecome more important. In many domains, transparency and accountability are nolonger optional. In this article, instead of opposing white-box againstblack-box models, we use a multi-step procedure that combines the Fast andFrugal Tree (FFT) methodology of Martignon et al. (2005) and Phillips et al.(2017) with the extraction of post-hoc explainable informationfrom ensemble ML models. New interpretable models are then built thanks to theinclusion of explainable ML outputs chosen by human intervention. Ourmethodology improves significantly the accuracy of the FFT predictions whilepreserving their explainable nature. We apply our approach to a dataset ofshort-term loans granted to borrowers in the UK, and show how complex machinelearning can challenge simpler machines and help decision makers.