We consider the wisdom of the crowd situation in which individuals make binary decisions, and the majority answer is used as the group decision. Using data sets from nine different domains, we examine the relationship between the size of the majority and the accuracy of the crowd decisions. We find empirically that these calibration curves take many different forms for different domains, and the distribution of majority sizes over decisions in a domain also varies widely. We develop a growth model for inferring and interpreting the calibration curve in a domain, and apply it to the same nine data sets using Bayesian methods. The modeling approach is able to infer important qualitative properties of a domain, such as whether it involves decisions that have ground truths or are inherently uncertain. It is also able to make inferences about important quantitative properties of a domain, such as how quickly the crowd accuracy increases as the size of the majority increases. We discuss potential applications of the measurement model, and the need to develop a psychological account of the variety of calibration curves that evidently exist.

Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTrees package. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTrees can predict data. Simulation results show that FFTs created by FFTrees can predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use.

Good judgment is often gauged against two gold standards – coherence and correspondence. Judgments are coherent if they demonstrate consistency with the axioms of probability theory or propositional logic. Judgments are correspondent if they agree with ground truth. When gold standards are unavailable, silver standards such as consistency and discrimination can be used to evaluate judgment quality. Individuals are consistent if they assign similar judgments to comparable stimuli, and they discriminate if they assign different judgments to dissimilar stimuli. We ask whether “superforecasters”, individuals with noteworthy correspondence skills (see Mellers et al., 2014) show superior performance on laboratory tasks assessing other standards of good judgment. Results showed that superforecasters either tied or out-performed less correspondent forecasters and undergraduates with no forecasting experience on tests of consistency, discrimination, and coherence. While multifaceted, good judgment may be a more unified than concept than previously thought.

The effect of choice bracketing — the consideration of repeated decisions as a set versus in isolation — has important implications for products that are inherently time-sensitive and entail varying levels of risk, including retirement accounts, insurance purchases, and lottery preferences. We show that broader choice brackets lead to more optimal risk preferences across all risk types, including negative expected value and pure-loss gambles, suggesting that broad decision framing can help individuals make better choices over risks more generally. We also examine the mechanism behind these bracketing effects. We find that bracketing effects work by attenuating (magnifying) the weight placed on potential losses for positive EV (non-positive EV) gambles and by providing aggregated outcomes that might not otherwise be calculated. Thus, decision frames that provide probability distributions or aggregated outcomes can help individuals maximize expected value across different types of risky prospects.

Experiments on economic games typically fail to find positive reputational effects of using peer punishment of selfish behavior in social dilemmas. Theorists had expected positive reputational effects because of the potentially beneficial consequences that punishment may have on norm violators’ behavior. Going beyond the game-theoretic paradigm, we used vignettes to study how various social factors influence approval ratings of a peer who reprimands a violator of a group-beneficial norm. We found that ratings declined when punishers showed anger, and this effect was mediated by perceived aggressiveness. Thus the same emotions that motivate peer punishers may make them come across as aggressive, to the detriment of their reputation. However, the negative effect of showing anger disappeared when the norm violation was sufficiently severe. Ratings of punishers were also influenced by social distance, such that it is less appropriate for a stranger than a friend to reprimand a violator. In sum, peer punisher ratings were very high for a friend reprimanding a severe norm violation, but particularly poor for a stranger showing anger at a mild norm violation. We found no effect on ratings of whether the reprimand had the beneficial consequence of changing the violator’s behavior. Our findings provide insight into how peer punishers can avoid negative reputational effects. They also point to the importance of going beyond economic games when studying peer punishment.

In an experimental gift-exchange game, we explore the transformative capacity of vulnerable trust, which we define as trusting untrustworthy players when their untrustworthiness is common knowledge between co-players. In our experiment, there are two treatments: the “Information” treatment and the “No-Information” treatment in which we respectively disclose or not information about trustees’ trustworthiness. Our laboratory evidence consistently supports the transformative capacity of trustors’ vulnerable trust, which generates higher transfers, more trustworthiness and increased reciprocity by untrustworthy trustees.

The results of song contests offer a unique opportunity to analyze possible distortions arising from various biases in performance evaluations using observational data. In this study we investigate the influence of contestants’ order of appearance on their ranking. We found that, in the New Wave Song Contest, expert judgments were significantly influenced by the contestant’s running number, an exogenous factor that, being assigned randomly, clearly did not influence the output quality. We also found weaker statistical evidence of such an ordering effect in Eurovision Song Contest finals of 2009–2012.