People model other people’s mental states in order to understand and predict their behavior. Sometimes they model what others think about them as well: “He thinks that I intend to stop.” Such second-order theory of mind is needed to navigate some social situations, for example, to make optimal decisions in turn-taking games. Adults sometimes find this very difficult. Sometimes they make decisions that do not fit their predictions about the other player. However, the main bottleneck for decision makers is to take a second-order perspective required to make a correct opponent model. We report a methodical investigation into supporting factors that help adults do better. We presented subjects with two-player, three-turn games in which optimal decisions required second-order theory of mind (Hedden and Zhang, 2002). We applied three “scaffolds” that, theoretically, should facilitate second-order perspective-taking: 1) stepwise training, from simple one-person games to games requiring second-order theory of mind; 2) prompting subjects to predict the opponent’s next decision before making their own decision; and 3) a realistic visual task representation. The performance of subjects in the eight resulting combinations shows that stepwise training, but not the other two scaffolds, improves subjects’ second-order opponent models and thereby their own decisions.

The present study was conducted to validate the Chinese version of the Adult Decision-Making Competence scale. 364 college students were recruited from four universities in China. The results indicate the Chinese Adult Decision-Making Competence subscales have good internal consistency and the two-factor structure in the study of Bruine de Bruin et al. (2007) was confirmed. Gender differences were found in Resistance to Sunk Cost. Differences of Applying Decision Rules and Consistency in Risk Perception were found between participants with different education background. Overall, the Chinese Adult Decision-Making Competence scale is validated in China.

Risk behavior can be capricious and may vary from month to month. We study 62 clients of a private bank in Northern Italy. The individuals are of special interest for several reasons. As active traders, they manage the value-at-risk (VaR) of a portion of their wealth portfolios. In addition, they act alone, i.e., without input from a financial adviser. Based on VaR-statistics, we find that, in general, the subjects become more risk-averse after suffering losses and more risk-seeking after experiencing gains. The monthly gains and losses that alter investor risk behavior represent true changes in wealth but are “on paper” only, i.e., not immediately realized. Our results allow several interpretations, but they are not at odds with a house money effect, or the possibility that overconfident investors trade on illusions. Rapidly shifting risk behavior in fast response to unstable circumstances weakens individual risk tolerance as a deep parameter and key construct of finance theory.

Recent evidence suggests that Americans’ beliefs about upward mobility are overly optimistic. Davidai & Gilovich (2015a), Kraus & Tan (2015), and Kraus (2015) all found that people overestimate the likelihood that a person might rise up the economic ladder, and underestimate the likelihood that they might fail to do so. However, using a different methodology, Chambers, Swan and Heesacker (2015) reported that Americans’ beliefs about mobility are much more pessimistic. Swan, Chambers, Heesacker and Nero (2017) provide a much-needed summary of these conflicting findings and question the utility of measuring population-level biases in judgments of inequality and mobility. We value their summary but argue that their conclusion is premature. By focusing on measures that best tap how laypeople naturally think about the distribution of income, we believe that researchers can draw meaningful conclusions about the public’s perceptions of economic mobility. When more ecologically representative measures are used, the consistent finding is that Americans overestimate the extent of upward mobility in the United States. To explain the divergent findings in the literature, we provide evidence that the methods used by Chambers et al. (2015) inadvertently primed participants to think about immobility rather than mobility. Finally, using a novel method to examine beliefs about economic mobility, we show that Americans indeed overestimate the degree of mobility in the United States.

Decades of experimental research show that some people forgo personal gains to benefit others in unilateral anonymous interactions. To explain these results, behavioral economists typically assume that people have social preferences for minimizing inequality and/or maximizing efficiency (social welfare). Here we present data that cannot be explained by these standard social preference models. We use a “Trade-Off Game” (TOG), where players unilaterally choose between an equitable option and an efficient option. We show that simply changing the labelling of the options to describe the equitable versus efficient option as morally right completely reverses the correlation between behavior in the TOG and play in a separate Dictator Game (DG) or Prisoner’s Dilemma (PD): people who take the action framed as moral in the TOG, be it equitable or efficient, are much more prosocial in the DG and PD. Rather than preferences for equity and/or efficiency per se, our results suggest that prosociality in games such as the DG and PD are driven by a generalized morality preference that motivates people to do what they think is morally right.

This paper presents the results of a study that sheds new light on the shape of indifference curves in the Marschak-Machina triangle. The most important observation, obtained non-parametrically, concerns jumps in indifference curves at the triangle legs towards the triangle origin. These jumps, however, do not appear at the hypotenuse. The pattern observed suggests discontinuity in lottery valuation when the range of lottery outcomes changes and is best explained by decision-making models based on the psychological phenomenon of range dependence (Parducci, 1965; Cohen, 1992; Kontek & Lewandowski, 2018). Models founded on other psychological phenomena, e.g., discontinuity in decision weights (Kahneman & Tversky, 1979), cumulative probability weighting (Tversky & Kahneman, 1992), attention shifting (Birnbaum, 2008), overweighting of salient payoffs (Bordallo, Gennaioli & Shefrin, 2012), and treating stated probabilities as imperfect information (Viscusi, 1989), predict indifference curve shapes that differ from the one obtained in this study.

Riddles can teach us psychology when we stop to consider the psychological principles that make them “work”. This paper studies a particular class of riddles that we call stumpers, and provides analysis of the various principles (some familiar, some novel) that inhibit most people from finding the correct solution – or any solution – even though they find the answers obvious ex post. We restrict our analysis to four stumpers, propose the psychological antecedents of each, and provide experimental support for our conjectures.

A routine part of intelligence analysis is judging the probability of alternative hypotheses given available evidence. Intelligence organizations advise analysts to use intelligence-tradecraft methods such as Analysis of Competing Hypotheses (ACH) to improve judgment, but such methods have not been rigorously tested. We compared the evidence evaluation and judgment accuracy of a group of intelligence analysts who were recently trained in ACH and then used it on a probability judgment task to another group of analysts from the same cohort that were neither trained in ACH nor asked to use any specific method. Although the ACH group assessed information usefulness better than the control group, the control group was a little more accurate (and coherent) than the ACH group. Both groups, however, exhibited suboptimal judgment and were susceptible to unpacking effects. Although ACH failed to improve accuracy, we found that recalibration and aggregation methods substantially improved accuracy. Specifically, mean absolute error (MAE) in analysts’ probability judgments decreased by 61% after first coherentizing their judgments (a process that ensures judgments respect the unitarity axiom) and then aggregating their judgments. The findings cast doubt on the efficacy of ACH, and show the promise of statistical methods for boosting judgment quality in intelligence and other organizations that routinely produce expert judgments.

Davidai and Gilovich (2018) contend that (a) Americans tend to think about their nation’s income distribution in terms of quintiles (fifths), and (b) when Americans’ perceptions of socio-economic mobility rates are measured properly (e.g., by asking online survey respondents to guess upward-mobility rates across quintiles), a trend of overestimation (too much optimism concerning the number of people who manage to transcend poverty) will emerge. In this reply, we hail Davidai and Gilovich’s new data as novel, important, and relevant to the former (a), but we doubt that they can support the latter (b) claim about population-level (in)accuracy. Namely, we note that even if mobility-rate perceptions could be measured perfectly, inferences about the accuracy of those perceptions still depend on a particular comparator—a point-estimate of the "true" rate of upward social mobility in the U.S. against which survey respondents’ guesses are evaluated—that is itself an error-prone estimate. Applying different established comparators to survey respondents’ guesses changes both the direction and magnitude of previously observed overestimation effects. We conclude with a challenge: researchers who wish to compute the average distance between socio-economic perceptions and socio-economic reality must first select and justify a fair comparator.

When faced with a decision, people collect information to help them decide. Though it may seem unnecessary, people often continue to search for information about alternatives after they have already chosen an option, even if this choice is irreversible (e.g., checking out other cars after just purchasing one). While previous post-decision search studies focused on “one-shot” decisions and highlighted its irrational aspects, here we explore the possible benefits of post-decision search in the long run. We use a simple search task in which participants repeatedly decide whether to select the current alternative or continue to search for a better alternative. In a preliminary study we find that participants indeed conduct post-decision search even in unique environments, where information about forgone options cannot be used in future choices. In the main studies exposure to post-decision information was manipulated directly in unique environments, and was found to lead to better performance. The source of the observed improvement was further investigated with an explicit strategy elicitation methodology. We find that following exposure to post-decision information, people collect more data before generating thresholds. Thus, although post-decision search in unique environments might appear redundant, our results suggest it can help decision makers to modify their strategy and improve their future choices.

Birnbaum and Quispe-Torreblanca (2018) evaluated a set of six models developed under true-and-error theory against data in which people made choices in repeated gambles. They concluded the three models based on expected utility theory were inadequate accounts of the behavioral data, and argued in favor of the simplest of the remaining three more general models. To reach these conclusions, they used non-Bayesian statistical methods: frequentist point estimation of parameters, bootstrapped confidence intervals of parameters, and null hypothesis significance testing of models. We address the same research goals, based on the same models and the same data, using Bayesian methods. We implement the models as graphical models in JAGS to allow for computational Bayesian analysis. Our results are based on posterior distribution of parameters, posterior predictive checks of descriptive adequacy, and Bayes factors for model comparison. We compare the Bayesian results with those of Birnbaum and Quispe-Torreblanca (2018). We conclude that, while the very general conclusions of the two approaches agree, the Bayesian approach offers better detailed answers, especially for the key question of the evidence the data provide for and against the competing models. Finally, we discuss the conceptual and practical advantages of using Bayesian methods in judgment and decision making research highlighted by this case study.

We developed the Short Maximization Inventory (SMI) by shortening the Maximization Inventory (Turner, Rim, Betz & Nygren, 2012) from 34 items to 15 items. Using the Item Response Theory framework, we identified and removed the items of the Maximization Inventory that contributed least to the performance of the original scale. The construct validity assessed for SMI is similar to the full MI and is in line with the predictions from the literature: the Satisficing subscale is positively related to the indices of well-being, while the Decision Difficulty and Alternative Search subscales are negatively related to well-being. The new scale retains the good psychometric properties of the original scale. Furthermore, its brevity allows researchers to use the scale in studies in which maximization is not the primary focus. Although the SMI lacks the “High Standards” subscale, as did the original MI, we believe that SMI is a step towards developing a reliable and conceptually sound measure of maximizing that can be used in various research designs.

Scott, Inbar and Rozin (2016) reported an association between greater disgust sensitivity (DS) and more negative attitudes towards genetically modified (GM) food. Royzman, Cusimano and Leeman (2017) replicated this association but argued that fear, not disgust, is the primary emotion associated with negative GM food attitudes. We believe that this conclusion is premature, because the “high-granularity” disgust scale variants on which it is based are theoretically and statistically questionable. We explain the issues with these disgust scale variants and report direct evidence showing that people respond to GM food with disgust more than fear.

The present study investigates how group-cooperation heuristics boost voluntary contributions in a repeated public goods game. We manipulate two separate factors in a two-person public goods game: i) group composition (Selfish Subjects vs. Conditional Cooperators) and ii) common knowledge about group composition (Information vs. No Information). In addition, we let the subjects signal expectations of the other’s contributions in the experiment’s second phase. Common knowledge of Selfish type alone slightly dampens contributions but dramatically increases contributions when signaling of expectations is allowed. The results suggest that group-cooperation heuristics are triggered when two factors are jointly salient to the agent: (i) that there is no one to free-ride on; and (ii) that the other wants to cooperate because of (i). We highlight the potential effectiveness of group-cooperation heuristics and propose solution thinking as the schema of reasoning underlying the heuristics. The high correlation between expectations and actual contributions is compatible with the existence of default preference to satisfy others’ expectations (or to avoid disappointing them), but the stark end-game effect suggests that group-cooperation heuristics, at least among selfish players, function ultimately to benefit material self-interest rather than to just please others.

Scott, Inbar and Rozin (2016) presented evidence that trait disgust predicts opposition to genetically modified food (GMF). Royzman, Cusimano, and Leeman (2017) argued that these authors did not appropriately measure trait disgust (disgust qua oral inhibition or OI) and that, once appropriately measured, the hypothesized association between disgust and GMF attitudes was not present. In their commentary, Inbar and Scott (2018) challenge our conclusions in several ways. In this response, we defend our conclusions by showing (a) that OI is psychometrically distinct from other affective categories, (b) that OI is widely held to be the criterial feature of disgust and (c) that we were well-justified to pair OI with the pathogen-linked vignettes that we used. Furthermore, we extend our critique to the new findings presented by Inbar and Scott (2018); we show that worry and suspicion (not disgust) are the dominant affective states one is likely to experience while thinking about GMF and that the true prevalence of disgust is about zero. We conclude by underscoring that the present argument and findings are a part of a larger body of evidence challenging any causal effect of disgust on morality.