We investigated the extent to which the human capacity for recognition helps to forecast political elections: We compared naïve recognition-based election forecasts computed from convenience samples of citizens’ recognition of party names to (i) standard polling forecasts computed from representative samples of citizens’ voting intentions, and to (ii) simple—and typically very accurate—wisdom-of-crowds-forecasts computed from the same convenience samples of citizens’ aggregated hunches about election results. Results from four major German elections show that mere recognition of party names forecast the parties’ electoral success fairly well. Recognition-based forecasts were most competitive with the other models when forecasting the smaller parties’ success and for small sample sizes. However, wisdom-of-crowds-forecasts outperformed recognition-based forecasts in most cases. It seems that wisdom-of-crowds-forecasts are able to draw on the benefits of recognition while at the same time avoiding its downsides, such as lack of discrimination among very famous parties or recognition caused by factors unrelated to electoral success. Yet it seems that a simple extension of the recognition-based forecasts—asking people what proportion of the population would recognize a party instead of whether they themselves recognize it—is also able to eliminate these downsides.

We aggregated data from 28 studies (total N=13,386) to assess the relationship between individual differences in character strengths, as described by the VIA model of character, and economically-relevant behaviors and cognition. Factor analyzing the character strength inventory responses revealed four factors – Caring, Leadership, Inquisitiveness, and Self-control – each of which correlated with a variety of measures. Caring was associated with the willingness to pay costs to benefit others, as well as reliance on intuitive decision-making; Leadership was associated with inefficient, anti-social behaviors, risk taking, and trusting one’s intuitions while also liking to reason; Inquisitiveness was associated with efficient behaviors in both the social and risk domains, and reliance on deliberative decision-making; and Self-control was associated with delaying gratification, risk aversion, and a reliance on reason. These results help shed light on the relationship between character – and personality more generally – and economic behaviors. In doing so, we give some indication of which types of people will be most successful in which decision-making contexts.

Previous research has demonstrated that consumers’ decisions regarding supplementary pensions could be affected by biases. Bernatzi and Thaler’s experiment demonstrated that menu design can influence pension fund enrollment decisions, in that participants appear to adopt a naïve heuristic, i.e., “extremeness aversion”. Using a database of 27 occupational pension funds from 2007 to 2011, representing 1,732,530 employees, this study asked whether menu design affected Italian workers’ choices regarding the supplementary pension system as a result of the new rules enacted by the regulator in 2007. Most enrolled workers opted for the median investment line. I discuss the possible relevance of this result to public policy, in particular the possibility of including these preferences in the regulations, with the aim of benefiting employees.

Individual true and error theory assumes that responses by the same person to the same choice problem within a block of trials are based on the same true preferences but may show preference reversals due to random error. Between blocks, a person’s true preferences may differ or stay the same. This theory is illustrated with studies testing two critical properties that distinguish models of risky decision making: (1) restricted branch independence, which is implied by original prospect theory and violated in a specific way by both cumulative prospect theory and the priority heuristic; and (2) stochastic dominance, which is implied by cumulative prospect theory. Corrected for random error, most individuals systematically violated stochastic dominance, ruling out cumulative prospect theory. Furthermore, most people violated restricted branch independence in the opposite way predicted by that theory and the priority heuristic. Both violations are consistent with the transfer of attention exchange model. No one was found whose data were compatible with cumulative prospect theory, except for those that were also compatible with expected utility, and no one satisfied the priority heuristic.

The less-is-more effect predicts that people can be more accurate making paired-comparison decisions when they have less knowledge, in the sense that they do not recognize all of the items in the decision domain. The traditional theoretical explanation is that decisions based on recognizing one alternative but not the other can be more accurate than decisions based on partial knowledge of both alternatives. I present new data that directly test for the less-is-more effect, coming from a task in which participants judge which of two cities is larger and indicate whether they recognize each city. A group-level analysis of these data provides evidence in favor of the less-is-more effect: there is strong evidence people make decisions consistent with recognition, and that these decisions are more accurate than those based on knowledge. An individual-level analysis of the same data, however, provides evidence inconsistent with a simple interpretation of the less-is-more effect: there is no evidence for an inverse-U-shaped relationship between accuracy and recognition, and especially no evidence that individuals who recognize a moderate number of cities outperform individuals who recognize many cities. I suggest a reconciliation of these contrasting findings, based on the systematic change of the accuracy of recognition-based decisions with the underlying recognition rate. In particular, the data show that people who recognize almost none or almost all cities make more accurate decisions by applying the recognition heuristic, when compared to the accuracy achieved by people with intermediate recognition rates. The implications of these findings for precisely defining and understanding the less-is-more effect are discussed, as are the constraints our data potentially place on models of the learning and decision-making processes involved. Keywords: recognition heuristic, less-is-more effect.

We study the influence of numerological superstitions on people’s buying behavior in the apartment market using unique actual sales data. Based on the dataset from Saint-Petersburg primary real estate market we compare the share of sold apartments on floor 7 with that on floors 6 and 8, whereas floor 13 is benchmarked to floors 12 and 14. As floor plans are absolutely identical we manage to isolate the effects of the “lucky” and “unlucky” floors. The data we use allows clean identification of superstition effects, while being publicly available. We have found a clear negative effect of the 13th floor on demand for apartments, and a significant effect of preference towards the 7th floor compared to the two neighboring floors. Possible applications of our approach to other areas of consumer research are discussed.

Using a cognitive task (mental calculation) and a perceptual-motor task (stylized golf putting), we examined differential proficiency using the CWS index and several other quantitative measures of performance. The CWS index (Weiss & Shanteau, 2003) is a coherence criterion that looks only at internal properties of the data without incorporating an external standard. In Experiment 1, college students (n = 20) carried out 2- and 3-digit addition and multiplication problems under time pressure. In Experiment 2, experienced golfers (n = 12), also college students, putted toward a target from nine different locations. Within each experiment, we analyzed the same responses using different methods. For the arithmetic tasks, accuracy information (mean absolute deviation from the correct answer, MAD) using a coherence criterion was available; for golf, accuracy information using a correspondence criterion (mean deviation from the target, also MAD) was available. We ranked the performances of the participants according to each measure, then compared the orders using Spearman’s rs. For mental calculation, the CWS order correlated moderately (rs =.46) with that of MAD. However, a different coherence criterion, degree of model fit, did not correlate with either CWS or accuracy. For putting, the ranking generated by CWS correlated .68 with that generated by MAD. Consensual answers were also available for both experiments, and the rankings they generated correlated highly with those of MAD. The coherence vs. correspondence distinction did not map well onto criteria for performance evaluation.

Recent research reported evidence that contradicts cumulative prospect theory and the priority heuristic. The same body of research also violates two editing principles of original prospect theory: cancellation (the principle that people delete any attribute that is the same in both alternatives before deciding between them) and combination (the principle that people combine branches leading to the same consequence by adding their probabilities). This study was designed to replicate previous results and to test whether the violations of cumulative prospect theory might be eliminated or reduced by using formats for presentation of risky gambles in which cancellation and combination could be facilitated visually. Contrary to the idea that decision behavior contradicting cumulative prospect theory and the priority heuristic would be altered by use of these formats, however, data with two new graphical formats as well as fresh replication data continued to show the patterns of evidence that violate cumulative prospect theory, the priority heuristic, and the editing principles of combination and cancellation. Systematic violations of restricted branch independence also contradicted predictions of “stripped” prospect theory (subjectively weighted additive utility without the editing rules).

Online, social media communication is often ambiguous, and it can encourage speed and inattentiveness. We investigated whether Actively Open Minded Thinking (AOT), a dispositional willingness to seek out new or potentially threatening information, may help users avoid these pitfalls. In Study 1, we determined that correctly assessing social media authors’ traits was positively predicted by raters’ AOT. In Study 2, we used data-driven methods to devise a three-dimensional picture of online behaviors of people high or low in AOT, finding that AOT is associated with thoughtful, nuanced, idiosyncratic actions and with resisting the typically fast pace of online interactions. AOT may be an important factor in accurate, socially responsible online behavior.

The deliberation-without-attention effect occurs when better decisions are made when people experience a period of distraction before a decision than when they make decisions immediately or when they spend time reflecting on the alternatives. This effect has been explained (e.g., Dijksterhuis, 2004) by the claim that people engage in unconscious deliberation when distracted and that unconscious thought is better suited for complex decisions than conscious thought. Experiments 1, 2A, and 2B in this study included a dominant alternative and failed to find evidence for this effect. Experiment 3 removed the dominant alternative and manipulated mode of thought within-subjects to eliminate alternative explanations for the failed replication. In all experiments participants did not make better decisions after unconscious thought; decisions were consistently better than chance when made immediately after the encoding of information. Encouraging people not to think about complex decisions appears to be unwarranted.

Transitivity is the assumption that if a person prefers A to B and B to C, then that person should prefer A to C. This article explores a paradigm in which Birnbaum, Patton and Lott (1999) thought people might be systematically intransitive. Many undergraduates choose C = ($96, .85; $90, .05; $12, .10) over A = ($96, .9; $14, .05; $12, .05), violating dominance. Perhaps people would detect dominance in simpler choices, such as A versus B = ($96, .9; $12, .10) and B versus C, and yet continue to violate it in the choice between A and C, which would violate transitivity. In this study we apply a true and error model to test intransitive preferences predicted by a partially effective editing mechanism. The results replicated previous findings quite well; however, the true and error model indicated that very few, if any, participants exhibited true intransitive preferences. In addition, violations of stochastic dominance showed a strong and systematic decrease in prevalence over time and violated response independence, thus violating key assumptions of standard random preference models for analysis of transitivity.

This paper examines how observing other people’s behavior affects risk taking in repeated decision tasks. In Study 1, 100 participants performed experience-based decision tasks either alone or in pairs, with the two members being exposed to each others’ choices and outcomes. The tasks involved either equiprobable gains and losses or frequent small gains and rare large losses. The results indicated that, in both risk types, the social exposure increased the proportion of risky selection, but its effect was stronger in the rare-loss condition. In Study 2 the rare-loss task was administered to 32 study participants, with a target individual observing the choices of a paired individual. The results showed that observing others, rather than being observed, led to the pattern of increased risk taking. The findings of the two studies indicate the importance of distinguishing different types of risky situations and shed light on contradictory findings in the literature.

We propose a novel method of using eye-tracking to study strategic decisions. The conventional approach is to hypothesize what eye-patterns should be observed if a given model of decision-making was accurate, and then proceed to verify if this occurs. When such hypothesis specification is difficult a priori, we propose instead to expose subjects to a variant of the original strategic task that should induce processing it in a way consistent with the postulated model. It is then possible to use machine learning pattern recognition techniques to check if the associated eye-patterns are similar to those recorded during the original task. We illustrate the method using simple examples of 2x2 matching-pennies and coordination games with or without feedback about the counterparts’ past moves. We discuss the strengths and limitations of the method in this context.

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.

The calibration of probability or confidence judgments concerns the association between the judgments and some estimate of the correct probabilities of events. Researchers rely on estimates using relative frequencies computed by aggregating data over observations. We show that this approach creates conceptual problems, and may result in the confounding of explanatory variables or unstable estimates. To circumvent these problems we propose using probability estimates obtained from statistical models—specifically mixed models for binary data—in the analysis of calibration. We illustrate this methodology by re-analyzing data from a published study and comparing the results from this approach to those based on relative frequencies. The model-based estimates avoid problems with confounding variables and provided more precise estimates, resulting in better inferences.

One major statistical and methodological challenge in Judgment and Decision Making research is the reliable identification of individual decision strategies by selection of diagnostic tasks, that is, tasks for which predictions of the strategies differ sufficiently. The more strategies are considered, and the larger the number of dependent measures simultaneously taken into account in strategy classification (e.g., choices, decision time, confidence ratings; Glöckner, 2009), the more complex the selection of the most diagnostic tasks becomes. We suggest the Euclidian Diagnostic Task Selection (EDTS) method as a standardized solution for the problem. According to EDTS, experimental tasks are selected that maximize the average difference between strategy predictions for any multidimensional prediction space. In a comprehensive model recovery simulation, we evaluate and quantify the influence of diagnostic task selection on identification rates in strategy classification. Strategy classification with EDTS shows superior performance in comparison to less diagnostic task selection algorithms such as representative sampling. The advantage of EDTS is particularly large if only few dependent measures are considered. We also provide an easy-to-use function in the free software package R that allows generating predictions for the most commonly considered strategies for a specified set of tasks and evaluating the diagnosticity of those tasks via EDTS; thus, to apply EDTS, no prior programming knowledge is necessary.

Four laboratory studies were conducted to test the hypothesis that correct Bayesian reasoning can be predicted by two factors of task complexity — the number of mental steps required to reach the normative solution, and the compatibility between the framing of data presented and the framing of the question posed. The findings show that participants performed better on frequency format questions only when one mental step was required to solve the task and when the data were in a compatible frequency format. By contrast, participants performed more poorly on more complicated tasks which required more mental steps (in a compatible frequency or probability format) or when the data and question formats were incompatible (Studies 1 and 2). Incompatibility between data and question formats was also associated with higher reaction times (Study 2b). Furthermore, on problems that incorporated incompatibility between the data sample size and the target (question) sample size, participants performed better on the probability question than the frequency question, regardless of data format (Study 3). The latter findings highlight the ecological advantage of translating data into probability terms, which are normalized in a range between 0 and 1, and thus can be transferred from one situation to another.

Is prosociality parochial or universalist? To shed light on this issue, we examine the relationship between the amount of money given to a stranger (giving in an incentivized Dictator Game) and intergroup attitudes and behavior in the context of randomly assigned teams (a minimal group paradigm) among N = 4,846 Amazon Mechanical Turk workers. Using a set of Dynamic Identity Diffusion Index measures, we find that participants who give more in the Dictator Game show less preferential identification with their team relative to the other team, and more identification with all participants regardless of team. Furthermore, in an incentivized Voter Game, participants who give more in the Dictator Game are more likely to support compromise by voting for the opposing team in order to avoid deadlock. Together, these results suggest that – at least in this subject pool and using these measures – prosociality is better characterized by universalism than parochialism.

The classic preference reversal phenomenon, where monetary evaluations contradict risky choices, has been argued to arise due to a focus on outcomes during the evaluation of alternatives, leading to overpricing of long-shot options. Such an explanation makes the implicit assumption that attentional shifts drive the phenomenon. We conducted an eye-tracking study to causally test this hypothesis by comparing a treatment based on cardinal, monetary evaluations with a different treatment avoiding a monetary frame. We find a significant treatment effect in the form of a shift in attention toward outcomes (relative to probabilities) when evaluations are monetary. Our evidence suggests that attentional shifts resulting from the monetary frame of evaluations are a driver of preference reversals.

Individuals have been shown to adaptively select decision strategies depending on the environment structure. Two experiments extended this research to the group level. Subjects (N = 240) worked either individually or in two-person groups, or dyads, on a multi-attribute paired-comparison task. They were randomly assigned to two different environments that favored one of two prototypical decision strategies—weighted additive or take-the-best (between-subjects design in Experiment 1 and within-subject design in Experiment 2). Performance measures revealed that both individuals and dyads learned to adapt over time. A higher starting and overall performance rate in the environment in which weighted additive performed best led to the conclusion that weighted additive served as a default strategy. When this default strategy had to be replaced, because the environment structure favored take-the-best, the superior adaptive capacity of dyads became observable in the form of a steeper learning rate. Analyses of nominal dyads indicate that real dyads performed at the level of the best individuals. Fine-grained analyses of information-search data are presented. Results thus point to the strong moderating role of the environment structure when comparing individual with group performance and are discussed within the framework of adaptive strategy selection.