As the adage goes, “money makes the world go round” – but which direction does it spin? This analysis considers how basic decision research can help us work out how to answer this question. It suggests that the difficulty of deriving clear predictions based on existing decision research is at least partly rooted in two restrictive conventions. The first is the focus on deviations from rational choice, and the effort to capture observed deviations by assuming subjective value functions. While it is difficult to reject the hypothesis that choice behavior reflects the weighting of subjective values, it is not clear that it advances the derivation of useful predictions. A second restrictive convention is the focus on objective hypothesis testing, which favors analyses that evaluate small refinements of the popular models. The potential benefits of relaxing these conventions are considered, with reference to recent choice prediction competitions that facilitate the exploration of distinct assumptions and model development techniques. The winners in these competitions assume very different decision processes than those assumed by the popular “subjective functions” models. The relationship of the results to the big data revolution is discussed.

Prescriptive rules guide human behavior across various domains of community life, including law, morality, and etiquette. What, specifically, are rules in the eyes of their subjects, i.e., those who are expected to abide by them? Over the last sixty years, theorists in the philosophy of law have offered a useful framework with which to consider this question. Some, following H. L. A. Hart, argue that a rule’s text at least sometimes suffices to determine whether the rule itself covers a case. Others, in the spirit of Lon Fuller, believe that there is no way to understand a rule without invoking its purpose — the benevolent ends which it is meant to advance. In this paper we ask whether people associate rules with their textual formulation or their underlying purpose. We find that both text and purpose guide people’s reasoning about the scope of a rule. Overall, a rule’s text more strongly contributed to rule infraction decisions than did its purpose. The balance of these considerations, however, varied across experimental conditions: In conditions favoring a spontaneous judgment, rule interpretation was affected by moral purposes, whereas analytic conditions resulted in a greater adherence to textual interpretations. In sum, our findings suggest that the philosophical debate between textualism and purposivism partly reflects two broader approaches to normative reasoning that vary within and across individuals.

Most prior research on the external validity of mixed-motive games has studied only one single game version and/or one specific type of real-life prosocial behavior. The present study employs a different approach. We used multiple game trials — with different payoff structures — to measure participants’ behavior in the Prisoner’s Dilemma, the Commons Dilemma, and the Public Goods Dilemma. We then examined the associations between these aggregated game behaviors and a wide set of self-reported prosocial behaviors such as donations, commuting, and environmental behaviors. We also related these prosocial behavior measures to a dispositional measure of prosociality, social value orientation. We report evidence that the weak statistical relationships routinely observed in prior studies are at least partially a consequence of failures to aggregate. More specifically, our results show that aggregation over multiple game trials was especially effective for the Prisoner’s Dilemma, whereas it was somewhat effective for the Public Goods Dilemma. Yet, aggregation on the side of the prosocial behaviors was effective for both these games, as well as for social value orientation. The Commons Dilemma, however, turned out to yield invariably poor relationships with prosocial behavior, regardless of the level of aggregation. Based on these findings, we conclude that the use of multiple instances of game behavior and prosocial behavior is preferable to the use of only a single measurement.

We conduct a laboratory experiment to investigate whether the rebate and matching subsidy schemes cause crowding-out or crowding-in effects (reductions or increases in amount donated) on individual net donations. We find that when the rebate subsidy scheme is implemented, it does not result in crowding-out or crowding-in effects on individual net donations. However, when the matching subsidy scheme is implemented, it encourages individuals to donate more and generates crowding-in effects on individual net donations.

Do people cheat more when they have something to gain, or when they have something to lose? The answer to this question isn’t straightforward, as research is mixed when it comes to understanding how unethical people will be when they might acquire something good versus avoid something bad. To wit, research has found that people cheat more in a loss (vs. gain) frame, yet research on regulatory focus has found that people cheat more in a promotion focus (where the focus is on acquiring gains) than in a prevention focus (where the focus is on avoiding losses). Through a large-scale field study containing 332,239 observations including 27,350 transgressions, we address the contradictory results of gain/loss frames and regulatory focus on committing unethical behavior in a context that contains a high risk of detecting unethical behavior (NFL football games). Our results replicated the separate effects of more cheating in a loss frame, and more cheating in a promotion focus. Furthermore, our data revealed a heretofore undocumented crossover interaction, in accordance with regulatory fit, which could disentangle past results: Specifically, we found promotion focus is associated with more cheating in a loss (vs. gain) frame, whereas prevention focus is associated with more cheating in a gain (vs. loss) frame. In gridiron football, this translates to offensive players fouling more when their team is losing (vs. winning) and defensive players fouling more when their team is winning (vs. losing).

We propose a novel experimental design aimed at investigating whether inducing individuals to use certain choice procedures has an effect on the outcome of their decision. Specifically, by implementing a modification of the mouse-tracing method, we induce subjects to use either alternative-based or characteristic-based search procedures in a between-subject lottery-choice experiment. We find that encouraging subjects to search by characteristic systematically makes them choose riskier options. Consistently with existing literature, our evidence indicates that individuals typically look up information within alternatives. However, when induced to search by characteristic, high prizes receive more attention, leading individuals to switch to non-compensatory heuristics and – consequently – make riskier choices. Our findings are robust to variations in the complexity of the choice problem and individual differences in risk-attitudes, CRT scores, and gender.

Preferences about delayed rewards and preferences about risk are central to the literature on decision making. Several proposals suggest that such preferences arise from a single process and thus predict strong associations between preferences about delay and risk. Although there is a wealth of data on this association, the evidence is inconclusive; some studies have reported significant associations but many have not. Consequently, it is unclear whether the association between delay preferences and risk preferences is strong enough to support single-process theories. To further explore this question, we took a meta-analytic approach surveying 26 studies totaling 32 effect sizes. Results reveal a small to moderate association between risk preferences and delay preferences. This result provides little support for existing proposals because the observed relationship is no stronger than associations observed between either delay preferences or risk preferences and other variables. Moderating variables provide some explanation for inconsistencies across studies. Implications, including the apparent discrepancy between this literature and the conventional construct of impulsivity, are also discussed.

Pseudo-profound bullshit receptivity is the tendency to perceive meaning in important-sounding, nonsense statements. To understand how bullshit receptivity differs across domains, we develop a scale to measure scientific bullshit receptivity — the tendency to perceive truthfulness in nonsensical scientific statements. Across three studies (total N = 1,948), scientific bullshit receptivity was positively correlated with pseudo-profound bullshit receptivity. Both types of bullshit receptivity were positively correlated with belief in science, conservative political beliefs, and faith in intuition. However, compared to pseudo-profound bullshit receptivity, scientific bullshit receptivity was more strongly correlated with belief in science, and less strongly correlated with conservative political beliefs and faith in intuition. Finally, scientific literacy moderated the relationship the two types of bullshit receptivity; the correlation between the two types of receptivity was weaker for individuals scoring high in scientific literacy.

We present evidence from a pre-registered experiment indicating that a philosophical argument – a type of rational appeal – can persuade people to make charitable donations. The rational appeal we used follows Singer’s “shallow pond” argument (1972), while incorporating an evolutionary debunking argument (Paxton, Ungar and Greene, 2012) against favoring nearby victims over distant ones. The effectiveness of this rational appeal did not differ significantly from that of a well-tested emotional appeal involving an image of a single child in need (Small, Loewenstein and Slovic, 2007). This is a surprising result, given evidence that emotions are the primary drivers of moral action, a view that has been very influential in the work of development organizations. We found no support for our hypothesis that combining our rational and emotional appeals would have a stronger effect than either appeal in isolation. However, our finding that both kinds of appeal can increase charitable donations is cause for optimism, especially concerning the potential efficacy of well-designed rational appeals. We consider the significance of these findings for moral psychology, ethics, and the work of organizations aiming to alleviate severe poverty.

Gawronski, Armstrong, Conway, Friesdorf and Hütter (2017, GACFH) presented a model of choices in utilitarian moral dilemmas, those in which following a moral principle or norm (the deontological response) leads to worse consequences than violating the principle (the utilitarian response). In standard utilitarian dilemmas, the utilitarian option involves action (which causes some harm in order to prevent greater harm), and the deontological response, omission. GACFH propose that responses in such dilemmas arise in three different ways: a psychological process leading to a deontological choice, a different process leading to a utilitarian choice, or a bias toward inaction or action. GACFH attempt to separate these three processes with new dilemmas in which action and omission are switched, and dilemmas in which the utilitarian and deontological processes lead to the same choice. They conclude that utilitarian and deontological responses are indeed separable, and that past research has missed this fact by treating them as naturally opposed. We argue that a bias toward harmful inaction is best understood as an explanation of deontological responding rather than as an alternative process. It thus should be included as an explanation of deontological responding, not an alternative response type. We also argue that GACFH’s results can be largely explained in terms of subjects’ unwillingness to accept the researchers’ assumptions about which consequence is worse and which course of action is consistent with a moral norm. This problem is almost inherent in the attempt to switch act and omission while maintaining equivalent norms. We support this argument with data from experiments with new and old scenarios, in which we asked subjects to judge both norms and consequences. We also find that GACFH’s results are not as consistent as they appear to be in the paper.

We call attention to an important, but overlooked finding in research reported by Longoni, Bonezzi and Morewedge (2019). Longoni et al. claim that people always prefer a human to an artificially intelligent (AI) medical provider. We show that this was only the case when the historical performance of the human and AI providers was equal.  When the AI is known to outperform the human, their data showed a clear preference for the automated provider. We provide additional statistical analyses of their data to support this claim.

In Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and artificial intelligence providers. Pezzo and Beckstead’s (2020) commentary asks whether resistance to medical AI takes the form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always reject healthcare provided by AI, and our article makes no claim that they do.

We clarify two points made in our commentary (Pezzo & Beckstead, 2020, this issue) on a recent paper by Longoni, Bonezzi, and Morewedge (2019). In both Experiments 1 and 4 from their paper, it is not possible to determine whether accuracy can compensate for algorithm aversion. Experiments 3A-C, however, do show a strong effect of accuracy such that AI that is superior to a human provider is embraced by patients. Many papers, including Longoni et al. tend to minimize the role of this compensatory process, apparently because it seems obvious to the authors (Longoni, Bonezzi, Morewedge, 2020, this issue). Such minimization, however, can lead to (mis)citations in which research that clearly demonstrates a compensatory role of AI accuracy is cited as non-compensatory.