Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs.

Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity-variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity-estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs-indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.

Mating motives are neither necessary nor sufficient to create the beauty premium.

Mating motives lead decision makers to favor attractive people, but this favoritism is not sufficient to create a beauty premium in competitive settings. Further, economic approaches to discrimination, when correctly characterized, could neatly accommodate the experimental and field evidence of a beauty premium. Connecting labor economics and evolutionary psychology is laudable, but mating motives do not explain the beauty premium.

The transmission game: Testing behavioral interventions in a pandemic-like simulation.

During pandemics, effective nonpharmaceutical interventions encourage people to adjust their behavior in fast-changing environments in which exponential dynamics aggravate the conflict between the individual benefits of risk-taking and its social costs. Policy-makers need to know which interventions are most likely to promote socially advantageous behaviors. We designed a tool for initial evaluations of the effectiveness of large-scale interventions, the transmission game framework, which integrates simulations of outbreak dynamics into large-group experiments with monetary stakes. In two studies (n = 700), we found substantial differences in the effectiveness of five behavioral interventions. A simple injunctive-norms message proved most effective, followed by two interventions boosting participants' ability to anticipate the consequences of risky behavior. Interventions featuring descriptive norms or concurrent risk information failed to reduce risk-taking.

Toward an ecological analysis of Bayesian inferences: how task characteristics influence responses.

In research on Bayesian inferences, the specific tasks, with their narratives and characteristics, are typically seen as exchangeable vehicles that merely transport the structure of the problem to research participants. In the present paper, we explore whether, and possibly how, task characteristics that are usually ignored influence participants' responses in these tasks. We focus on both quantitative dimensions of the tasks, such as their base rates, hit rates, and false-alarm rates, as well as qualitative characteristics, such as whether the task involves a norm violation or not, whether the stakes are high or low, and whether the focus is on the individual case or on the numbers. Using a data set of 19 different tasks presented to 500 different participants who provided a total of 1,773 responses, we analyze these responses in two ways: first, on the level of the numerical estimates themselves, and second, on the level of various response strategies, Bayesian and non-Bayesian, that might have produced the estimates. We identified various contingencies, and most of the task characteristics had an influence on participants' responses. Typically, this influence has been stronger when the numerical information in the tasks was presented in terms of probabilities or percentages, compared to natural frequencies - and this effect cannot be fully explained by a higher proportion of Bayesian responses when natural frequencies were used. One characteristic that did not seem to influence participants' response strategy was the numerical value of the Bayesian solution itself. Our exploratory study is a first step toward an ecological analysis of Bayesian inferences, and highlights new avenues for future research.

Natural frequencies facilitate diagnostic inferences of managers.

In Bayesian inference tasks, information about base rates as well as hit rate and false-alarm rate needs to be integrated according to Bayes' rule after the result of a diagnostic test became known. Numerous studies have found that presenting information in a Bayesian inference task in terms of natural frequencies leads to better performance compared to variants with information presented in terms of probabilities or percentages. Natural frequencies are the tallies in a natural sample in which hit rate and false-alarm rate are not normalized with respect to base rates. The present research replicates the beneficial effect of natural frequencies with four tasks from the domain of management, and with management students as well as experienced executives as participants. The percentage of Bayesian responses was almost twice as high when information was presented in natural frequencies compared to a presentation in terms of percentages. In contrast to most tasks previously studied, the majority of numerical responses were lower than the Bayesian solutions. Having heard of Bayes' rule prior to the study did not affect Bayesian performance. An implication of our work is that textbooks explaining Bayes' rule should teach how to represent information in terms of natural frequencies instead of how to plug probabilities or percentages into a formula.

The impact of affect on willingness-to-pay and desired-set-size.

What role does affect play in economic decision making? Previous research showed that the number of items had a linear effect on the willingness-to-pay for those items when participants were computationally primed, whereas participants' willingness-to-pay was insensitive to the amount when they were affectively primed. We extend this research by also studying the impact of affect on nonmonetary costs of waiting for items to be displayed and of screening them in a computer task. We assessed these costs by asking participants how many items they desired to see before making their selection. In our experiment, the effect of priming on desired-set-size was even larger than on willingness-to-pay, which can be explained by the fact that the nonmonetary costs, waiting time, were real, whereas willingness-to-pay was hypothetical. Participants also reported their satisfaction with the choosing process and the chosen items; no linear or nonlinear relationship was found between the self-determined desired-set-size and satisfaction.