Recently, social science research replicability has received close examination, with discussions revolving around the degree of success in replicating experimental results. We lend insight to the replication discussion by examining the quality of replication studies. We examine how even a seemingly minor protocol deviation in the experimental process (Camerer et al. in Science 351(6280):143–1436, 2016. https://doi.org/10.1126/science.aaf0918), the removal of common information, can lead to a finding of “non-replication” of the results from the original study (Chen and Chen in Am Econ Rev 101(6):2562–2589, 2011). Our analysis of the data from the original study, its replication, and a series of new experiments shows that, with common information, we obtain the original result in Chen and Chen (2011), whereas without common information, we obtain the null result in Camerer et al. (2016). Together, we use our findings to propose a set of procedure recommendations to increase the quality of replications of laboratory experiments in the social sciences.

Bayes’ statistical rule remains the status quo for modeling belief updating in both normative and descriptive models of behavior under uncertainty. Some recent research has questioned the use of Bayes’ rule in descriptive models of behavior, presenting evidence that people overweight ‘good news’ relative to ‘bad news’ when updating ego-relevant beliefs. In this paper, we present experimental evidence testing whether this ‘good-news, bad-news’ effect is present in a financial decision making context (i.e. a domain that is important for understanding much economic decision making). We find no evidence of asymmetric updating in this domain. In contrast, in our experiment, belief updating is close to the Bayesian benchmark on average. However, we show that this average behavior masks substantial heterogeneity in individual updating behavior. We find no evidence in support of a sizeable subgroup of asymmetric updators.

Welfare-enhancing policies such as congestion pricing are argued to improve efficiency in situations with externalities. Unfamiliarity and lack of any personal experience with such policies, however, can hinder their implementation; particularly the ex-ante uncertainties of incidences of gains and losses as well as debates regarding equity concerns and how to recycle revenues often stymies implementation. This paper employs a laboratory experiment with heterogeneous users to investigate the effectiveness and acceptability of a toll in a six-player-two-route congestion game. To measure acceptability and how it is affected by experience with the toll, we conduct referenda before, during, and after subjects experience a congestion problem and a toll. The experiment employs a $2\times 2$ design that varies two treatments: the rate of revenue reallocation and the level of information before the final vote. After an experiential learning phase, congestion pricing is found to curb congestion effectively, and although some subjects do not vote in their monetary self-interest initially, the majority does so after experiencing the congestion pricing policy. Data on worldviews and beliefs are collected and matched to voting behavior to examine the evolution of how experience determines acceptability. Some worldviews and beliefs can predict voting behavior and the timing of when an individual finds a toll (un)acceptable.

This paper investigates the usefulness of non-choice data, namely response times, as a predictor of threshold behavior in a simple global game experiment. Our results indicate that the signals associated to the highest or second highest response time at the beginning of the experiment are both unbiased estimates of the threshold employed by subjects at the end of the experiment. This predictive ability is lost when we move to the third or higher response times. Moreover, the response time predictions are better than the equilibrium predictions of the game. They are also robust, in the sense that they characterize behavior in an “out-of-treatment” exercise where we use the strategy method to elicit thresholds.

Public reputation mechanisms are an effective means to limit opportunistic behavior in markets suffering from moral hazard problems. While previous research was mostly concerned with the influence of exogenous feedback mechanisms, this study considers the endogenous emergence of reputation through deliberate information sharing among actors and the role of barriers in hindering information exchange. Using a repeated investment game, we analyze the effects of competition and transfer costs on players’ willingness to share information with each other. While transfer costs are a direct cost of the information exchange, competition costs represent an indirect cost that arises when the transfer of valuable information to competitors comes at the loss of a competitive advantage. We show that barriers to information exchange not only affect the behavior of the senders of information, but also affect the ones about whom the information is shared. While the possibility of sharing information about others significantly improves trust and market efficiency, both competition and direct transfer costs diminish the positive effect by substantially reducing the level of information exchange. Players about whom the information is shared anticipate and react to the changes in the costs by behaving more or less cooperatively. For reputation building, an environment is needed that fosters the sharing of information. Reciprocity is key to understanding information exchange. Even when it is costly, information sharing is used as a way to sanction others.

We conduct a lab experiment to investigate an important corporate prediction market setting: A manager needs information about the state of a project, which workers have, in order to make a state-dependent decision. Workers can potentially reveal this information by trading in a corporate prediction market. We test two different market designs to determine which provides more information to the manager and leads to better decisions. We also investigate the effect of top-down advice from the market designer to participants on how the prediction market is intended to function. Our results show that the theoretically superior market design performs worse in the lab—in terms of manager decisions—without top-down advice. With advice, manager decisions improve and both market designs perform similarly well, although the theoretically superior market design features less mis-pricing. We provide a behavioral explanation for the failure of the theoretical predictions and discuss implications for corporate prediction markets in the field.

We present an interactive eye-tracking study that explores the strategic use of gaze. We analyze gaze behavior in an experiment with four simple games. The game can either be a competitive (hide & seek) game in which players want to be unpredictable, or a game of common interest in which players want to be predictable. Gaze is transmitted either in real time to another subject, or it is not transmitted and therefore non-strategic. We find that subjects are able to interpret non-strategic gaze, obtaining substantially higher payoffs than subjects who do not see gaze. If gaze is transmitted in real time, gaze becomes more informative in the common interest games and players predominantly succeed to coordinate on efficient outcomes. In contrast, gaze becomes less informative in the competitive game.

Despite public health efforts, uptake of preventive health technologies remains low in many settings. In this paper, we develop a formal model of prevention and test it through a laboratory experiment. In the model, rational agents decide whether to take up health technologies that reduce, but do not eliminate the risk of adverse health events. As long as agents are sufficiently risk averse and priors are diffuse, we show that initial uptake of effective technologies will be limited. Over time, the model predicts that take-up will decline as users with negative experiences revise their effectiveness priors towards zero. In our laboratory experiments, we find initial uptake rates between 65 and 80% for effective technologies with substantial declines over time, consistent with the model’s predictions. We also find evidence of decision-making not consistent with our model: subjects respond most strongly to the most recent health outcomes, and react to negative health outcomes by increasing their willingness to invest in prevention, even when health risks without prevention are known by all subjects. Our findings suggest that high uptake of preventive technologies should only be expected if the risk of adverse health outcomes without prevention is high, or if preventive technologies are so effective that the risk of adverse outcomes is negligible with prevention.

In many search environments, searchers are learning about the distribution of offers in the market. I conduct an experiment exploring a broad class of search problems with learning about the distribution of payoffs. My results support the prediction that learning results in declining reservation values, providing evidence that learning may be an explanation for recall. Theory predicts a “one step” reservation value strategy, but many subjects instead choose to set a high reservation value in order to learn about the distribution before adjusting based on their observations. Under-searching in search experiments may stem from a reinforcement heuristic and lack of negative feedback after using sub-optimal strategies.

Charitable donations provide positive externalities and can potentially be increased with an understanding of donor preferences. We obtain a uniquely comprehensive characterization of donation motives using an experiment that varies treatments between and within subjects. Donations are increasing in peers’ donations and past subjects’ donations. These and other results suggest a model of heterogeneous beliefs about the social norm for giving. Estimation of such a model reveals substantial heterogeneity in subjects’ beliefs about and adherence to the norm. A simple fundraising strategy increases donations by an estimated 30% by exploiting previously unstudied correlations between dimensions of donor preferences.

How do risk attitudes change after experiencing gains or losses? For the case of losses, Imas (Am Econ Rev 106:2086–2109, 2016) shows that subsequent risk-taking behavior depends on whether these losses have been realized or not. After a realized loss, individuals’ risk-taking decreases, whereas it increases after an unrealized (paper) loss. He refers to this asymmetry as the realization effect. In this study, we derive theoretical predictions for risk-taking after paper and realized gains, and for investment opportunities with different skewness. We experimentally test these predictions and, at the same time, replicate Imas’ original study. Independent of a prior gain or loss, we show that subsequent risk-taking is higher when outcomes remain unrealized. However, we find no evidence of a realization effect for non-positively skewed lotteries. While the first result suggests that the effect is more general, the second result reveals its boundary conditions.

The level-k literature classifies subjects into different Lk types by their observed levels of reasoning in games. However, it remains unclear whether the observed level-k behavior is determined by belief or reasoning ability. This study proposes a strategy to identify the ability-bounded Lk subjects, who could not reason more than k steps of iterated best responses and thus have reached the upper bounds of their reasoning capacity. The identification utilizes a combination of simultaneous and sequential ring games. In the sequential games, it requires an extra step to best respond as Lk, and thus the ability-bounded ones would fail the task. Results show that more than half of the L2 and L3 subjects are ability-bounded. Additionally, subjects’ CRT scores, a measure of their cognitive ability, support the separation of the ability-bounded types. The findings suggest that both belief and reasoning ability could determine the observed levels, and thus one must be cautious when trying to infer belief or ability from the existing level-k data.

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