We explore whether individuals are averse to telling a Pareto white lie—a lie that benefits both themselves and another. We first review and summarize the existing evidence on Pareto white lies. We find that the evidence is relatively limited and varied in its conclusions. We then present new experimental results obtained using a coin-tossing experiment. Results are provided for both the UK and China. We find evidence of willingness to tell a partial lie (i.e., inflating reports slightly) and high levels of aversion to telling a Pareto white lie that would maximize payoffs. We also find no significant difference between willingness to tell a Pareto white lie and a selfish black lie—a lie that harms another. We find marginal evidence of more lying in China than the UK, but the overall results in the UK and China are very similar.

In this paper, we revisit the evidence for framing effects in threshold public good games. Our particular focus is on why the probability of providing the public good appears to be higher in positive, give frames compared with negative, take frames. We show that the impulse balance theory can explain this effect. We also report a new experiment designed to test the predictions of the impulse balance theory. The results of the experiment fit well, both in quantitative and qualitative terms, with our predictions.

This study examines the role of Artificial Intelligence (AI) in reducing medical overtreatment, a critical healthcare challenge that increases costs and patient risks. In two experiments - with 196 physicians at a hospital and 120 students at a medical school in Wuhan - we use a novel medical prescription task under three incentive schemes: flat (constant pay), progressive (pay increases with treatment quantity), and regressive (penalties for overtreatment) to estimate receptivity to AI assistance and its effects on overtreatment and treatment accuracy, and test whether effects vary with incentives. AI recommendation of a treatment is estimated to increase the probability a physician prescribes it by 25.7-28.4 percentage points (pp), with the largest effect under the flat scheme. Physicians are more receptive to AI recommendations in medical domains with which they are less familiar. We estimate that AI assistance reduces the probability a physician overtreats by 10.9-25.7 pp (15.2-80.3%), with significantly larger absolute and relative effects under the flat scheme compared to progressive and regressive schemes. AI assistance improves physicians' treatment accuracy by 9.8-13.3 pp (14.6-19.9%), with the largest absolute effect under the regressive scheme. These findings are corroborated by the medical school experiment, which reveals that factors indicative of insufficient ability account for 34% of the explained variation in overtreatment, monetary incentives account for 22%, patient welfare considerations account for 20%, and factors related to defensive medicine for 10%. These results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.