We conduct an incentivized lab experiment to test participants' ability to understand the DA matching mechanism and the strategyproofness property, conveyed in different ways. We find that while many participants can (using a novel GUI) learn DA's mechanics and calculate its outcomes, such understanding does not imply understanding of strategyproofness (as measured by specially designed tests). However, a novel menu description of strategyproofness conveys this property significantly better than other treatments. While behavioral effects are small on average, participants with levels of strategyproofness understanding above a certain threshold play the classical dominant strategy at very high rates.

We propose and initiate the study of privacy elasticity---the responsiveness of economic variables to small changes in the level of privacy given to participants in an economic system. Individuals rarely experience either full privacy or a complete lack of privacy; we propose to use differential privacy---a computer-science theory increasingly adopted by industry and government---as a standardized means of quantifying continuous privacy changes. The resulting privacy measure implies a privacy-elasticity notion that is portable and comparable across contexts. We demonstrate the feasibility of this approach by estimating the privacy elasticity of public-good contributions in a lab experiment.

A menu description presents a mechanism to player i in two steps. Step (1) uses the reports of other players to describe i's menu: the set of i's potential outcomes. Step (2) uses i's report to select i's favorite outcome from her menu. Can menu descriptions better expose strategyproofness, without sacrificing simplicity? We propose a new, simple menu description of Deferred Acceptance. We prove that---in contrast with other common matching mechanisms---this menu description must differ substantially from the corresponding traditional description. We demonstrate, with a lab experiment on two elementary mechanisms, the promise and challenges of menu descriptions.

In a controlled experiment, participants ( n=60) competed in a monotonous task with an autonomous robot for real monetary incentives. For each participant, we manipulated the robot's performance and the monetary incentive level across ten rounds. In each round, a participant's performance compared to the robot's would affect their odds in a lottery for the monetary prize. Standard economic theory predicts that people's effort will increase with prize value. Furthermore, recent work in behavioral economics predicts that there will also be a discouragement effect, with stronger robot performance discouraging human effort, and that this effect will increase with prize. We were not able to detect a meaningful effect of monetary prize, but we found a small discouragement effect, with human effort decreasing with increased robot performance, significant at the level. Using per-round subjective indicators, we also found a positive effect of robot performance on its perceived competence, a negative effect on the participants' liking of the robot, and a negative effect on the participants' own competence, all at . These findings shed light on how people may exert work effort and perceive robotic competitors in a human-robot workforce, and could have implications on labor supply decisions and the design of compensation schemes in the workplace.