Information sharing has become increasingly important in helping consumers make better, more informed choices over competing products. Our project uses a novel theoretical framework and laboratory experiments to analyze three simple, commonly used incentive schemes against an unincentivized...

This paper experimentally examines the selection of equilibria in dynamic games. Our baseline treatment is a two-state extension of an indefinitely repeated prisoner's dilemma, which we modify in series of treatments to study the focality of efficiency and symmetry, the effect dynamic and static...

Real-effort experiments are frequently used when examining a response to incentives. For any particular real-effort task to be well-suited for such an exercise, subjects' cost for exerting effort must, for the range of incentives considered, result in an interior effort choice. The popular...

Under regret theory, decision-makers derive utility both from the outcome of their chosen action and the counterfactual. Evidence for anticipatory regret aversion has been found in one shot settings, with ”regret lotteries” that always reveal outcomes, as a counterfactual on non-entry, being...

This paper uses a new data set on domestic child adoption to document the preferences of potential adoptive parents over born and unborn babies relinquished for adoption by their birth mothers. We show that adoptive parents exhibit significant biases in favor of girls and against...

We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire university student population, a representative sample of the U.S. population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments with student participants....

This paper uses a new data set on domestic child adoption to document the preferences of potential adoptive parents over born and unborn babies relinquished for adoption by their birth mothers. We show that adoptive parents exhibit significant biases in favor of girls and against...