Most work in mechanism design assumes that buyers are risk neutral; some
considers risk aversion arising due to a non-linear utility for money. Yet
behavioral studies have established that real agents exhibit risk attitudes
which cannot be captured by any expected utility model. We initiate the study
of revenue-optimal mechanisms under behavioral models beyond expected utility
theory. We adopt a model from prospect theory which arose to explain these
discrepancies and incorporates agents under-weighting uncertain outcomes. In
our model, an event occurring with probability x < 1 is worth strictly less to
the agent than x times the value of the event when it occurs with certainty.
I will present three main results. First, I will characterize optimal mechanisms as
menus of two-outcome lotteries. Second, I will show that under a reasonable
bounded-risk-aversion assumption, posted pricing obtains a constant
approximation to the optimal revenue. Notably, this result is "risk-robust"
in that it does not depend on the details of the buyer's risk attitude. Third,
I will discuss dynamic settings in which the buyer’s uncertainty about his future
value may allow the seller to extract more revenue. In contrast to the positive
result above, here I will show it is not possible to achieve any constant-factor
approximation to revenue using deterministic mechanisms in a risk-robust
Based on joint work with Shuchi Chawla, Kira Goldner, and Emmanouil Pountourakis.