Mechanism Design Under Approximate Incentive Compatibility

Abstract

An assumption that is pervasive in revenue management and economics is that buyers are perfect optimizers. However, in practice, buyers may be limited by their computational capabilities or lack of information and may not be able to perfectly optimize their response to a selling mechanism. This has motivated the introduction of approximate incentive compatibility as a solution concept for practical mechanisms. In “Mechanism Design under Approximate Incentive Compatibility,” Balseiro, Besbes, and Castro study, for the first time, the problem of designing optimal selling mechanisms when buyers are imperfect optimizers. Their work characterizes structural properties of approximate incentive compatible mechanisms and establishes fundamental bounds on how much revenue can be garnered by moving from exact to approximate incentive constraints. Their work brings a new perspective to the theory of mechanism design by shedding light on a novel class of optimization problems, techniques, and challenges that emerge when relaxing incentive constraints.