This award will support a theoretical and experimental study on the dynamic stability of Nash-efficient public goods mechanisms. The theoretical investigation will evolve around developing a new family of supermodular Nash mechanisms that implement Lindahl allocations. The experimental research will study the stability of variants from the same new family of mechanisms in a laboratory setting with real human subjects. The data will enable the investigator to study the effects of institutions/mechanisms on the resulting learning dynamics adopted, to calibrate and compare the performance of different learning rules, and to refine and improve on existing learning models in light of the data. Previous theoretical work on Nash implementation has mainly focused on establishing the static properties of Nash equilibria. The few exceptions have been using specific dynamic learning rules to study the stability of different mechanisms, such as Cournot best response or the gradient adjustment process. Based on previous experimental findings and its robust theoretical properties, the investigator proposes to use supermodularity as a reasonable stability criterion for Nash-efficient mechanisms. This provides a fresh perspective on implementation theory and contributes to the public goods mechanism literature. The new family of mechanisms will be the first Nash-efficient public goods mechanisms which implement Lindahl allocations and also have a robust stability property. The experimental study will contribute to the ongoing research on learning and implementation. The PI is an assistant professor who is in an early stage of building up a career that combines theoretical and experimental research in mechanism design. She is in her fourth year at Michigan where tenure in the field of economics is difficult to achieve. The POWRE award will assist her at a critical career stage.
