We are interested in how Groves-Ledyard mechanisms perform when used repeatedly in a sequence of one-shot games where agents know only their own preferences. In particular, how fast do the mechanisms converge to the stage game Nash equilibrium and how does that speed of convergence depend on the mechanism parameter γ. Prior theoretical and experimental work provide little guidance. Neither do existing behavioral models designed for small games with a small finite number of strategies. For example, even though Experience Weighted Attraction learning is very successful in modeling behavior in one-shot games with very small, finite strategy spaces, it is not successful in modeling behavior in repeated games with a continuum strategy space unless one wants to be involved in fine tuning. We provide a behavioral model, Individual Evolutionary Learning. The time to first convergence is predicted to be smooth and U-shaped in γ. These predictions are robust to a wide range of parameter values. To test the IEL predictions, we ran our own experiments at the California Institute of Technology. Qualitatively, the data from those experiments are consistent with the IEL predictions about convergence and the U-shaped curve. Quantitatively, the human subjects are a little faster, a little less stable, and slightly less efficient than IEL. But for γ = 50 and 100, the differences between humans and IEL are very small.

This paper presents an experimental study of two mechanisms that influence incentives to reduce emissions that increase ambient pollution levels. In the formal mechanism individuals face a penalty if the group generates total pollution that exceeds a specified target, whereas in the informal mechanism individuals can choose to incur costs to punish each other after observing their individual emissions. We examine the effectiveness of these mechanisms, in isolation and in combination. The results suggest that the formal targeting mechanism is significantly more effective than informal peer punishment in reducing pollution and increasing efficiency.

ABSTRACT. We study the design of mechanisms that implement Lindahl or Walrasian allocations and whose Nash equilibria are dynamically stable for a wide class of adaptive dynamics. We argue that supermodularity is not a desirable stability criterion in this mechanism design context, focusing instead on contractive mechanisms. We provide necessary and sufficient conditions for a mechanism to Nash implement Lindahl or Walrasian allocations, show that these conditions are inconsistent with the contraction property when message spaces are onedimensional, and then show how to use additional dimensions to achieve dynamic stability while gaining budget balance out of equilibrium.