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This study examines the mechanism design problem for public-good provision in a large economy with $n$ independent agents. We propose a class of dominant-strategy incentive compatible (DSIC) and ex post individual rational (EPIR) mechanisms which we call the adjusted mean-thresholding (AMT) mechanisms. We show that when the cost of provision grows slower than the $sqrt{n}$ rate, the AMT mechanisms are both asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE). When the cost grows faster than the $sqrt{n}$ rate, in contrast, we show that any DSIC, EPIR, and AEABB mechanism must have provision probability converging to zero and hence cannot be AE. Lastly, the AMT mechanisms are more informationally robust when compared to, for example, the second-best mechanism. This is because the construction of AMT mechanisms depends only on the first moments of the valuation distributions.
This paper considers incentives to provide goods that are partially excludable along social links. Individuals face a capacity constraint in that, conditional upon providing, they may nominate only a subset of neighbours as co-beneficiaries. Our mode
We study the design of revenue-maximizing bilateral trade mechanisms in the correlated private value environment. We assume the designer only knows the expectations of the agents values, but knows neither the marginal distribution nor the correlation
In this Brief Report we study the evolutionary dynamics of the Public Goods Game in a population of mobile agents embedded in a 2-dimensional space. In this framework, the backbone of interactions between agents changes in time, allowing us to study
Public goods games in undirected networks are generally known to have pure Nash equilibria, which are easy to find. In contrast, we prove that, in directed networks, a broad range of public goods games have intractable equilibrium problems: The exist
We consider agents with non-linear preferences given by private values and private budgets. We quantify the extent to which posted pricing approximately optimizes welfare and revenue for a single agent. We give a reduction framework that extends the