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The results show that residual-graph value learning yields state-dependent dynamic matching policies that adapt to realized connectivity and exit information.
We study the welfare-maximizing allocation of heterogeneous objects when screening uses costly effort rather than monetary transfers. No-screening mechanisms perform well as object variety increases. In a symmetric continuous market with i.i.d. values whose CDF is log-concave, the multidimensional problem reduces exact...
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