Approximate implementation in Markovian Environments
Ludovic Renou | University of Essex
This paper considers dynamic implementation problems in environments with changing private information (according to Markov processes).
A social choice function is approximatively implementable if it is correctly implemented an arbitrary large number of times with arbitrary high probability in all (communication) equilibria.
We show that if a social choice function is strictly efficient in the set of social choice functions that satisfy an undetectability condition, then it is approximatively implementable.
We revisit the classical monopolistic screening problem and show that the monopolist can extract the full surplus in almost all periods with arbitrary high probability.
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