Monte Carlo Sampling Processes and Incentive Compatible Allocations in Large Economies
Peter Hammond (),
Lei Qiao and
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Lei Qiao: Shanghai University of Finance and Economics
Yeneng Sun: National University of Singapore
CRETA Online Discussion Paper Series from Centre for Research in Economic Theory and its Applications CRETA
Monte Carlo simulation is used in  to characterize a standard stochastic framework involving a continuum of random variables that are conditionally independent given macro shocks. This paper presents some general properties of such Monte Carlo sampling processes, including their one-way Fubini extension and regular conditional independence. In addition to the almost sure convergence of Monte Carlo simulation considered in , here we also consider norm convergence when the random variables are square integrable. This leads to a necessary and suﬃcient condition for the classical law of large numbers to hold in a general Hilbert space. Applying this analysis to large economies with asymmetric information shows that the conﬂict between incentive compatibility and Pareto eﬃciency is resolved asymptotically for almost all sampling economies, corresponding to some results in  and .
Keywords: Law of large numbers; Monte Carlo sampling process; one-way Fubini property; Hilbert space; incentive compatibility; asymmetric information; Pareto eﬃciency Jel Classification: C65; D51; D61; D82 (search for similar items in EconPapers)
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Journal Article: Monte Carlo sampling processes and incentive compatible allocations in large economies (2021)
Working Paper: Monte Carlo Sampling Processes and Incentive Compatible Allocations in Large Economies (2020)
Working Paper: Monte Carlo Sampling Processes and Incentive Compatible Allocations in Large Economies (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:wcreta:54
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