Please use this identifier to cite or link to this item:
Title: Monte Carlo sampling processes and incentive compatible allocations in large economies
Authors: Hammond, Peter J.
Qiao, Lei
Sun, Yeneng 
Keywords: Asymmetric information
Hilbert space
Incentive compatibility
Law of large numbers
Monte Carlo sampling process
One-way Fubini property
Pareto efficiency
Issue Date: 23-Oct-2020
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: Hammond, Peter J., Qiao, Lei, Sun, Yeneng (2020-10-23). Monte Carlo sampling processes and incentive compatible allocations in large economies. Economic Theory 71 (3) : 1161-1187. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Monte Carlo simulation is used in Hammond and Sun (Econ Theory 36:303–325, 2008. 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 Hammond and Sun (2008), 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, following some similar results in McLean and Postlewaite (Econometrica 70:2421–2453, 2002) and Sun and Yannelis (J Econ Theory 134:175–194, 2007. © 2020, The Author(s).
Source Title: Economic Theory
ISSN: 0938-2259
DOI: 10.1007/s00199-020-01318-5
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1007_s00199-020-01318-5.pdf495.24 kBAdobe PDF




checked on Dec 2, 2022

Page view(s)

checked on Dec 1, 2022

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons