scholarly journals Monte Carlo sampling processes and incentive compatible allocations in large economies

2020 ◽  
Author(s):  
Peter J. Hammond ◽  
Lei Qiao ◽  
Yeneng Sun
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Incentive Compatibility ◽  
Random Variables ◽  
Almost Sure Convergence ◽  
Econ Theory ◽  
Monte Carlo Sampling ◽  
Link Type ◽  
Stochastic Framework ◽  
Almost All

Abstract Monte Carlo simulation is used in Hammond and Sun (Econ Theory 36:303–325, 2008. 10.1007/s00199-007-0279-7) 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. 10.1016/j.jet.2006.03.001).

Analysis of RC Member’s Reliability Subjected to Eccentric Compression Based on Monte Carlo Simulation

2014 ◽  
Vol 487 ◽  
pp. 465-469
Author(s):  
Wen Feng Duan ◽  
Chang Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reinforced Concrete ◽  
Concrete Strength ◽  
Random Variables ◽  
Structural Member ◽  
Eccentric Compression ◽  
Compression Members ◽  
Geometric Size ◽  
Compression Member

Reinforced concrete eccentric compression member is one of the most common structural member. Eccentric compression members are divided into large eccentric compression members and small eccentric compression members. Uncertainty of calculation, geometric size and concrete strength were considered as random variables, the reliability of eccentric compression members were discussed by monte carlo simulation.

Approximations to densities in geometric probability

1980 ◽  
Vol 17 (01) ◽  
pp. 145-153 ◽  
Author(s):  
H. Solomon ◽  
M. A. Stephens
Keyword(s):  
Monte Carlo ◽  
Random Variables ◽  
Monte Carlo Sampling ◽  
Geometric Probability ◽  
Simple Approximation ◽  
Chi Square ◽  
Percentage Points ◽  
Random Polygons

Many random variables arising in problems of geometric probability have intractable densities, and it is very difficult to find probabilities or percentage points based on these densities. A simple approximation, a generalization of the chi-square distribution, is suggested, to approximate such densities; the approximation uses the first three moments. These may be theoretically derived, or may be obtained from Monte Carlo sampling. The approximation is illustrated on random variables (the area, the perimeter, and the number of sides) associated with random polygons arising from two processes in the plane. Where it can be checked theoretically, the approximation gives good results. It is compared also with Pearson curve fits to the densities.

Reservoir Volume Estimation in Exploration Phase by Monte Carlo Simulation

2013 ◽  
Vol 859 ◽  
pp. 248-252
Author(s):  
Lei Zhao ◽  
Bing Li ◽  
Peng Xiang Diwu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Civil Engineering ◽  
Resource Assessment ◽  
Monte Carlo Sampling ◽  
Volume Estimation ◽  
Volume Calculation ◽  
Reservoir Volume ◽  
Few Data ◽  
Exploration Phase

The STOIIP determines the scale of civil engineering in the oilfield, so the accurate calculation STOIIP has a very important significance on civil engineering, especially in the exploration phase few data are available in oilfield, traditional volume calculation method is hardly to provide a reasonable result. The mathematical statistics method, namely Monte Carlo simulation is introduced to calculate reservoir volumes for hydrocarbons in place (STOIIP or GIIP). This method can provide several volume results by monte carlo sampling. making the resource assessment results a probability distribution rather than a single valuation, which greatly improve the credibility and usefulness of evaluation results. The S oilfield in Malaysia are evaluated and the results show the P50 STOIIP is 4.82 MMbbl.

Reliability Optimization Design of Gear Box

2011 ◽  
Vol 284-286 ◽  
pp. 2509-2512
Author(s):  
Wen Hui Mo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Material Properties ◽  
Optimization Design ◽  
Random Variables ◽  
Reliability Optimization ◽  
Objective Functions ◽  
Feasible Method ◽  
Reliability Calculation ◽  
Gear Box

Geometry parameters, material properties and applied loads of the gear box are regarded as normal random variables. A model of reliability optimization design of the gear box is introduced. Two objective functions are selected. The Monte Carlo simulation of reliability calculation is presented. With rapid increasing of the speed of CPU, it is a feasible method. The optimization effect is very good.

Reliability Assessment with Fuzzy Random Variables Using Interval Monte Carlo Simulation

2013 ◽  
Vol 29 (3) ◽  
pp. 208-220 ◽  
Author(s):  
Ehsan Jahani ◽  
Rafi L. Muhanna ◽  
Mohsen A. Shayanfar ◽  
Mohammad A. Barkhordari
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Assessment ◽  
Random Variables ◽  
Fuzzy Random Variables ◽  
Fuzzy Random

Monte Carlo Simulation of Reliability for Gear

2011 ◽  
Vol 268-270 ◽  
pp. 42-45 ◽  
Author(s):  
Wen Hui Mo
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fatigue Strength ◽  
Random Variables ◽  
Random Variable ◽  
Accurate Solution ◽  
Bending Fatigue ◽  
Simulation Based ◽  
Production Errors

Production errors, material properties and applied loads of the gear are stochastic .Considering the influence of these stochastic factors, reliability of gear is studied. The sensitivity analysis of random variable can reduce the number of random variables. Simulating random variables, a lot of samples are generated. Using the Monte Carlo simulation based on the sensitivity analysis, reliabilities of contacting fatigue strength and bending fatigue strength can be obtained. The Monte Carlo simulation approaches the accurate solution gradually with the increase of the number of simulations. The numerical example validates the proposed method.

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