DeepAI AI Chat
Log In Sign Up

Monte Carlo Methods for Calculating Shapley-Shubik Power Index in Weighted Majority Games

by   Yuto Ushioda, et al.

This paper addresses Monte Carlo algorithms for calculating the Shapley-Shubik power index in weighted majority games. First, we analyze a naive Monte Carlo algorithm and discuss the required number of samples. We then propose an efficient Monte Carlo algorithm and show that our algorithm reduces the required number of samples as compared to the naive algorithm.


page 1

page 2

page 3

page 4


MoCaNA, un agent de négociation automatique utilisant la recherche arborescente de Monte-Carlo

Automated negotiation is a rising topic in Artificial Intelligence resea...

A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows

We propose a novel Monte-Carlo based ab-initio algorithm for directly co...

Eficient Monte Carlo Simulation of the Left Tail of Positive Gaussian Quadratic Forms

Estimating the left tail of quadratic forms in Gaussian random vectors i...

Toward Optimal Stratification for Stratified Monte-Carlo Integration

We consider the problem of adaptive stratified sampling for Monte Carlo ...

Monte Carlo Techniques for Approximating the Myerson Value – Theoretical and Empirical Analysis

Myerson first introduced graph-restricted games in order to model the in...

Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess

This paper introduces deep synoptic Monte Carlo planning (DSMCP) for lar...