Alejandro Saavedra-Nieves
University of Vigo
23 Papers
7 Citations
Alejandro Saavedra-Nieves is an academic researcher from University of Vigo. The author has contributed to research in topics: Computer science & Sampling (statistics). The author has an hindex of 4, co-authored 14 publications. Previous affiliations of Alejandro Saavedra-Nieves include University of Santiago de Compostela.
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Papers
Estimation of the Owen Value Based on Sampling
Alejandro Saavedra-Nieves,Ignacio García-Jurado,M. Gloria Fiestras-Janeiro +2 more
- 01 Jan 2018
TL;DR: In this paper, the authors introduce a sampling-based approach for the estimation of the Owen value of a cooperative game, which is an adaptation of an analogous procedure for estimating the Shapley value, and is specially useful for games having large sets of players.
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On systems of quotas from bankruptcy perspective: the sampling estimation of the random arrival rule
TL;DR: A sampling procedure for estimating the random arrival rule in bankruptcy situations is addressed, based on simple random sampling with replacement and an estimation method of the Shapley value for transferable utility games, especially useful when dealing with large-scale problems.
14
Sampling methods to estimate the Banzhaf–Owen value
TL;DR: This paper addresses two sampling methods to estimate the Banzhaf–Owen value for general cooperative games and proposes an alternative estimation procedure based on two-stage sampling that reduces the required computation time.
13
Risk analysis sampling methods in terrorist networks based on the Banzhaf value.
TL;DR: In this paper , the authors proposed the Banzhaf and the BOW values as novel measures of risk analysis of a terrorist attack, determining the most dangerous terrorists in a network, which counts with the advantage of integrating at the same time the complete topology (i.e., nodes and edges) of the network and a coalitional structure on the nodes.
8
On interactive sequencing situations with exponential cost functions
TL;DR: This paper addresses interactive one-machine sequencing situations in which the costs of processing a job are given by an exponential function of its completion time and shows that in these subclasses, all neighbor switches in any path from the initial order to an optimal order lead to a non-negative gain.
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