18,048 research outputs found
Cooperation Enforcement and Collusion Resistance in Repeated Public Goods Games
Enforcing cooperation among substantial agents is one of the main objectives
for multi-agent systems. However, due to the existence of inherent social
dilemmas in many scenarios, the free-rider problem may arise during agents'
long-run interactions and things become even severer when self-interested
agents work in collusion with each other to get extra benefits. It is commonly
accepted that in such social dilemmas, there exists no simple strategy for an
agent whereby she can simultaneously manipulate on the utility of each of her
opponents and further promote mutual cooperation among all agents. Here, we
show that such strategies do exist. Under the conventional repeated public
goods game, we novelly identify them and find that, when confronted with such
strategies, a single opponent can maximize his utility only via global
cooperation and any colluding alliance cannot get the upper hand. Since a full
cooperation is individually optimal for any single opponent, a stable
cooperation among all players can be achieved. Moreover, we experimentally show
that these strategies can still promote cooperation even when the opponents are
both self-learning and collusive
Observational Constraints on Varying Alpha in CDM Cosmology
In this work, we consider the so-called CDM cosmology with
while the fine-structure "constant" is
varying. In this scenario, the accelerated expansion of the universe is driven
by the cosmological "constant" (equivalently the vacuum energy), and
the varying is driven by a subdominant scalar field coupling
with the electromagnetic field. The observational constraints on the varying
and models with various couplings
between the subdominant scalar field and the electromagnetic
field are considered.Comment: 13 pages, 5 figures, 1 table, revtex4; v2: appendix removed, Commun.
Theor. Phys. in press; v3: published version. arXiv admin note: text overlap
with arXiv:1605.0457
Sparsifying the Fisher Linear Discriminant by Rotation
Many high dimensional classification techniques have been proposed in the
literature based on sparse linear discriminant analysis (LDA). To efficiently
use them, sparsity of linear classifiers is a prerequisite. However, this might
not be readily available in many applications, and rotations of data are
required to create the needed sparsity. In this paper, we propose a family of
rotations to create the required sparsity. The basic idea is to use the
principal components of the sample covariance matrix of the pooled samples and
its variants to rotate the data first and to then apply an existing high
dimensional classifier. This rotate-and-solve procedure can be combined with
any existing classifiers, and is robust against the sparsity level of the true
model. We show that these rotations do create the sparsity needed for high
dimensional classifications and provide theoretical understanding why such a
rotation works empirically. The effectiveness of the proposed method is
demonstrated by a number of simulated and real data examples, and the
improvements of our method over some popular high dimensional classification
rules are clearly shown.Comment: 30 pages and 9 figures. This paper has been accepted by Journal of
the Royal Statistical Society: Series B (Statistical Methodology). The first
two versions of this paper were uploaded to Bin Dong's web site under the
title "A Rotate-and-Solve Procedure for Classification" in 2013 May and 2014
January. This version may be slightly different from the published versio
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