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 Λ(α)\Lambda(\alpha)CDM Cosmology

In this work, we consider the so-called $\Lambda(\alpha)$CDM cosmology with $\Lambda\propto\alpha^{-6}$ while the fine-structure "constant" $\alpha$ is varying. In this scenario, the accelerated expansion of the universe is driven by the cosmological "constant" $\Lambda$ (equivalently the vacuum energy), and the varying $\alpha$ is driven by a subdominant scalar field $\phi$ coupling with the electromagnetic field. The observational constraints on the varying $\alpha$ and $\Lambda\propto\alpha^{-6}$ models with various couplings $B_F(\phi)$ between the subdominant scalar field $\phi$ 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