Maximum Entropy Correlated Equilibria

We study maximum entropy correlated equilibria in (multi-player)games and provide two gradient-based algorithms that are guaranteedto converge to such equilibria. Although we do not provideconvergence rates for these algorithms, they do have strong connectionsto other algorithms (such as iterative scaling) which are effectiveheuristics for tasks such as statistical estimation

Network Structure Mining and Evolution Analysis - Based on BA Scale-Free Network Model

The massive adoption of the Internet facilitates growth of online social networks, in which information can be exchanged in a more efficient way. Such as products, user accounts, web pages, there may be a variety of objects suitable to structurize this kind of networks. As a result, this gives the networks complexity and dynamics. The work in this paper is aiming to studying the topological property of online social network structure from the aspect of dynamics, and make clear the evolution processes of the networks. This is done by a Mean-Field analysis of network growth based on BA Scale-Free network model. Data resources come from the Chinese online e-commerce platform you.163.com and graphs are modeled through commentator and mutual comments by calculating degree distribution of the networks. We build a growing random model for forecasting dynamics of degree evolution. Finally, we use data set on Sina Weibo to test the model and the results are satisfying

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity

We consider the problem of learning sparse polymatrix games from observations of strategic interactions. We show that a polynomial time method based on $\ell_{1,2}$-group regularized logistic regression recovers a game, whose Nash equilibria are the $\epsilon$-Nash equilibria of the game from which the data was generated (true game), in $\mathcal{O}(m^4 d^4 \log (pd))$ samples of strategy profiles --- where $m$ is the maximum number of pure strategies of a player, $p$ is the number of players, and $d$ is the maximum degree of the game graph. Under slightly more stringent separability conditions on the payoff matrices of the true game, we show that our method learns a game with the exact same Nash equilibria as the true game. We also show that $\Omega(d \log (pm))$ samples are necessary for any method to consistently recover a game, with the same Nash-equilibria as the true game, from observations of strategic interactions. We verify our theoretical results through simulation experiments