Nonlinear bubbles in Chinese Stock Markets in the 1990s

A time series of the Shanghai stock index in China for the 1990s is studied for the possible existence of nonlinear speculative bubbles. Three alternative specifications of fundamentals are estimated using VAR models of domestic and international variables. These are subjected to regime switching tests and rescaled range analysis tests. Nulls of no persistence were mostly rejected, suggesting the strong possibility of bubbles. Nonlinearities beyond ARCH effects using the BDS test could not be rejected. The paper also discusses the special circumstances of the stock market in an emerging transition economy.

Social Norm, Costly Punishment and the Evolution to Cooperation

Both laboratory and field evidence suggest that people tend to voluntarily incur costs to punish non-cooperators. While costly punishment typically reduces the average payoff as well as promotes cooperation. Why does the costly punishment evolve? We study the role of punishment in cooperation promotion within a two-level evolution framework of individual strategies and social norms. In a population with certain social norm, players update their strategies according to the payoff differences among different strategies. In a longer horizon, the evolution of social norm may be driven by the average payoffs of all members of the society. Norms differ in whether they allow or do not allow for the punishment action as part of strategies, and, for the former, they further differ in whether they encourage or do not encourage the punishment action. The strategy dynamics are articulated under different social norms. It is found that costly punishment does contribute to the evolution toward cooperation. Not only does the attraction basin of cooperative evolutionary stable state (CESS) become larger, but also the convergence speed to CESS is faster. These two properties are further enhanced if the punishment action is encouraged by the social norm. This model can be used to explain the widespread existence of costly punishment in human society.social norm; costly punishment; cooperative evolutionary stable state; attraction basin; convergence speed

TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks

Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs). In this paper, we extend the research over BS switching operations, which should match up with traffic load variations. Instead of depending on the dynamic traffic loads which are still quite challenging to precisely forecast, we firstly formulate the traffic variations as a Markov decision process. Afterwards, in order to foresightedly minimize the energy consumption of RANs, we design a reinforcement learning framework based BS switching operation scheme. Furthermore, to avoid the underlying curse of dimensionality in reinforcement learning, a transfer actor-critic algorithm (TACT), which utilizes the transferred learning expertise in historical periods or neighboring regions, is proposed and provably converges. In the end, we evaluate our proposed scheme by extensive simulations under various practical configurations and show that the proposed TACT algorithm contributes to a performance jumpstart and demonstrates the feasibility of significant energy efficiency improvement at the expense of tolerable delay performance.Comment: 11 figures, 30 pages, accepted in IEEE Transactions on Wireless Communications 2014. IEEE Trans. Wireless Commun., Feb. 201

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc