When Globalization Meets Urbanization: Labor Market Reform, Income Inequality, and Economic Growth in the People's Republic of China

The development path that the People's Republic of China (PRC) has been following during the past thirty years has led to both internal and external economic imbalances, and is now greatly challenged by the global crisis. This unbalanced growth path was primarily a result of the PRC's labor market reform which took the years of the mid-1990s as its turning point. Before the mid-1990s, the scale of rural-to-urban migration was limited, but it has grown dramatically since then. 1996 also saw drastic employment restructuring in urban areas of the PRC. Labor market reform, accompanied by the foreign exchange system reform in 1994, confirmed the PRC's comparative advantage of low labor cost, and therefore further increased the PRC's reliance on exports. However, the increased income disparity that resulted from the labor market reform may jeopardize sustainable growth if no adjustment is made. To sustain the high economic growth, especially in face of the current crisis, the PRC needs to adjust its reform and development strategies to promote income equality.china labor market unemployment; china income inequality; china economic growth crisis

Evolutionary Game Dynamics for Two Interacting Populations under Environmental Feedback

We study the evolutionary dynamics of games under environmental feedback using replicator equations for two interacting populations. One key feature is to consider jointly the co-evolution of the dynamic payoff matrices and the state of the environment: the payoff matrix varies with the changing environment and at the same time, the state of the environment is affected indirectly by the changing payoff matrix through the evolving population profiles. For such co-evolutionary dynamics, we investigate whether convergence will take place, and if so, how. In particular, we identify the scenarios where oscillation offers the best predictions of long-run behavior by using reversible system theory. The obtained results are useful to describe the evolution of multi-community societies in which individuals' payoffs and societal feedback interact.Comment: 7 pages, submitted to a conferenc

Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a provably ideal method of inference using Kolmogorov complexity. We test how the theory behaves in practice on a general problem in model selection: that of learning the best model granularity. The performance of a model depends critically on the granularity, for example the choice of precision of the parameters. Too high precision generally involves modeling of accidental noise and too low precision may lead to confusion of models that should be distinguished. This precision is often determined ad hoc. In MDL the best model is the one that most compresses a two-part code of the data set: this embodies ``Occam's Razor.'' In two quite different experimental settings the theoretical value determined using MDL coincides with the best value found experimentally. In the first experiment the task is to recognize isolated handwritten characters in one subject's handwriting, irrespective of size and orientation. Based on a new modification of elastic matching, using multiple prototypes per character, the optimal prediction rate is predicted for the learned parameter (length of sampling interval) considered most likely by MDL, which is shown to coincide with the best value found experimentally. In the second experiment the task is to model a robot arm with two degrees of freedom using a three layer feed-forward neural network where we need to determine the number of nodes in the hidden layer giving best modeling performance. The optimal model (the one that extrapolizes best on unseen examples) is predicted for the number of nodes in the hidden layer considered most likely by MDL, which again is found to coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To appea