Personnel Practices in Japanese Government

This paper examines key aspects of Japanese Government personnel practices, and draws comparisons with the practices in the U.S. civil service and, as appropriate, with other U.S. models. Particular reference is made to the practices of two major agencies in Japan, the Ministry of International Trade and Industry (MITI) and the Ministry of Posts and Telecommunications (MPT). As appropriate, selected government businesses practices other than personnel practices are noted.

Online Learning of Non-stationary Sequences

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate a class of universal learning algorithms for this problem by expressing them as simple Bayesian algorithms operating on models analogous to Hidden Markov Models (HMMs). We derive a new performance bound for such algorithms which is considerably simpler than existing bounds. The bound provides the basis for learning the rate at which the identity of the optimal expert switches over time. We find an analytic expression for the a priori resolution at which we need to learn the rate parameter. We extend our scalar switching-rate result to models of the switching-rate that are governed by a matrix of parameters, i.e. arbitrary homogeneous HMMs. We apply and examine our algorithm in the context of the problem of energy management in wireless networks. We analyze the new results in the framework of Information Theory.