Bureau of Labor Statistics

About: Bureau of Labor Statistics is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Wage & Population. The organization has 410 authors who have published 941 publications receiving 38635 citations. The organization is also known as: BLS & U.S. Bureau of Labor Statistics.

The particle swarm - explosion, stability, and convergence in a multidimensional complex space

Abstract: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.

The fully informed particle swarm: simpler, maybe better

Abstract: The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.

Defining a Standard for Particle Swarm Optimization

Abstract: Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developments that can be expected to improve performance on standard measures. This standard algorithm is intended for use both as a baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community

Employer Learning and Statistical Discrimination

Abstract: We provide a test for statistical discrimination or rational stereotyping in in environments in which agents learn over time. Our application is to the labor market. If profit maximizing firms have limited information about the general productivity of new workers, they may choose to use easily observable characteristics such as years of education to 'statistically discriminate' among workers. As firms acquire more information about a worker, pay will become more dependent on actual productivity and less dependent on easily observable characteristics or credentials that predict productivity. Consider a wage equation that contains both the interaction between experience and a hard to observe variable that is positively related to productivity and the interaction between experience and a variable that firms can easily observe, such as years of education. We show that the wage coefficient on the unobservable productivity variable should rise with time in the labor market and the wage coefficient on education should fall. We investigate this proposition using panel data on education, the AFQT test, father's education, and wages for young men and their siblings from NLSY. We also examine the empirical implications of statistical discrimination on the basis of race. Our results support the hypothesis of statistical discrimination, although they are inconsistent with the hypothesis that firms fully utilize the information in race. Our analysis has wide implications for the analysis of the determinants of wage growth and productivity and the analysis of statistical discrimination in the labor market and elsewhere.(This abstract was borrowed from another version of this item.)