Exchange Market Pressure and De Facto The Evolution of Demographic Phenomena in Terms of Globalization and Environmental Changes

Globalization, climatic changes and demography are the main forces modeling the development of societies, in general, and of each nation, in particular. Both offer opportunities but also imply challenges. The paper aims to identify and synthetically present some factors of influence which triggered by the end of the 20th century and the beginning of the 21st century a series of characteristics defining the changes in the demographic model and structure of population by age brackets. Romania’s population lost in the period 1992-2007 about 1250 thousand persons. Also, this paper presents the most important factors that determined the demographic decline beginning in the last decade of the 20th century at national level. For Romania, another phenomenon was represented by the demographic implications of the emigration of the female population of fertile age. The increasing share of female population in the emigration flows became more noticeable over the period 2002-2007. The paper presents the results obtained by using the Markov model for studying the development of demographic indicators in Romania, and their forecasting as well.globalization, climate changes, demographic evolution, socio-economic impact, demo-economic factors, models, Markov models

The evolution of Romanian demographic phenomena in terms of globalization

Globalisation and demography are the main forces modelling the development of societies at large and of each nation in particular.The paper aims to identify and synthetically present some factors of influence which triggered by the end of the 20th century and the beginning of the 21st century a series of characteristics defining the changes of the demographic model and structure of population on age brackets.The paper presents the results obtained by using the Markov-type model, for studying the development of demographic indicators in Romania, and their forecasting as well.

A Reinforcement Learning Technique For Enhancing Human Behavior Models In A Context-based Architecture

A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly