جستجوی مقالات مرتبط با کلیدواژه « Agent-based modeling » در نشریات گروه « علوم اجتماعی »

Many people die each year due to smoking. Social pressures, such as peer pressure, which are manifested in one's interactions with his/her peers, are from very effective factors in people's tendency to smoke. In this research, it was attempted to study and analyze peer pressure in smoking and its impact on some of the welfare indicators including mortality number due to starvation, wealth average of agents and Gini coefficient using agent-based modeling and simulation. The research used agent-based modeling and simulation method and also used library studies. Using the NetLogo software and the basic models and considering other requirements, a model was developed and some experiments were performed. Findings of the experiments showed that in both situations; absence of peer pressure in smoking and presence of peer pressure in smoking, by increasing number of smoker agents in society, mortality number due to starvation and average of Gini coefficient increase and wealth average of agents decreases. Findings of the experiments in the situation of absence of peer pressure in smoking indicate a richer society in comparison with the situation of presence of peer pressure in smoking. Consequently, increasing the number of smoker agents in society can lead to decrease social welfare. Also, presence of peer pressure in smoking can lead to decrease wealth average and increase mortality number due to starvation of agents. Therefore, in order to properly and principally deal with peer pressure for smoking and preventing the consequence resulting from it, it is necessary to study more about this problem and to employ appropriate and effective encounter strategies for it.

One of the important issues in demography is helping to adopt appropriate and effective policies dealing with low fertility. This topic has a special importance in Iran due to the attainment of below-replacement fertility. Using analytical methods, some studies have been conducted to analyze low fertility in recent years. In this paper, the studies explaining low fertility using analytic method are critically reviewed. The absence of a linear, uniform and homogeneous relationship between the independent variables affecting low fertility and lack of deep understanding of the complex system of lower fertility are evident in previous studies. Agent-based modeling is introduced as a technical tool and a suitable solution developed based on computer programming. This approach can easily resolve the limitations associated with analytical methods. The ability of agent-based modeling in making bridge between micro and macro levels as well as the capability of complex system modeling are the strengths that can be employed in explaining the complex system of low fertility.

Application of the agent-based modeling in analyzing population issues has attracted the attention of demographers, and the status of demography in deeper understanding of population trends has been improved. The aim of this paper is to review the evolutionary path of demography from the beginning to the emergence and usage of the agent-based modeling in population studies. The literature review revealed the inability of demographic paradigms in connecting the micro and macro levels as one of the main limitations in the analysis of complicated demographic issues. Today, the agent-based modeling is of particular importance among demographers due to development of mathematics and production of powerful software and processors. Policy making based on the agent-based modeling is an efficient approach with the ability to analyze the micro- and macro-level relationships, increaseing the likelihood of achieving the goals pursued by macro policies. By reviewing the evolution of different paradigms and expressing the need to use the agent-based modeling approach in the process of demographic analysis, this article reviews and introduces the position of the agent-based modeling with its strengths and weaknesses in comparison with other paradigms. Finally, some suggestions for using the agent-based modeling in the analysis of Iran's demographic issues are presented.

Scientists believe that the spread of contagious diseases and immunization of the population has huge effects on the health and economy of the community, and vaccination has been pointed out to be one of the most effective methods of preventive interventions. The purpose of the study was to investigate and analyze the effects of contagious disease spread and immunization of the population on social welfare by the agent based modeling computational approach. In the current research, attempts were made to simulate the spread of contagious disease and immunization in a society of agents and investigate their effects on some welfare indicators including Gini coefficient, wealth average, and mortality rate due to starvation. The intended method in the present study was agent-based modeling and simulation along with conducting library studies, and the instrument used was NetLogo. Therefore, a model was extended and developed, and the society was simulated once in the absence of the sick agent and infection in the environment. Depending on the level of vision, the agents began to collect sugar from the environment. Next simulation was a society consisting of sick or infected agents and transmission of disease among population. Besides, the agents collected and consumed the environmental resource (sugar). In the third simulation, immunization of the population (through vaccination) was applied to control the infection and spread of the disease. Thus, some experiments were carried out, and the observations and findings were recorded to eventually be compared and analyzed. Findings of the experiments suggest that the presence and spread of contagious diseases in the population could lead to decreased wealth average of agents, increased rate of mortality due to starvation, and increased Gini coefficient (more unequal distribution of wealth) as well. Thus, it can lead to reduced welfare of agents in an artificial society. Moreover, immunization of the population and control of disease by vaccination can increase wealth average of agents, decrease the mortality rate due to starvation, and decrease Gini coefficient. Therefore, it can lead to the improvement of welfare status in an artificial society. Using agent-based modeling, the spread of contagious disease in the population and immunization of the population (through vaccination) was simulated and investigated through various experiments, and their effects on wealth distribution, mortality rate due to starvation, and wealth average of the agents were identified. Mapping the results of the artificial environment into the real world suggests that the spread of contagious disease in the population could lead to decreased social welfare in a society. Immunization of the population as well as controlling the disease can lead to the improvement of welfare in a society. Library studies also confirm the validity of the results derived from the model. Studies indicate that vaccination is capable of significantly decreasing mortality and inequality. Studies also show that contagious diseases, especially in low-income countries, still account for a significant part of mortality. Vaccination can reduce the burden of contagious diseases and their imposed costs, decrease differences in wealth, and support social development and economic growth. Moreover, studies show that controlling contagious diseases can save wealth for societies and countries and provide social and economic advantages. Finally, some points should be mentioned about the methodology of the study: despite the strengths and advantages of using of agent based modeling approach in social, economic, and health studies, some points should be noted. Some social science experts believe that an intelligent computational agent has little similarity with being human. In addition, in the development of computational models, some variables, or parameters or conditions, and rules affecting a system or a real problem may not be considered. Furthermore, the results from the artificial environment may be accompanied by errors in comparison with the results from the real environment, although the error value may be acceptable. Additionally, Some questions remain, like: Is using such modeling always possible while facing any complex problems or systems? With what combination of parameters, rules, and conditions? Is any developed model (in the artificial environment) reliable and valid? Does any developed model have the required validity and quality? What approaches or methods and issues should be considered in order to validate and verify agent based computational models? These are some of the concerns and questions that are still likely to exist in the face of the developers of these models and users of this approaches (methods) and these can be referred to as some of the limitations and challenges in similar research studies.