Chang-Won Ahn

Multi-Agent modeling for match-making using BDI architecture

Multi-Agent based modeling and simulation (MAS) has proven to be a useful approach for the study of complex social phenomena. Due to the diversity and huge number of factors, many population dynamics problems are difficult to be addressed properly with traditional analytical and statistical techniques. This research work focus on match making process of population dynamics. We designed a model in which agents interacting with other agents and environment to find a life partner. We are considering that agents age and socio-economics (referred to as education and income level) conditions are the key factors while taking decision for family formation. Using belief, desires and intensions (BDI) architecture, we explicitly take into account the agents heterogeneity with respect to age and income level. Using multi-agent model, this study explores how changes in agents desires and intensions might be transmitted through a population to effect the overall perception. Our model give more substantial evidence about how and why these attributes can influence the evolution of family formation.

Designing a multi-agent model using BDI architecture for population dynamics

Multi-Agent System (MAS) is a proven approach that permits to solve large and complex social problems. Due to heterogeneous nature of agents and various variables, many population dynamics problems are difficult to be addressed properly with traditional micro-simulation methodologies. This research work focus the designing of match making and fertility modules of population dynamics. In our model agents interacting with other agents and environment to find a life partner and then take decision about childbirth. The agents age and socio-economics (referred to as education and income level) conditions are the key factors while taking decisions about family formation and childbirth. Using belief, desires and intensions (BDI) architecture, we explicitly take into account the agents heterogeneity with respect to age and income level. Designing a conceptual multi-agent model, we are trying to explore how changes in agents desires and intensions might be transmitted through a population to effect the overall perception while taking decision about life partner and childbirth. The implementation of our model will give more substantial evidence about how and why these attributes can influence the evolution of family formation and childbirth in Korea.

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or “soft,” aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Simulating demography — Dynamics of fertility using a multi agent model

Many countries of the world have seen fertility rate declines. Sincere efforts have been made by the governments to change this trend. But still, countries like Korea & Japan are facing the problem of fertility rates well below the replacement fertility rates. Modeling and simulation offer a good way of understanding the dynamics of population. Of late, agent based modeling (ABM) has gained quite a lot of popularity in the field of simulation. We propose an actual population data fed agent based model whereby the agents in the simulation would take as input the actual census data and simulate the population fertility dynamics. Fertility rates are the main measures of population change. We will try and understand how fertility evolves by taking into various factors such as age, income, expenditure, social benefits; from the micro to macro level. The decisions of the agents, such as to get married and then have certain number of children, would be based on these factors. The fertility is predicted by accessing number of children a couple would have in the child bearing age. We hope to analyze and understand the effect of factors such as social benefits by this model.

Social simulation: The need of data-driven agent-based modelling approach

Agent-based modeling and simulation (ABMS) has attracted social scientists and demographers in the field of social simulation. Due to large number of computer simulation technologies, ABMS approaches have been proposed with majority applications. ABMS composed of heterogeneous interacting agents, with several features which turn them into a significantly attractive modeling approach to simulate complex social systems. In this paper, first we explore the underlying social theories for ABMS, its simulation and modeling techniques, and computational frameworks. Second, our paper concentrate on the potential need of ABM techniques in the context of social simulation. An alternative ABM approach that is getting popularity is to inject data into agent-based simulation. To validate our model, we compare our results with actual-data. Our results closely matched with actual-data results in the case of mean-age at the demographic transition of first childbirth. Our work encourage ABM modelers to promote this trend while designing their models. Further, our paper is an attempt to merge the microsimulation approach into the agent-based simulation through injecting data into ABM approach.

A Study on Policy Priorities for Implementing Big Data Analytics in the Social Security Sector : Adopting AHP Methodology

주제어 : 사회보장, 빅 데이터, 분석적계층화과정(AHP:Analytic Hierarchy Process), 부적정 수급, 사각지대 발굴 Abstract The primary purpose of this paper is to find out what issues are important in the Social Security sector, and then, through AHP methodology, this study analyzes what kind of big data methodologies and projects can be implemented to solves these issues. To the aim, this paper first confirmed 8 big data projects from reviewing all issues in the Social Security sector such as administrative works and social policies. After the result of pairwise comparison, policy validity is most important factors rather then effectiveness and practicability. With regard to the priorities among sub-big data projects, the project about preventing improper recipients has come out the most important project in terms of validity, effectiveness and practicability. And the results showed that the project about outreaching and reducing a blind spot on the welfare sector is weighed as a significant project. The results of this paper, in particular 8 sub-big data projects, will be useful to anyone who is interested in using big data and its methodologies for the social welfare sector.