{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:27:09Z","timestamp":1761395229232},"reference-count":36,"publisher":"MIT Press - Journals","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Artificial Life"],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:p> 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 \u201csoft,\u201d 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. <\/jats:p>","DOI":"10.1162\/artl_a_00260","type":"journal-article","created":{"date-parts":[[2018,4,17]],"date-time":"2018-04-17T15:45:27Z","timestamp":1523979927000},"page":"128-148","source":"Crossref","is-referenced-by-count":3,"title":["A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling"],"prefix":"10.1162","volume":"24","author":[{"given":"Karandeep","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Computer Software, Korea University of Science & Technology (UST); and Smart Data Research Group, SW-Content Research Laboratory, Electronics & Telecommunications Research Institute (ETRI)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-Won","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Computer Software, Korea University of Science & Technology (UST); and Smart Data Research Group, SW-Content Research Laboratory, Electronics & Telecommunications Research Institute (ETRI)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Euihyun","family":"Paik","sequence":"additional","affiliation":[{"name":"Smart Data Research Group, SW-Content Research Laboratory, Electronics & Telecommunications Research Institute (ETRI)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jang Won","family":"Bae","sequence":"additional","affiliation":[{"name":"Smart Data Research Group, SW-Content Research Laboratory, Electronics & Telecommunications Research Institute (ETRI)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Hee","family":"Lee","sequence":"additional","affiliation":[{"name":"Smart Data Research Group, SW-Content Research Laboratory, Electronics & Telecommunications Research Institute (ETRI)."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"bib1","first-page":"93","volume-title":"Proceedings of the 2008 Workshop on Complex Systems Modelling and Simulation","author":"Andrews P. 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