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Journal articles on the topic 'Agent-based modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Johnson, Paul E. "Agent-Based Modeling." Social Science Computer Review 20, no. 2 (2002): 174–86. http://dx.doi.org/10.1177/089443930202000207.

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2

Nicholls, Sarah, Bas Amelung, and Jillian Student. "Agent-Based Modeling." Journal of Travel Research 56, no. 1 (2016): 3–15. http://dx.doi.org/10.1177/0047287515620490.

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Agent-based modeling (ABM) is a way of representing complex systems of autonomous agents or actors, and of simulating the multiple potential outcomes of these agents’ behaviors and interactions in the form of a range of alternatives or futures. Despite the complexity of the tourism system, and the power and flexibility of ABM to overcome the assumptions such as homogeneity, linearity, equilibrium, and rationality typical of traditional modeling techniques, ABM has received little attention from tourism researchers and practitioners. The purpose of this paper is to introduce ABM to a wider tour

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3

Jackson, Joshua Conrad, David Rand, Kevin Lewis, Michael I. Norton, and Kurt Gray. "Agent-Based Modeling." Social Psychological and Personality Science 8, no. 4 (2017): 387–95. http://dx.doi.org/10.1177/1948550617691100.

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Agent-based modeling is a long-standing but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions over time. ABMs are especially useful when investigating group behavior or evolutionary processes and can uniquely reveal nonlinear dynamics and emergence—the process whereby local interactions aggregate into often-surprising collective phenomena such

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4

Castiglione, Filippo. "Agent based modeling." Scholarpedia 1, no. 10 (2006): 1562. http://dx.doi.org/10.4249/scholarpedia.1562.

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5

McLean, Allen, Wade McDonald, Donna Goodridge, and Nathaniel Osgood. "Agent-Based Modeling." Nursing Research 68, no. 6 (2019): 473–82. http://dx.doi.org/10.1097/nnr.0000000000000390.

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6

Akram, Waseem, Muaz A. Niazi, Laszlo Barna Iantovics, and Athanasios V. Vassilakos. "TOWARDS AGENT-BASED MODEL SPECIFICATION OF SMART GRID: A COGNITIVE AGENT-BASED COMPUTING APPROACH." Interdisciplinary Description of Complex Systems 17, no. 3-B (2019): 546–85. https://doi.org/10.7906/indecs.17.3.14.

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A smart grid can be considered as a complex network where each node represents a generation unit or a consumer, whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling paradigm. The agent-based modeling is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the agent-based modeling paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of t

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El-Amine, Samar, Stephane Galland, and Abderraffiaa Koukam. "Agent–based Modeling of Social Behavioral Challenges in Transportation." Journal of Ubiquitous Systems and Pervasive Networks 10, no. 1 (2018): 33–40. http://dx.doi.org/10.5383/juspn.10.01.005.

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8

Batty, Michael. "Agent-Based Pedestrian Modeling." Environment and Planning B: Planning and Design 28, no. 3 (2001): 321–26. http://dx.doi.org/10.1068/b2803ed.

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9

Egorov, Andrey V. "EVIDENCE-BASED AGENT MODELING." SOFT MEASUREMENTS AND COMPUTING 2, no. 87 (2025): 13–23. https://doi.org/10.36871/2618-9976.2025.02.002.

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Within the article we present a novel methodology of constructing agentbased models (ABM) on the basis of evidencebased modeling concepts of G.B. Kleiner. We redefine modeling as finding a computable mapping between two essentially mathematical object (enclosing and enclosed ABMs). We then introduce correct models space – a fourdimensional lattice, where the axes correspond to compression, evolution, emergence and convolution operators (defined over various types of sets via close linkage). Within our paradigm moves every move along a given lattice dimension represents adding a corresponding o

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10

Wellman, Michael P. "Putting the agent in agent-based modeling." Autonomous Agents and Multi-Agent Systems 30, no. 6 (2016): 1175–89. http://dx.doi.org/10.1007/s10458-016-9336-6.

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11

Klügl, Franziska, and Ana L. C. Bazzan. "Agent-Based Modeling and Simulation." AI Magazine 33, no. 3 (2012): 29. http://dx.doi.org/10.1609/aimag.v33i3.2425.

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This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challeng

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Bissett, Keith R., Jose Cadena, Maleq Khan, and Chris J. Kuhlman. "Agent-Based Computational Epidemiological Modeling." Journal of the Indian Institute of Science 101, no. 3 (2021): 303–27. http://dx.doi.org/10.1007/s41745-021-00260-2.

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Bissett, Keith R., Jose Cadena, Maleq Khan, and Chris J. Kuhlman. "Agent-Based Computational Epidemiological Modeling." Journal of the Indian Institute of Science 101, no. 3 (2021): 303–27. http://dx.doi.org/10.1007/s41745-021-00260-2.

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14

Timchenko, I. E., E. E. Sovga, E. M. Igumnova, and S. M. Solodova. "Agent-Based Marine-Ecosystems Modeling." Physical Oceanography 13, no. 5 (2003): 299–312. http://dx.doi.org/10.1023/b:poce.0000009223.09931.27.

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15

Bankes, S. C. "Agent-based modeling: A revolution?" Proceedings of the National Academy of Sciences 99, Supplement 3 (2002): 7199–200. http://dx.doi.org/10.1073/pnas.072081299.

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16

Zhang, Le, Zhihui Wang, Jonathan A. Sagotsky, and Thomas S. Deisboeck. "Multiscale agent-based cancer modeling." Journal of Mathematical Biology 58, no. 4-5 (2008): 545–59. http://dx.doi.org/10.1007/s00285-008-0211-1.

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17

Birks, Daniel, Elizabeth R. Groff, and Nick Malleson. "Agent-Based Modeling in Criminology." Annual Review of Criminology 8, no. 1 (2025): 75–95. https://doi.org/10.1146/annurev-criminol-022222-033905.

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An agent-based model is a form of complex systems model that is capable of simulating how the micro-level behavior of individual system entities contributes to macro-level system outcomes. Researchers draw on theory and evidence to identify the key elements of a given system and specify behaviors of agents that simulate the individual entities of that system—be they cells, animals, or people. The model is then used to run simulations in which agents interact with one another and the resulting outcomes are observed. These models enable researchers to explore proposed causal explanations of real

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18

Tochukwu, Chiagunye, Inyiama Hyacinth, and Aguodoh Patrick C. "Agent-Based Modeling: Methods and Techniques for Scheduling Industrial Machine Operation." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 465–74. http://dx.doi.org/10.31142/ijtsrd7018.

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19

Makarov, Valery. "Agent-based modeling for a complex world. Part 2." Economics and the Mathematical Methods 58, no. 2 (2022): 7. http://dx.doi.org/10.31857/s042473880020009-8.

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The main goal of this paper is to summarize selected developments in the field of artificial societies and agent-based modeling and to suggest, how this fundamentally new toolkit can contribute to solving some of the most complex scientific and practical problems of our time. The entire field of agent-based modeling has expanded dramatically over the last quarter century, with applications across a remarkable array of fields, at scales ranging from molecular to global.The models described in this paper are a small part of worldwide scientific and practical developments in the field of agent-ba

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Makarov, Valery. "Agent-based modeling for a complex world. Part 1." Economics and the Mathematical Methods 58, no. 1 (2022): 5. http://dx.doi.org/10.31857/s042473880018970-6.

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The main goal of this paper is to summarize selected developments in the field of artificial societies and agent-based modeling and to suggest, how this fundamentally new toolkit can contribute to solving some of the most complex scientific and practical problems of our time. The entire field of agent-based modeling has expanded dramatically over the last quarter century, with applications across a remarkable array of fields, at scales ranging from molecular to global.The models described in this paper are a small part of worldwide scientific and practical developments in the field of agent-ba

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21

Banks, David L., and Mevin B. Hooten. "Statistical Challenges in Agent-Based Modeling." American Statistician 75, no. 3 (2021): 235–42. http://dx.doi.org/10.1080/00031305.2021.1900914.

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22

Ramazanov, Ruslan. "Agent-based modeling of competitive federalism." Artificial societies 14, no. 4 (2019): 0. http://dx.doi.org/10.18254/s207751800007785-2.

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23

Anantsuksomsri, Sutee, and Nij Tontisirin. "Agent-based Modeling and Disaster Management." Journal of Architectural/Planning Research and Studies (JARS) 10, no. 2 (2022): 1–14. http://dx.doi.org/10.56261/jars.v10i2.16697.

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Social simulation is usually used to analyze issues in social science and to study behaviors of people inspecific events. Unlike scientific experiments in other hard sciences, which can be tested in a closed environmentor in a laboratory, social simulation applies computation methods to examine social phenomena.In urban planning, understanding stakeholders—which include residents, businesses, factories, and localgovernments—is one of the important factorsin a successful project. In many cases, these stakeholders areheterogeneous individuals who may have different behaviors. Thus, to effectivel

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24

Fischbach, Kai, Johannes Marx, and Tim Weitzel. "Agent-based modeling in social sciences." Journal of Business Economics 91, no. 9 (2021): 1263–70. http://dx.doi.org/10.1007/s11573-021-01070-9.

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25

Gorokhovskyi, Semen, and Oleksandra Radziievska. "Agent-Based Modeling of Collaborative Work." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 60–63. http://dx.doi.org/10.18523/2617-3808.2021.4.60-63.

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In the modern world, it is no longer enough to simply create a product that performs its function, but it should perform it better than thousands of competitors. However, the problem is that human intellectual abilities are limited and many complex tasks are beyond the capabilities of a single person. The natural way of raising our intellectual level is to build teams to share our experience, knowledge, and worldview to create something beyond the capacity of the individual.Thus it is not surprising that according to a recent ranking, collaborative skills are considered most essential in the 2

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26

Wang, Kung-Jeng, and Chung-How Chou. "MODELING OF AGENT-BASED MANUFACTURING SYSTEMS." Journal of the Chinese Institute of Industrial Engineers 18, no. 1 (2001): 1–14. http://dx.doi.org/10.1080/10170660109509443.

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27

Axtell, Robert L., Clinton J. Andrews, and Mitchell J. Small. "Agent-Based Modeling and Industrial Ecology." Journal of Industrial Ecology 5, no. 4 (2001): 10–13. http://dx.doi.org/10.1162/10881980160084006.

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28

Richiardi, Matteo G. "The Future of Agent-Based Modeling." Eastern Economic Journal 43, no. 2 (2016): 271–87. http://dx.doi.org/10.1057/s41302-016-0075-9.

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29

Tesfatsion, Leigh. "Agent-based Modeling and Institutional Design." Eastern Economic Journal 37, no. 1 (2010): 13–19. http://dx.doi.org/10.1057/eej.2010.34.

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30

Abraham, Ralph. "Agent-Based Modeling of Growth Processes." Mind, Brain, and Education 8, no. 3 (2014): 115–31. http://dx.doi.org/10.1111/mbe.12045.

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31

Nissen, Mark E., and Raymond E. Levitt. "Agent-based modeling of knowledge dynamics." Knowledge Management Research & Practice 2, no. 3 (2004): 169–83. http://dx.doi.org/10.1057/palgrave.kmrp.8500039.

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32

Gonzalez de Durana, José María, Oscar Barambones, Enrique Kremers, and Liz Varga. "Agent based modeling of energy networks." Energy Conversion and Management 82 (June 2014): 308–19. http://dx.doi.org/10.1016/j.enconman.2014.03.018.

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33

Birbaumer, M., and F. Schweitzer. "Agent-based modeling of intracellular transport." European Physical Journal B 82, no. 3-4 (2011): 245–55. http://dx.doi.org/10.1140/epjb/e2011-20283-x.

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34

Heckbert, Scott, Tim Baynes, and Andrew Reeson. "Agent-based modeling in ecological economics." Annals of the New York Academy of Sciences 1185, no. 1 (2010): 39–53. http://dx.doi.org/10.1111/j.1749-6632.2009.05286.x.

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35

Falchuk, B., and A. Karmouch. "Visual modeling for agent-based applications." Computer 31, no. 12 (1998): 31–38. http://dx.doi.org/10.1109/2.735848.

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36

Evans, Tom P., and Steven Manson. "Space, Complexity, and Agent-Based Modeling." Environment and Planning B: Planning and Design 34, no. 2 (2007): 196–99. http://dx.doi.org/10.1068/b3402ed.

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37

B�l�ni,, L., D. C. Marinescu, J. R. Rice, P. Tsompanopoulou, and E. A. Vavalis. "Agent based scientific simulation and modeling." Concurrency: Practice and Experience 12, no. 9 (2000): 845–61. http://dx.doi.org/10.1002/1096-9128(20000810)12:9<845::aid-cpe494>3.0.co;2-j.

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38

Cosgrove, J., J. Butler, K. Alden, et al. "Agent-Based Modeling in Systems Pharmacology." CPT: Pharmacometrics & Systems Pharmacology 4, no. 11 (2015): 615–29. http://dx.doi.org/10.1002/psp4.12018.

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39

Yu, Songmin, and Zhanyi Hou. "Melodie: Agent-based Modeling in Python." Journal of Open Source Software 8, no. 83 (2023): 5100. http://dx.doi.org/10.21105/joss.05100.

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40

Ageeva, Alina. "Budget Processes Modeling: Agent-Based Approach." Artificial societies 18, no. 2 (2023): 0. http://dx.doi.org/10.18254/s207751800025645-8.

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The financial and economic crisis caused by anti-Russian sanctions and geopolitical tensions necessitates an increase in budget expenditures at all levels, in connection with which the issues of improving the fiscal system and reorganizing interbudgetary relations are being updated. To develop plans for adapting to current challenges, achieving the goals of fiscal stability and improving economic performance, transformations in the field of budget processes, the implementation of an effective fiscal policy, and a rethinking of the principles of fiscal federalism are necessary. Achieving this g

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41

Chiagunye, Tochukwu, Hyacinth Inyiama, and Patrick C. Aguodoh. "Agent Based Modeling Methods and Techniques for Scheduling Industrial Machine Operation." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 465–74. https://doi.org/10.31142/ijtsrd7018.

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Owing to its impact on the industrial economy, job shop scheduler and controller are vital algorithms for modern manufacturing processes. Hence a production scheduling and control that performs reactive not deterministic scheduling and can make decision on which job to process next based solely on its partial not central view of the plant becomes necessary. This requirement puts the problem in the class of agent based model ABM . Hence this work adopts an alternative view on job shop scheduling problem where each resource is equipped with adaptive agent that, independent of other agents makes

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42

Gorodnichev, Mikhail. "Simulation model of traffic flow based on agent-based modeling." Heritage and Sustainable Development 4, no. 2 (2022): 195–90. http://dx.doi.org/10.37868/hsd.v4i2.149.

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The work is devoted to traffic flow simulation using agent-based modeling using distributed computing. The role of modeling traffic flows in the modern world is considered. The types of traffic flow modeling are considered. The developed software makes it possible to simulate the movement and interaction of vehicles.

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43

Bragin, Alexey. "Modern Software tools for Agent-based modeling." Artificial societies 17, no. 4 (2022): 0. http://dx.doi.org/10.18254/s207751800023501-0.

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The article describes the current state of the industry of software tools for creating agent-based models, conducting agent-based simulation and processing the results of such modeling. Despite the large number of articles on various problems in the field of agent-based modeling and reviews of some software tools, only one comprehensive English-language work on this issue was published in 2017. Publications in recent years do not fully reflect the state of the industry, there are practically no reviews in Russian. This work is devoted to a review of agent-based and simulation modeling tools wi

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44

Gao, Wei, and Hui Min Huang. "Agent-Based Modeling of Gates Assignment System." Applied Mechanics and Materials 235 (November 2012): 368–72. http://dx.doi.org/10.4028/www.scientific.net/amm.235.368.

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The optimization of Gate Assignment System is to reduce flight delays and improve the operation efficiency of airports, due to many complex factors and vulnerably sudden situation. Through analyzing the working process of gates assignment system and the rules of Agent technology, Agent concept is introduced into Gate Assignment System and a modeling architecture of Agent-based system is put forward, as well as the structure and function of each Agent is declared and the communication and collaboration mechanism among Agents is recommended. Additionally, in the framework construction, the Agent

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45

Singh, Karandeep, Chang-Won Ahn, Euihyun Paik, Jang Won Bae, and Chun-Hee Lee. "A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling." Artificial Life 24, no. 2 (2018): 128–48. http://dx.doi.org/10.1162/artl_a_00260.

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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

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46

Shaaban, Khaled, and Karim Abdelwarith. "Pedestrian Attribute Analysis Using Agent-Based Modeling." Applied Sciences 10, no. 14 (2020): 4882. http://dx.doi.org/10.3390/app10144882.

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Crossing a road outside of a crosswalk is a major cause of pedestrian fatalities. The aim of this study was to investigate this type of behavior for different pedestrian attributes in terms of risk and gap acceptance using agent-based modeling techniques. An agent-based model was developed and tested to represent pedestrian behavior in different situations. Different pedestrian attributes were analyzed, including gender, age, type of clothing, carrying bags, using mobile phones, and crossing in a group. The results showed that pedestrians add a positive risk factor to the speed of approaching

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47

Michael P. Wellman and Elaine Wah. "Strategic Agent-Based Modeling of Financial Markets." RSF: The Russell Sage Foundation Journal of the Social Sciences 3, no. 1 (2017): 104. http://dx.doi.org/10.7758/rsf.2017.3.1.06.

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48

Xie, Rong, and Yan Zhang. "Agent-Based Crowd Evacuation Modeling in Buildings." Applied Mechanics and Materials 411-414 (September 2013): 2639–42. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2639.

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Crowd evacuation in large and complex buildings is becoming a challenging direction in the field of emergency management. In the paper, we present an agent-based approach to modeling crowd evacuation, including navigator model and crowd movement model. Visualization results show these models can access to realistic evacuation process and achieve the goal of rapid evacuation.

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49

Abdullah, Azween, and Ramachandran Ponnan. "Modeling State Dependency in Agent Based Systems." British Journal of Applied Science & Technology 19, no. 5 (2017): 1–7. http://dx.doi.org/10.9734/bjast/2017/31759.

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50

Rashidova, E. A. "Agent-Based Modeling of Wholesale Electricity Market." WORLD OF ECONOMICS AND MANAGEMENT 16, no. 1 (2017): 70–85. http://dx.doi.org/10.25205/2542-0429-2017-17-1-70-85.

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