Articoli di riviste: "Agent-based modeling"

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Johnson, Paul E. "Agent-Based Modeling". Social Science Computer Review 20, n. 2 (maggio 2002): 174–86. http://dx.doi.org/10.1177/089443930202000207.

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Nicholls, Sarah, Bas Amelung e Jillian Student. "Agent-Based Modeling". Journal of Travel Research 56, n. 1 (4 agosto 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 tourism audience. Specifically, the appropriateness of tourism as a phenomenon to be subjected to ABM is established; the power and benefits of ABM as an alternative scientific mechanism are illuminated; the few existing applications of ABM in the tourism arena are summarized; and, a range of potential applications in the areas of tourism planning, development, marketing and management is proposed.

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Jackson, Joshua Conrad, David Rand, Kevin Lewis, Michael I. Norton e Kurt Gray. "Agent-Based Modeling". Social Psychological and Personality Science 8, n. 4 (13 marzo 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 as spatial segregation and relational homophily. We review several illustrative ABMs, describe the strengths and limitations of this method, and address two misconceptions about ABMs: reductionism and “you get out what you put in.” We also offer maxims for good and bad ABMs, give practical tips for beginner modelers, and include a list of resources and other models. We conclude with a seven-step guide to creating your own model.

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Castiglione, Filippo. "Agent based modeling". Scholarpedia 1, n. 10 (2006): 1562. http://dx.doi.org/10.4249/scholarpedia.1562.

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McLean, Allen, Wade McDonald, Donna Goodridge e Nathaniel Osgood. "Agent-Based Modeling". Nursing Research 68, n. 6 (2019): 473–82. http://dx.doi.org/10.1097/nnr.0000000000000390.

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El-Amine, Samar, Stephane Galland e Abderraffiaa Koukam. "Agent–based Modeling of Social Behavioral Challenges in Transportation". Journal of Ubiquitous Systems and Pervasive Networks 10, n. 1 (7 marzo 2018): 33–40. http://dx.doi.org/10.5383/juspn.10.01.005.

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Batty, Michael. "Agent-Based Pedestrian Modeling". Environment and Planning B: Planning and Design 28, n. 3 (giugno 2001): 321–26. http://dx.doi.org/10.1068/b2803ed.

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Wellman, Michael P. "Putting the agent in agent-based modeling". Autonomous Agents and Multi-Agent Systems 30, n. 6 (13 aprile 2016): 1175–89. http://dx.doi.org/10.1007/s10458-016-9336-6.

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Klügl, Franziska, e Ana L. C. Bazzan. "Agent-Based Modeling and Simulation". AI Magazine 33, n. 3 (20 settembre 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 challenges is given.

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Bissett, Keith R., Jose Cadena, Maleq Khan e Chris J. Kuhlman. "Agent-Based Computational Epidemiological Modeling". Journal of the Indian Institute of Science 101, n. 3 (luglio 2021): 303–27. http://dx.doi.org/10.1007/s41745-021-00260-2.

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Bissett, Keith R., Jose Cadena, Maleq Khan e Chris J. Kuhlman. "Agent-Based Computational Epidemiological Modeling". Journal of the Indian Institute of Science 101, n. 3 (luglio 2021): 303–27. http://dx.doi.org/10.1007/s41745-021-00260-2.

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Timchenko, I. E., E. E. Sovga, E. M. Igumnova e S. M. Solodova. "Agent-Based Marine-Ecosystems Modeling". Physical Oceanography 13, n. 5 (settembre 2003): 299–312. http://dx.doi.org/10.1023/b:poce.0000009223.09931.27.

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Bankes, S. C. "Agent-based modeling: A revolution?" Proceedings of the National Academy of Sciences 99, Supplement 3 (7 maggio 2002): 7199–200. http://dx.doi.org/10.1073/pnas.072081299.

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Zhang, Le, Zhihui Wang, Jonathan A. Sagotsky e Thomas S. Deisboeck. "Multiscale agent-based cancer modeling". Journal of Mathematical Biology 58, n. 4-5 (12 settembre 2008): 545–59. http://dx.doi.org/10.1007/s00285-008-0211-1.

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Tochukwu, Chiagunye, Inyiama Hyacinth e 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 (31 dicembre 2017): 465–74. http://dx.doi.org/10.31142/ijtsrd7018.

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Banks, David L., e Mevin B. Hooten. "Statistical Challenges in Agent-Based Modeling". American Statistician 75, n. 3 (22 aprile 2021): 235–42. http://dx.doi.org/10.1080/00031305.2021.1900914.

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Ramazanov, Ruslan. "Agent-based modeling of competitive federalism". Artificial societies 14, n. 4 (2019): 0. http://dx.doi.org/10.18254/s207751800007785-2.

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Anantsuksomsri, Sutee, e Nij Tontisirin. "Agent-based Modeling and Disaster Management". Journal of Architectural/Planning Research and Studies (JARS) 10, n. 2 (7 agosto 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 effectively solve issues in urban planning,planners need to understand stakeholders. Agent-based modeling (ABM) is widely used to analyze behaviors ofstakeholders under implementation of urban policies, especially in the events of natural disasters, which areconsidered as complex systems. In these analyses, spatial structures of affected areason which stakeholdersare located and interact are a crucial ground of ABM. Together with the development of geographic informationsystems (GIS), the database systems and analyses of ABM become more accurate and reliable, especially onphenomena with the complexity of spatial structures.This review article explains the development and definition of ABM and introduces software and toolkitsfor building an agent-based model, as well as reviews articles and research that use ABM to analyze the issuesin theoretical testing and urban planning. Schelling’s Segregation and Hotelling’s Law models are discussed asexamples of theoretical testing while robbery and driving behavior models are selected as the implications ofABM in urban planning. This article also focuses on the use of ABM on natural disaster policies and managementusing case studies of Japan and the United Kingdom.

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Fischbach, Kai, Johannes Marx e Tim Weitzel. "Agent-based modeling in social sciences". Journal of Business Economics 91, n. 9 (novembre 2021): 1263–70. http://dx.doi.org/10.1007/s11573-021-01070-9.

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Gorokhovskyi, Semen, e Oleksandra Radziievska. "Agent-Based Modeling of Collaborative Work". NaUKMA Research Papers. Computer Science 4 (10 dicembre 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 21st century [2]. To cope with all challenges and create high-quality products, there should be a team whose members are experts in communication, discussion, problem-solving, and critical thinking. In addition, it is important to manage the team effectively. To do so, it is necessary to know more about the social processes which take place inside a team. Agent-based modeling can be an effective tool to gain such insights.Agent-based modeling is a powerful instrument for simulating different processes, including social ones. This technology was formed under the influence of many other fields such as artificial intelligence, sociology, game theory, and so on.In this article, a model which simulates human interaction in the framework of «Wilderness Survival: A Consensus-Seeking Task» is used to demonstrate the core principle of agent-based modeling. The group of agents complete a test by themselves and afterwards discuss their answers to reach a consensus and achieve the best score.It will be analyzed which human character traits are more important for successful collaborative work. Situations in which some team members are not interested in the team success will be identified. Also, a user interface is provided to enable running custom experiments to better understand how specific character traits impact the team results.

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Wang, Kung-Jeng, e Chung-How Chou. "MODELING OF AGENT-BASED MANUFACTURING SYSTEMS". Journal of the Chinese Institute of Industrial Engineers 18, n. 1 (gennaio 2001): 1–14. http://dx.doi.org/10.1080/10170660109509443.

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Axtell, Robert L., Clinton J. Andrews e Mitchell J. Small. "Agent-Based Modeling and Industrial Ecology". Journal of Industrial Ecology 5, n. 4 (settembre 2001): 10–13. http://dx.doi.org/10.1162/10881980160084006.

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Richiardi, Matteo G. "The Future of Agent-Based Modeling". Eastern Economic Journal 43, n. 2 (18 ottobre 2016): 271–87. http://dx.doi.org/10.1057/s41302-016-0075-9.

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Tesfatsion, Leigh. "Agent-based Modeling and Institutional Design". Eastern Economic Journal 37, n. 1 (28 dicembre 2010): 13–19. http://dx.doi.org/10.1057/eej.2010.34.

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Abraham, Ralph. "Agent-Based Modeling of Growth Processes". Mind, Brain, and Education 8, n. 3 (18 agosto 2014): 115–31. http://dx.doi.org/10.1111/mbe.12045.

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Nissen, Mark E., e Raymond E. Levitt. "Agent-based modeling of knowledge dynamics". Knowledge Management Research & Practice 2, n. 3 (dicembre 2004): 169–83. http://dx.doi.org/10.1057/palgrave.kmrp.8500039.

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

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Birbaumer, M., e F. Schweitzer. "Agent-based modeling of intracellular transport". European Physical Journal B 82, n. 3-4 (agosto 2011): 245–55. http://dx.doi.org/10.1140/epjb/e2011-20283-x.

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Heckbert, Scott, Tim Baynes e Andrew Reeson. "Agent-based modeling in ecological economics". Annals of the New York Academy of Sciences 1185, n. 1 (gennaio 2010): 39–53. http://dx.doi.org/10.1111/j.1749-6632.2009.05286.x.

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Falchuk, B., e A. Karmouch. "Visual modeling for agent-based applications". Computer 31, n. 12 (1998): 31–38. http://dx.doi.org/10.1109/2.735848.

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Evans, Tom P., e Steven Manson. "Space, Complexity, and Agent-Based Modeling". Environment and Planning B: Planning and Design 34, n. 2 (aprile 2007): 196–99. http://dx.doi.org/10.1068/b3402ed.

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B�l�ni,, L., D. C. Marinescu, J. R. Rice, P. Tsompanopoulou e E. A. Vavalis. "Agent based scientific simulation and modeling". Concurrency: Practice and Experience 12, n. 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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Cosgrove, J., J. Butler, K. Alden, M. Read, V. Kumar, L. Cucurull-Sanchez, J. Timmis e M. Coles. "Agent-Based Modeling in Systems Pharmacology". CPT: Pharmacometrics & Systems Pharmacology 4, n. 11 (novembre 2015): 615–29. http://dx.doi.org/10.1002/psp4.12018.

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Yu, Songmin, e Zhanyi Hou. "Melodie: Agent-based Modeling in Python". Journal of Open Source Software 8, n. 83 (26 marzo 2023): 5100. http://dx.doi.org/10.21105/joss.05100.

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Ageeva, Alina. "Budget Processes Modeling: Agent-Based Approach". Artificial societies 18, n. 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 goals predetermines the need to use flexible budget analysis tools, such as agent-based modeling, which allows to reproduce the multi-level structure of the country's economy and budget system, as well as simulate complex budget processes. The article presents an overview of agent-based models developed by foreign and Russian researchers that reproduce various aspects of budgetary processes. The analysis of the architecture of models was carried out and the conceptual ideas of building models in accordance with the types of agents, their behavioral algorithms, as well as the goals of simulation modeling were considered. The expediency and efficiency of using the agent-based approach for modeling complex budgetary processes is substantiated. The analysis carried out in the article allows us to conclude that the agent-based simulation approach is successfully used for the following purposes: testing variable strategies for reforming budgetary processes, predicting the consequences of implementing fiscal policy, choosing the optimal strategy aimed at achieving budgetary self-sufficiency of the regions, as well as creating tools for assessing the effective allocation of budgetary funds to achieve target variables. The results of the study are proposed to be used for agent-based modeling of sanctions effects and search for strategies for reforming the fiscal system in order to counter the financial and economic crisis.

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Makarov, Valery. "Agent-based modeling for a complex world. Part 2". Economics and the Mathematical Methods 58, n. 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-based modelling and related areas. We have attempted to give an impression of the vast range of application areas (epidemiology, economics, demography, environment, urban dynamics, history, conflict, disaster preparedness), scales (from cellular to local to urban to planetary), and goals (simple exploratory models, optimization, generative explanation, forecasting, policy) of agent-based modeling. Agent-based models offer a new and powerful alternative, or complement, to traditional mathematical methods for addressing complex challenges.

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Makarov, Valery. "Agent-based modeling for a complex world. Part 1". Economics and the Mathematical Methods 58, n. 1 (2022): 5. http://dx.doi.org/10.31857/s042473880018970-6.

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Abstract (sommario):

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-based modelling and related areas. We have attempted to give an impression of the vast range of application areas (epidemiology, economics, demography, environment, urban dynamics, history, conflict, disaster preparedness), scales (from cellular to local to urban to planetary), and goals (simple exploratory models, optimization, generative explanation, forecasting, policy) of agent-based modeling. Agent-based models offer a new and powerful alternative, or complement, to traditional mathematical methods for addressing complex challenges.

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Bragin, Alexey. "Modern Software tools for Agent-based modeling". Artificial societies 17, n. 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 with a brief description of them.

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Gorodnichev, Mikhail. "Simulation model of traffic flow based on agent-based modeling". Heritage and Sustainable Development 4, n. 2 (30 dicembre 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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Shaaban, Khaled, e Karim Abdelwarith. "Pedestrian Attribute Analysis Using Agent-Based Modeling". Applied Sciences 10, n. 14 (16 luglio 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 vehicles before evaluating a gap, then proceed with the crossing decision. The factor for the female pedestrians was smaller in comparison to their male counterparts, which may infer that they are more prone to taking risks during crossing compared to male pedestrians. Another interpretation can be that they have a better ability to discern vehicle speeds and thus a better assessment of the critical gap. Compared to pedestrians crossing individually, the factor was smaller for pedestrians crossing in a group, which can be an indication that pedestrians have a higher sense of safety when crossing as a group. Moreover, the analysis suggested that there is no difference in perception between old and middle-age pedestrians, pedestrians carrying bags or not, and pedestrians using a mobile phone while crossing or not. These results can be useful in evaluating pedestrian safety at midblock crossings and providing a framework for modeling this type of behavior in simulation models.

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Michael P. Wellman e Elaine Wah. "Strategic Agent-Based Modeling of Financial Markets". RSF: The Russell Sage Foundation Journal of the Social Sciences 3, n. 1 (2017): 104. http://dx.doi.org/10.7758/rsf.2017.3.1.06.

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Xie, Rong, e Yan Zhang. "Agent-Based Crowd Evacuation Modeling in Buildings". Applied Mechanics and Materials 411-414 (settembre 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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Abdullah, Azween, e Ramachandran Ponnan. "Modeling State Dependency in Agent Based Systems". British Journal of Applied Science & Technology 19, n. 5 (10 gennaio 2017): 1–7. http://dx.doi.org/10.9734/bjast/2017/31759.

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Rashidova, E. A. "Agent-Based Modeling of Wholesale Electricity Market". WORLD OF ECONOMICS AND MANAGEMENT 16, n. 1 (2017): 70–85. http://dx.doi.org/10.25205/2542-0429-2017-17-1-70-85.

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Jones, Gregory Todd. "AGENT-BASED MODELING: USE WITH NECESSARY CAUTION". American Journal of Public Health 97, n. 5 (maggio 2007): 780–81. http://dx.doi.org/10.2105/ajph.2006.109058.

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Wan, Shan Shan, Dong Liang Wang e Qing Cao. "Multi-Agent Based Modeling Simulation about VANET". Advanced Materials Research 760-762 (settembre 2013): 680–84. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.680.

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The self-organization characteristics and the interaction between a large numbers of self-organizing vehicles are complexity, to obtain a more accurate model of vehicular Ad-hoc network (VANET) and obtain a more profound comprehension of the complex behavior working mechanism of the vehicle in the VANET environment multi-agent based and bottom-up modeling approach is proposed here. It aims to describe the dynamics of VANET caused by the different behaviors of vehicular. The simulation tool for vehicular misbehaviors is developed with multi-agent. It aims to and be able to effectively reproduce the real VANET scene. Though the multi-agent based modeling the emergent behavior and sudden existing behaviors of VANET entities are well reflected.

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Stieler, David, Tobias Schwinn, Samuel Leder, Mathias Maierhofer, Fabian Kannenberg e Achim Menges. "Agent-based modeling and simulation in architecture". Automation in Construction 141 (settembre 2022): 104426. http://dx.doi.org/10.1016/j.autcon.2022.104426.

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Tzvetanov, MS, Krassimir, Austin Riegsecker, MS, Braiden Frantz, MS, PhD, Chenxi Xiong, Robert Bott, PhD, Travis Cline, MS, PhD, Ben Dubiel e J. Eric Dietz, PhD, PE. "Agent-based modeling for theme park evacuation". Journal of Emergency Management 20, n. 2 (1 marzo 2022): 157–73. http://dx.doi.org/10.5055/jem.0561.

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Each year theme parks can see up to 20 million patrons, but often little effort is put into planning for an emergency evacuation. In this study, we built a multiagent simulation model using AnyLogic® 8.5.1. The model was based on a preliminary design of a theme park provided by AOA Builds, Orlando.This research had two goals: the first was to compare evacuation time when the park is full (1) using only the main guest gate and (2) using all seven available exits. The second goal was to model first responder response time between various start and end locations within the park.Using only the main gate, evacuation took an average of 14 minutes and 51 seconds. Using all seven gates results in an average evacuation time of 11 minutes and 58 seconds. This was due to a gate being overwhelmed causing a delay in overall evacuation time. If that gate is not included in the calculation, the average evacuation time drops to 6 minutes and 44 seconds.For the purpose of measuring response times, four starting locations were chosen with the guidance of a subject matter expert. These locations included response teams positioned at the front gate, at a police station, at the service area behind a main attraction, and mobile patrol walking around the park. Based on our testing, walking around the park was the best option in terms of response time, using the main gate was 53.7 percent faster than other options and, using all seven gates, was 60.7 percent faster during an evacuation using all seven exits.

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Tsyplakov, Alexander A., e Larisa V. Melnikova. "Fixed Investments and Macroeconomic Agent-Based Modeling". World of Economics and Management 21, n. 1 (2021): 5–28. http://dx.doi.org/10.25205/2542-0429-2021-21-1-5-28.

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The significant progress observed in the field of artificial economy opens up new possibilities for modeling economic growth. Agent-based models (ABM) allow leaving the concept of a representative agent in the past and linking investment decisions of economic agents at the micro level with long-term macroeconomic growth. Modern ABMs offer new algorithms for modeling expectations, agent interaction, technical progress, pricing, and production planning. Our article analyzes the current state of modeling investment in fixed assets in operating macroeconomic ABMs. The subject of the review is the families of models Eurace, CATS, KS, Jamel, Lagom. The authors also present the investment block of the agent-based multiregional input-output model (ABMIOM) being developed. Comparative analysis demonstrates that modern ABMs, as a rule, implement the principle of stock-flow consistency. Modeling the investment process requires detailing the commodity nomenclature, so that the initially adopted two-sector division into investment and consumer goods is replaced by more detailed structures, which gives rise to the problem of accounting for inter-sectoral relations in production and consumption. The Leontief production function copes with this problem, which is confirmed by its widespread use in ABM. The size of firms' investments is often derived from the need to expand capacity in accordance with the current production plan, so that planning turns out to be myopic, and long-term aspects in ABM are still largely unrealized. Nevertheless, already now ABMs reproduce many phenomena associated with the economic cycle. The developed ABMIOM provides horizontal consistency of cash flows between agents and analysis of results using input-output tables. ABMIOM represents a step forward in reflecting intersectoral and interregional flows. The model reproduces the growth and contraction of the economy as a result of independent investment decisions of individual firms and households, which is reflected in the sectoral and spatial structure of the economy. Further development of ABMIOM is associated with the modeling of savings, intrafirm finance, money market, innovation and technical progress.

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Conesa, Julián, Jorge D. Camba, José Ángel Aranda e Manuel Contero. "An agent-based paradigm for virtual modeling". Expert Systems with Applications 192 (aprile 2022): 116393. http://dx.doi.org/10.1016/j.eswa.2021.116393.

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