Relevant bibliographies by topics / Agent-based modeling

Academic literature on the topic 'Agent-based modeling'

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Published: 4 June 2021
Last updated: 20 July 2024

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Journal articles on the topic "Agent-based modeling"

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

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Nicholls, Sarah, Bas Amelung, and Jillian Student. "Agent-Based Modeling." Journal of Travel Research 56, no. 1 (August 4, 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, and Kurt Gray. "Agent-Based Modeling." Social Psychological and Personality Science 8, no. 4 (March 13, 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, no. 10 (2006): 1562. http://dx.doi.org/10.4249/scholarpedia.1562.

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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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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 (March 7, 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, no. 3 (June 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, no. 6 (April 13, 2016): 1175–89. http://dx.doi.org/10.1007/s10458-016-9336-6.

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Klügl, Franziska, and Ana L. C. Bazzan. "Agent-Based Modeling and Simulation." AI Magazine 33, no. 3 (September 20, 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, and Chris J. Kuhlman. "Agent-Based Computational Epidemiological Modeling." Journal of the Indian Institute of Science 101, no. 3 (July 2021): 303–27. http://dx.doi.org/10.1007/s41745-021-00260-2.

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Dissertations / Theses on the topic "Agent-based modeling"

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Cao, Sen. "Role-based and agent-oriented teamwork modeling." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2540.

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Teamwork has become increasingly important in many disciplines. To support teamwork in dynamic and complex domains, a teamwork programming language and a teamwork architecture are important for specifying the knowledge of teamwork and for interpreting the knowledge of teamwork and then driving agents to interact with the domains. Psychological studies on teamwork have also shown that team members in an effective team often maintain shared mental models so that they can have mutual expectation on each other. However, existing agent/teamwork programming languages cannot explicitly express the mental states underlying teamwork, and existing representation of the shared mental models are inefficient and further become an obstacle to support effective teamwork. To address these issues, we have developed a teamwork programming language called Role-Based MALLET (RoB-MALLET) which has rich expressivity to explicitly specify the mental states underlying teamwork. By using roles and role variables, the knowledge of team processes is specified in terms of conceptual notions, instead of specific agents and agent variables, allowing joint intentions to be formed and this knowledge to be reused by different teams of agents. Further, based on roles and role variables, we have developed mechanisms of task decomposition and task delegation, by which the knowledge of a team process is decomposed into the knowledge of a team process for individuals and then delegate it to agents. We have also developed an efficient representation of shared mental models called Role-Based Shared Mental Model (RoB-SMM) by which agents only maintain individual processes complementary with others?? individual process and a low level of overlapping called team organizations. Based on RoB-SMMs, we have developed tworeasoning mechanisms to improve team performance, including Role-Based Proactive Information Exchange (RoB-PIE) and Role-Based Proactive Helping Behaivors (RoBPHB). Through RoB-PIE, agents can anticipate other agents?? information needs and proactively exchange information with them. Through RoB-PHB, agents can identify other agents?? help needs and proactively initialize actions to help them. Our experiments have shown that RoB-MALLET is flexible in specifying reusable plans, RoB-SMMs is efficient in supporting effective teamwork, and RoB-PHB improves team performance.
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NOYA, RICARDO CHOREN. "A MODELING LANGUAGE FOR AGENT BASED SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5304@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A tecnologia de agentes tem ganho destaque, tanto na academia quanto na indústria, no que se refere ao desenvolvimento de sistemas distribuídos. Linguagens de modelagem e processos de desenvolvimento foram criados para documentar e formalizar o desenvolvimento de sistemas baseados em agentes. Entretanto, as linguagens de modelagem existentes ou se baseiam em noções de uma tecnologia de desenvolvimento anterior (orientação a objetos) ou não possuem artefatos (modelos) que denotam as características de agência existentes no sistema. Esta tese propõe uma linguagem de modelagem para sistemas baseados em agentes, a LM-SMA, que gera artefatos (modelos) que mostram a modelagem de aspectos de agência, tais como adaptação, aprendizado, interação e autonomia. A LM-SMA ainda possui artefatos que permitem a modelagem da parte do sistema que não é composta por agentes, por meio de ontologias.
The agent technology is gaining acceptance, both in academy and industry, with regards to distributed systems development. Modeling languages and development processes were created to formalize the development of agent based systems. Nevertheless, existing modeling languages are either based on previous development methods (object oriented) or they do not have artifacts (models) that show the agency characteristics that exist in a system. This thesis proposes a modeling language, for agent based systems, that generates artifacts that model agency aspects, such as adaptation, learning, interaction and autonomy. The language has artifacts that allow the modeling of the non- agent part of an agent based system, using ontology.
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Sapkota, Pratibha. "Modeling Diffusion Using an Agent-Based Approach." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1270659453.

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VALLURUPALLI, VAISHALI. "AGENT BASED MODELING OF A BIO-INVERTER." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172264092.

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Dickie, Alistair James. "Modeling robot swarms using agent-based simulation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FDickie.pdf.

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Hajhashemi, Elham. "Agent-based Modeling for Recovery Planning after Hurricane Sandy." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85012.

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Hurricane Sandy hit New York City on October 29, 2012 and greatly disrupted transportation systems, power systems, work, and schools. This research used survey data from 397 respondents in the NYC Metropolitan Area to develop an agent-based model for capturing commuter behavior and adaptation after the disruption. Six different recovery scenarios were tested to find which systems are more critical to recover first to promote a faster return to productivity. Important factors in the restoration timelines depends on the normal commuting pattern of people in that area. In the NYC Metropolitan Area, transit is one of the common modes of transportation; therefore, it was found that the subway/rail system recovery is the top factor in returning to productivity. When the subway/rail system recovers earlier (with the associated power), more people are able to travel to work and be productive. The second important factor is school and daycare closure (with the associated power and water systems). Parents cannot travel unless they can find a caregiver for their children, even if the transportation system is functional. Therefore, policy makers should consider daycare and school condition as one of the important factors in recovery planning. The next most effective scenario is power restoration. Telework is a good substitute for the physical movement of people to work. By teleworking, people are productive while they skip using the disrupted transportation system. To telework, people need power and communication systems. Therefore, accelerating power restoration and encouraging companies to let their employees' telework can promote a faster return to productivity. Finally, the restoration of major crossings like bridges and tunnels is effective in the recovery process.
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Zhou, Feng. "Application of agent based modeling to insurance cycles." Thesis, City University London, 2013. http://openaccess.city.ac.uk/12195/.

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Traditional models of analyzing the general insurance market often focus on the behavior of a single insurer in a competitive market. They assume that the major players in this market are homogeneous and have a common goal to achieve a same long-term business objective, such as solving profit (or utility) maximization. Therefore these individual players in the traditional models can be implemented as a single representative economic agent with full rationality to solve the utility optimization. To investigate insurance pricing (or underwriting) cycles, the existing literature attempts to model various isolated aspects of the market, keeping other factors exogenous. We and that a multi-agent system describing an insurance market affords a helpful understanding of the dynamic interactions of individual agents that is a complementary to the traditional models. Such agent-based models (ABM) try to capture the complexity of the real world. Thus, economic agents are heterogeneous and follow divergent behavioral rules depending on their current unique competitive situations or comparative advantages relating to, for example, their existing market shares, distribution channels, information processes and product differentiations. The real-world continually-evolving environment leads agents to follow common rules of thumb to implement their business strategies, rather than completely be utility-maximizer with perfect foresight in an idealized world. The agents are adaptively learning from their local competition over time. In fact, the insurance cycles are the results of these dynamic interactions of agents in such complex system.
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Vuorinen, T. (Tapio). "Simulating project network governance using agent based modeling." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201612023187.

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Project management literature has previously mainly focused on the technical aspects of project delivery. Less focus has been on the complex inter-connected relations between different organizational and individual actors in project networks. One of the newer approaches to project management research is project network governance. Its aim is to provide a framework for controlling, rewarding and enabling collaboration between organizational actors in project networks. Project management literature has shied away from using simulation as a research method for various reasons. However, at some point in the future problems can become increasingly complex and traditional research methods can be ineffective. Additionally, to research different phenomena regarding complex networks, be it between organizations, actors or project alliances, traditional research methods can fail altogether due to sheer scope. This is where simulation can come in. Discrete event, system dynamics and agent based simulation are the most used paradigms today. Three of the most prevalent are discrete event, system dynamics and agent based modeling. Agent based simulation is a powerful tool for discovering emergent behavior using autonomous agents. Agent based modeling relies on the different characteristics of agents — learning ability, number of connections to other agents and individual behavioral guidelines. This approach enables modeling of emergent behavior without knowledge of exact processes or structures — the agents can be made capable of creating all of these, even with simple algorithms. In project management context, these agents can be individual persons, organizations or projects. The levels and layers of detail are left for the modeler. An agent based model could be about a set of inter-connected organizations working on a project based on different inputs. The organizations could comprise different individual actors with their own volition. Project could consist of different inter-connected tasks with varying requirements and goals. The ensuing outcome of the project could be for example be influenced by different project network governance mechanisms. That is what this thesis is about. The goal of the thesis was to develop a simulation model which could be used to simulate project network governance. Following research questions were answered: RQ1: What are the most significant characteristics of project network and task network? RQ2: What are different mechanisms of project network governance? RQ3: What are different stages in the simulation model development? RQ4: What are the strengths and weaknesses of different modeling paradigms in simulating project networks? An agent based model was successfully developed using Anylogic software and following the simulation model development process. It was verified that agent based modeling can be used to simulate project networks and project network governance. Next step would be to validate the results using inputs gathered from real world dataset. The developed model could also be further enhanced by implementing more project network governance mechanisms or including multiple concurrent projects and resource constraints. Agent based modeling provides a powerful platform for experimenting and exploring
Projektikirjallisuus on perinteisesti tarkastellut lähinnä projektien teknistä toteutusta. Vähemmän voimavaroja on kohdistettu kompleksisten projektiverkostojen sekä organisaatioden ja yksilöiden välillä olevien suhteiden tutkimukseen. Yksi projektimaailman uusimmista tutkimusalueista on projektiverkoston hallinta. Sen tarkoitus on tuottaa viitekehys, jolla projektiverkostoa voidaan esimerkiksi kontrolloida, palkita ja projektiverkoston osallistujien välistä yhteistyötä parantaa. Projektitutkimus on myös karttanut simulaatioiden käyttämistä tutkimusmetodina monestakin eri syystä. On kuitenkin todettava, että tulevaisuudessa ongelmat saattavat muuttua yhä monimutkaisimmiksi, jolloin perinteisemmät metodit voivat olla tehottomia. Kompleksisten verkostojen ja suhteiden tutkiminen perinteisin keinoin saattaa olla jopa mahdotonta silkan koon vuoksi. Simulaatiot ovat hyvä työkalu tilanteissa, joissa oikeaa systeemiä on hankala tutkia sellaisenaan. Nykyään prosessipohjainen, systeemidynaaminen ja agenttipohjainen mallinnus ovat kolme käytetyintä simulaatiometodia. Kolmesta vallitsevasta mallinnustavasta, agenttipohjainen mallinnus on uusin, mutta samalla myös joustavin. Agenttipohjainen mallinnus on tehokas työkalu emergentin käytöksen tutkimiseen autonomisten agenttien avulla. Agenttipohjaisen mallinnuksen taustalla ovat yksittäisten agenttien ominaisuudet — ne voivat oppia, niillä on erilaisia suhteita ympäristöönsä ja kullakin voi olla yksilölliset käyttäytymissäännöt. Täten emergenttiä käytöstä voidaan tutkia tuntematta järjestelmän rakennetta tai prosesseja — agentit kykenevät luomaan nämä itsenäisesti, jopa yksinkertaisten sääntöjen avulla. Projektikontekstissa agentit voivat olla yksittäisiä henkilöitä, organisaatioita tai vaikkapa projektialliansseja. Tarkkuus ja eri tasojen määrä jääköön mallintajan päätettäväksi. Esimerkiksi agenttipohjainen malli voisi olla useita organisaatioita työskentelemässä yhteisen projektin eteen erilaisista lähtökohdista. Organisaatiot voisivat koostua erilaisista yksilöistä, joilla on kyky toimia itsenäisesti. Projekti voisi koostua erilaisista tehtävistä, joilla on eriasteisia vaatimuksia ja päämääriä. Projektin lopputulos voisi määräytyä projektiverkoston hallinnan eri mekanismien vaikutuksesta. Tämän työn tavoitteena on tuottaa edellä kuvatun kaltainen malli. Työn tavoitteena on rakentaa simulaatiomalli projektiverkoston hallinnan simulointia varten. Työssä vastattiin seuraaviin tutkimuskysymyksiin: RQ1: Mitkä ovat projekti- ja tehtäväverkon tärkeimmät ominaisuudet? RQ2: Mitkä ovat projektiverkoston hallinnan eri mekanismit? RQ3: Mitkä ovat simulaatiomallin kehityksen eri vaiheet? RQ4: Mitkä ovat eri simulaatiometodien vahvuudet ja heikkoudet projektiverkoston hallinnan simuloinnissa? Kysymyksiin vastaamisen ohella, agenttipohjainen malli luotiin onnistuneesti käyttäen Anylogic-ohjelmistoa ja seuraten kirjallisuudesta perustuvaa simulaatiomallin kehitysprosessia. Agenttipohjainen malli verifioitiin toimivaksi projektiverkostojen ja projektiverkoston hallinnan simulointiin. Seuraava vaihe olisi validoida malli käyttäen oikeasta maailmasta saatua dataa. Lisäksi luotuun malliin voitaisiin tulevaisuudessa lisätä joko uusia projektiverkoston hallinnan mekanismeja tai samanaikaisia projekteja resurssirajoitteisilla tehtävillä. Agenttipohjainen mallinnus sopii tähän tarkoitukseen
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Waghray, Rasagnya. "EXPLORING REDUCED TRAFFIC SIGNALS USING AGENT BASED MODELING." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/372.

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The need for measures to reduce congestion in metropolitan traffic has been a pressing concern as citizens' cluster in larger cities with the immediate side effect of an increase in traffic demand. A functioning society depends on the mobility provided by the transportation network to enable its members to participate in essential activities such as production, consumption, communication, and recreation. However, it is necessary for a society to introduce congestion-relief measures for improved quality of life, the environment, and maintained safety of the citizens. The project has three components: 1. An interface, which models a road network and tools to describe data supplied to the network. 2. A simulation interface to observe the model run through time and produce suitable results for the naked streets and to find an improved traffic simulation for the cities. 3. Mathematical modeling for assessment of the pedestrian accident risk and their safety. The traffic is designed and implemented using agent-based modeling (ABM) techniques and I have used NetLogo as my testbed (Wilensky, 2003). Shared Space is not defined by the design or configuration of the environment. Design and detailing are important, but only as a catalyst for changing the way in which people interact within the public spaces. Design standards are not adequate. Even if you follow all design guidelines, it does not guarantee that the space will meet the requirements. Departing from established practice requires determination, careful thought and observation, and the courage to explore and refine novel solutions.
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Demianyk, Bryan C. P. "Development of agent-based models for healthcare: applications and critique." IEEE, 2010. http://hdl.handle.net/1993/31049.

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Agent-based modeling (ABM) is a modeling and simulation paradigm well-suited to social systems where agents interact and have some degree of autonomy. In their most basic sense, ABMs consist of agents (generally, individuals) interacting in an environment according to a set of behavioural rules. The foundational premise and the conceptual depth of ABM is that simple rules of individual behaviour will aggregate to illuminate complex and/or emergent group-level phenomena that are not specifically encoded by the modeler and that cannot be predicted or explained by the agent-level rules. In essence, ABM has the potential to reveal a whole that is greater than the sum of its parts. In this thesis, ABMs have been utilized as a modeling framework for three specific healthcare applications, including: • the development of an ABM of an emergency department within a hospital allowing the modeling of contact-based infectious diseases such as influenza, and simulating various mitigation strategies; • the development of an ABM to model the effectiveness of a real-time location system (RTLS) using radio frequency identification (RFID) in an emergency department, used for patient tracking as one measure of hospital efficiency; and, • the development of an ABM to test strategies for disaster preparedness (high volume, high risk patients) using a fictitious case of zombies in an emergency department. Although each ABM was purposeful and meaningful for its custom application, each ABM also represented an iteration toward the development of a generic ABM framework. Finally, a thorough critique of ABMs and the modifications required to create a more robust framework are provided.
February 2016
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Books on the topic "Agent-based modeling"

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Zaffar, Muhammed, and Hari Rajagopalan. Agent-Based Modeling. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071912539.

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Taylor, Simon J. E., ed. Agent-Based Modeling and Simulation. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137453648.

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Lemos, Carlos M. Agent-Based Modeling of Social Conflict. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67050-8.

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Alonso-Betanzos, Amparo, Noelia Sánchez-Maroño, Oscar Fontenla-Romero, J. Gary Polhill, Tony Craig, Javier Bajo, and Juan Manuel Corchado, eds. Agent-Based Modeling of Sustainable Behaviors. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46331-5.

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Hokamp, Sascha, László Gulyás, Matthew Koehler, and Sanith Wijesinghe, eds. Agent-based Modeling of Tax Evasion. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119155713.

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Arai, Kiyoshi, Hiroshi Deguchi, and Hiroyuki Matsui, eds. Agent-Based Modeling Meets Gaming Simulation. Tokyo: Springer Japan, 2005. http://dx.doi.org/10.1007/4-431-29427-9.

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1950-, Arai K., Deguchi Hiroshi 1955-, Matsui H, and International Simulation and Gaming Association. International Conference, eds. Agent-based modeling meets gaming simulation. Tokyo: Springer, 2005.

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1958-, Grimm Volker, ed. Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press, 2012.

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Wurzer, Gabriel, Kerstin Kowarik, and Hans Reschreiter, eds. Agent-based Modeling and Simulation in Archaeology. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-00008-4.

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Va.) Agent-Based Modeling and Simulation Workshop 2010 (2010 McLean. Agent-Based Modeling and Simulation Workshop 2010. McLean, VA: Turner-Fairbank Highway Research Center, Office of Operations R&D, 2011.

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Book chapters on the topic "Agent-based modeling"

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Helbing, Dirk. "Agent-Based Modeling." In Understanding Complex Systems, 25–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24004-1_2.

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Barnes, David J., and Dominique Chu. "Agent-Based Modeling." In Guide to Simulation and Modeling for Biosciences, 15–78. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6762-4_2.

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Marchione, Elio, and Mauricio Salgado. "Agent-Based Modeling." In Encyclopedia of the Sciences of Learning, 188–90. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_740.

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Barnes, David J., and Dominique Chu. "Agent-Based Modeling." In Introduction to Modeling for Biosciences, 15–77. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-326-8_2.

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Wang, Zhihui, and Thomas S. Deisboeck. "Agent-based Modeling." In Encyclopedia of Systems Biology, 13. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_925.

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DeLaurentis, Daniel A., Kushal Moolchandani, and Cesare Guariniello. "Agent-Based Modeling." In System of Systems Modeling and Analysis, 107–20. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003231011-8.

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Hoffmann, Matthew J. "Agent-based Modeling." In Qualitative Methods in International Relations, 187–208. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/9780230584129_12.

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Khazaii, Javad. "Agent-Based Modeling." In Advanced Decision Making for HVAC Engineers, 137–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33328-1_13.

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Tang, Chen, and Yihao Liu. "Agent-Based Modeling." In Computational Modeling for Industrial-Organizational Psychologists, 181–210. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003388852-9.

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Sulis, Emilio, and Kuldar Taveter. "Agent-Oriented Modeling." In Agent-Based Business Process Simulation, 77–104. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98816-6_5.

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Conference papers on the topic "Agent-based modeling"

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Li, Xiaoming, Zhongbin Su, Hongmin Sun, and Ping Zheng. "Agent-Based Plant Growth Modeling." In 2009 Fourth International Conference on Internet Computing for Science and Engineering (ICICSE). IEEE, 2009. http://dx.doi.org/10.1109/icicse.2009.8.

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Illangakoon, C., R. D. McLeod, and M. R. Friesen. "Agent based modeling of malaria." In 2014 IEEE Canada International Humanitarian Technology Conference (IHTC). IEEE, 2014. http://dx.doi.org/10.1109/ihtc.2014.7147546.

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Macal, Charles M., and Michael J. North. "Agent-based modeling and simulation." In 2009 Winter Simulation Conference - (WSC 2009). IEEE, 2009. http://dx.doi.org/10.1109/wsc.2009.5429318.

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Yue, Zheng, Dong Sui, Yingda Lin, and Junfeng Zhang. "Multi-Agent Based Air Traffic Simulation System." In AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-2495.

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Rothfeld, Raoul, MIlos Balac, Kay O. Ploetner, and Constantinos Antoniou. "Agent-based Simulation of Urban Air Mobility." In 2018 Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-3891.

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Situngkir, Hokky, and Deni Khanafiah. "Theorizing Corruption through Agent-Based Modeling." In 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.183.

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Ozik, Jonathan, Nicholson T. Collier, John T. Murphy, and Michael J. North. "The ReLogo agent-based modeling language." In 2013 Winter Simulation Conference - (WSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/wsc.2013.6721539.

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Masad, David, and Jacqueline Kazil. "Mesa: An Agent-Based Modeling Framework." In Python in Science Conference. SciPy, 2015. http://dx.doi.org/10.25080/majora-7b98e3ed-009.

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Ramazanov, Ruslan. "Agent-Based Modeling In Urban Studies." In IV International Scientific Conference "Competitiveness and the development of socio-economic systems" dedicated to the memory of Alexander Tatarkin. European Publisher, 2021. http://dx.doi.org/10.15405/epsbs.2021.04.77.

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Chen, Peng, Beth Plale, and Tom Evans. "Dependency Provenance in Agent Based Modeling." In 2013 IEEE 9th International Conference on eScience (eScience). IEEE, 2013. http://dx.doi.org/10.1109/escience.2013.39.

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Reports on the topic "Agent-based modeling"

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Butts, David Joshua. Agent Based Modeling for Multi-threat Environments. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1477601.

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Michels, Alexander, and Shaowen Wang. An Agent-Based Modeling Approach to Spatial Accessibility. Purdue University, October 2023. http://dx.doi.org/10.5703/1288284317670.

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Jensen, David. Analytical Tools for Investigating and Modeling Agent-Based Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada436228.

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Mniszewski, Susan M., and Sara Y. Del Valle. EpiSimS: Large-Scale Agent-based Modeling of the Spread of Disease. Office of Scientific and Technical Information (OSTI), May 2013. http://dx.doi.org/10.2172/1078365.

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Kogut, Paul. DARPA Agent Markup Language (DAML) Unified Modeling Language (UML)-Based Ontology Toolset (UBOT). Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada440653.

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Cajka, James, Philip Cooley, and William Wheaton. Attribute Assignment to a Synthetic Population in Support of Agent-Based Disease Modeling. Research Triangle Park, NC: RTI Press, September 2010. http://dx.doi.org/10.3768/rtipress.2010.mr.0019.1009.

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Sanders, David M., and William B. Carlton. Information Overload at the Tactical Level (an Application of Agent Based Modeling and Complexity Theory in Combat Modeling). Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada405459.

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Garagic, Denis. Fast Interactive Integrated Modeling and Strategy Design (FASTIMS) - The Dynamic Pathways Agent-Based Model. Fort Belvoir, VA: Defense Technical Information Center, August 2008. http://dx.doi.org/10.21236/ada485336.

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Ruth, Brian G., and J. D. Eckart. Agent-Based Modeling of a Network-Centric Battle Team Operating Within an Information Operations Environment. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada411990.

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Sotnik, Garry. SOSIEL: A Cognitive, Multi-Agent, and Knowledge-Based Platform for Modeling Boundedly-Rational Decision-Making. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6123.

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