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Books on the topic 'Agent-based modeling'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 July 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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

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9

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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10

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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11

BenDor, Todd K., and Jürgen Scheffran. Agent-Based Modeling of Environmental Conflict and Cooperation. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351106252.

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12

Arifin, S. M. Niaz, Gregory R. Madey, and Frank H. Collins. Spatial Agent-Based Simulation Modeling in Public Health. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118964385.

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13

Allen, Theodore T. Introduction to Discrete Event Simulation and Agent-based Modeling. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-139-4.

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14

Marie, Viccari Rosa, Verdin Regina, and Jaques Patricia Augustin, eds. Agent-based tutoring systems by cognitive and affective modeling. Hershey PA: Information Science Reference, 2008.

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15

Interdisciplinary applications of agent-based social simulation and modeling. Hershey, PA: Information Science Reference, 2014.

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16

Generative social science: Studies in agent-based computational modeling. Princeton: Princeton University Press, 2006.

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17

Unrath, Craig S. Dynamic exploration of helicopter reconnaissance through agent-based modeling. Monterey, Calif: Naval Postgraduate School, 2000.

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18

T, Crooks Andrew, See Linda M, Batty Michael, and SpringerLink (Online service), eds. Agent-Based Models of Geographical Systems. Dordrecht: Springer Netherlands, 2012.

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19

Niazi, Muaz A. Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods. Dordrecht: Springer Netherlands, 2013.

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20

Chakrabarti, Anindya S., Lukáš Pichl, and Taisei Kaizoji, eds. Network Theory and Agent-Based Modeling in Economics and Finance. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8319-9.

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21

Terano, Takao, Hajime Kita, Toshiyuki Kaneda, Kiyoshi Arai, and Hiroshi Deguchi, eds. Agent-Based Simulation: From Modeling Methodologies to Real-World Applications. Tokyo: Springer Tokyo, 2005. http://dx.doi.org/10.1007/b138317.

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22

Dam, Koen H. Agent-Based Modelling of Socio-Technical Systems. Dordrecht: Springer Netherlands, 2013.

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23

North, Michael J. Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation. New York: Oxford University Press, 2006.

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24

North, Michael J. Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation. New York, NY: Oxford University Press, 2006.

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25

de Marchi, Scott, and Scott E. Page. Agent‐Based Modeling. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0004.

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This article provides a discussion on agent-based modeling. Two examples that show the ability of computational methods to extend game-theoretic results are presented. It then discusses modeling agents, modeling agent interactions, and system behaviour. In addition, it describes how agent-based models differ from and complement mathematical models and concludes with some suggestions for how one might best leverage the strengths of agent-based models to advance political science. Most mathematical analyses of game-theoretic models do not look into the stability and attainability of their equilibria and would be made richer by complementing them with agent-based models that explored those properties. The ability of computational models to test the robustness of formal results would be reason alone to add them to tool kits. As a methodology, agent-based modeling should be considered as in its infancy, its enormous potential limited only by the scientific and creative talents of its practitioners.

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26

Agent-Based Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73879-4.

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27

Taylor, S. Agent-Based Modeling and Simulation. Palgrave Macmillan Limited, 2014.

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28

Romanowska, Iza. Agent-Based Modeling for Archaeology. SFI Press, 2021. http://dx.doi.org/10.37911/9781947864382.

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To fully understand not only the past, but also the trajectories, of human societies, we need a more dynamic view of human social systems. Agent-based modeling (ABM), which can create fine-scale models of behavior over time and space, may reveal important, general patterns of human activity. Agent-Based Modeling for Archaeology is the first ABM textbook designed for researchers studying the human past. Appropriate for scholars from archaeology, the digital humanities, and other social sciences, this book offers novices and more experienced ABM researchers a modular approach to learning ABM and using it effectively. Readers will find the necessary background, discussion of modeling techniques and traps, references, and algorithms to use ABM in their own work. They will also find engaging examples of how other scholars have applied ABM, ranging from the study of the intercontinental migration pathways of early hominins, to the weather–crop–population cycles of the American Southwest, to the trade networks of Ancient Rome. This textbook provides the foundations needed to simulate the complexity of past human societies, offering researchers a richer understanding of the past—and likely future—of our species.

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29

North, Michael John, Nicholson Collier, and John T. Murphy. High Performance Agent-Based Modeling. Taylor & Francis Group, 2015.

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30

North, Michael John. High Performance Agent-Based Modeling. Taylor & Francis Group, 2021.

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31

Taylor, S. Agent-based Modeling and Simulation. Palgrave Macmillan, 2014.

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32

Taylor, S. Agent-based Modeling and Simulation. Palgrave Macmillan, 2016.

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33

Taylor, Simon. Agent-Based Modeling and Simulation. Palgrave Macmillan, 2014.

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34

Iori, Giulia, and James Porter. Agent-based Modeling for Financial Markets. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.43.

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This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

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35

Corchado, Juan Manuel, Amparo Alonso-Betanzos, Noelia Sánchez-Maroño, Oscar Fontenla-Romero, J. Gary Polhill, Tony Craig, and Javier Bajo. Agent-Based Modeling of Sustainable Behaviors. Springer, 2017.

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36

Corchado, Juan Manuel, Amparo Alonso-Betanzos, Noelia Sánchez-Maroño, Oscar Fontenla-Romero, J. Gary Polhill, Tony Craig, and Javier Bajo. Agent-Based Modeling of Sustainable Behaviors. Springer, 2018.

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37

Agent-based modeling meets gaming simulation. Tokyo, Japan: Springer, 2006.

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38

Kiran, M. X-Machines for Agent-based Modeling. Taylor & Francis Group, 2020.

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39

Alonso-Betanzos, Amparo, Noelia Sánchez-Maroño, Oscar Fontenla-Romero, J. Gary Polhill, and Tony Craig. Agent-Based Modeling of Sustainable Behaviors. Springer, 2016.

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40

Matsui, H., H. Deguchi, and K. Arai. Agent-Based Modeling Meets Gaming Simulation. Springer London, Limited, 2006.

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41

Railsback, Steven F., and Volker Grimm. Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press, 2011.

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42

Railsback, Steven F., and Volker Grimm. Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press, 2011.

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43

Heaton, Brenda, Abdulrahman El-Sayed, and Sandro Galea. Agent-Based Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843496.003.0005.

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Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities, such as organizations or groups) with a view to assessing their effects on the system as a whole. Neighborhood characteristics and resources evolve and adapt as the individuals living within them change and vice versa. In this way, neighborhoods reflect a complex adaptive system. In this chapter, we introduce agent-based models as a tool for modeling these interactive and adaptive processes that occur within a system, such as a neighborhood. The chapter provides a basic introduction to this method, drawing on examples from the neighborhoods and health literature.

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44

Dawid, Herbert, Simon Gemkow, Philipp Harting, Sander van der Hoog, and Michael Neugart. Agent-Based Macroeconomic Modeling and Policy Analysis. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.19.

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This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.

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45

Iba, Hitoshi. Agent-Based Modeling and Simulation with Swarm. Taylor & Francis Group, 2013.

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46

Iba, Hitoshi. Agent-Based Modeling and Simulation with Swarm. Taylor & Francis Group, 2013.

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47

Wurzer, Gabriel, Kerstin Kowarik, and Hans Reschreiter. Agent-based Modeling and Simulation in Archaeology. Springer, 2016.

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48

Wurzer, Gabriel, Kerstin Kowarik, and Hans Reschreiter. Agent-based Modeling and Simulation in Archaeology. Springer, 2014.

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49

Wurzer, Gabriel, Kerstin Kowarik, and Hans Reschreiter. Agent-Based Modeling and Simulation in Archaeology. Springer, 2014.

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50

Agent-Based Modeling and Simulation with Swarm. Taylor & Francis Group, 2016.

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