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(Redirected from Agent based modeling) Type of computational models Not to be confused with Microsimulation.

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An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

Agent-based models are a kind of microscale model that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors (meaning rules followed by agents) generate complex behaviors (meaning state changes at the whole system level).

Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.

Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) an environment. ABMs are typically implemented as computer simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior.

History

The idea of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did not become widespread until the 1990s.

Early developments

The history of the agent-based model can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend Stanislaw Ulam, also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices later termed cellular automata. Another advance was introduced by the mathematician John Conway. He constructed the well-known Game of Life. Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.

The Simula programming language, developed in the mid 1960s and widely implemented by the early 1970s, was the first framework for automating step-by-step agent simulations.

1970s and 1980s: the first models

One of the earliest agent-based models in concept was Thomas Schelling's segregation model, which was discussed in his paper "Dynamic Models of Segregation" in 1971. Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome.

In the late 1970s, Paulien Hogeweg and Bruce Hesper began experimenting with individual models of ecology. One of their first results was to show that the social structure of bumble-bee colonies emerged as a result of simple rules that govern the behaviour of individual bees. They introduced the ToDo principle, referring to the way agents "do what there is to do" at any given time.

In the early 1980s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner. Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from ethnocentrism to the dissemination of culture. By the late 1980s, Craig Reynolds' work on flocking models contributed to the development of some of the first biological agent-based models that contained social characteristics. He tried to model the reality of lively biological agents, known as artificial life, a term coined by Christopher Langton.

The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory", based on an earlier conference presentation of theirs. A stronger and earlier candidate is Allan Newell, who in the first Presidential Address of AAAI (published as The Knowledge Level) discussed intelligent agents as a concept.

At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory (CMOT). This field grew as a special interest group of The Institute of Management Sciences (TIMS) and its sister society, the Operations Research Society of America (ORSA).

1990s: expansion

The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by Joshua M. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture. Other notable 1990s developments included Carnegie Mellon University's Kathleen Carley ABM, to explore the co-evolution of social networks and culture. The Santa Fe Institute (SFI) was important in encouraging the development of the ABM modeling platform Swarm under the leadership of Christopher Langton. Research conducted through SFI allowed the expansion of ABM techniques to a number of fields including study of the social and spatial dynamics of small-scale human societies and primates. During this 1990s timeframe Nigel Gilbert published the first textbook on Social Simulation: Simulation for the social scientist (1999) and established a journal from the perspective of social sciences: the Journal of Artificial Societies and Social Simulation (JASSS). Other than JASSS, agent-based models of any discipline are within scope of SpringerOpen journal Complex Adaptive Systems Modeling (CASM).

Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems (CASOS)—incorporated more and more agent-based modeling. Samuelson (2000) is a good brief overview of the early history, and Samuelson (2005) and Samuelson and Macal (2006) trace the more recent developments.

In the late 1990s, the merger of TIMS and ORSA to form INFORMS, and the move by INFORMS from two meetings each year to one, helped to spur the CMOT group to form a separate society, the North American Association for Computational Social and Organizational Sciences (NAACSOS). Kathleen Carley was a major contributor, especially to models of social networks, obtaining National Science Foundation funding for the annual conference and serving as the first President of NAACSOS. She was succeeded by David Sallach of the University of Chicago and Argonne National Laboratory, and then by Michael Prietula of Emory University. At about the same time NAACSOS began, the European Social Simulation Association (ESSA) and the Pacific Asian Association for Agent-Based Approach in Social Systems Science (PAAA), counterparts of NAACSOS, were organized. As of 2013, these three organizations collaborate internationally. The First World Congress on Social Simulation was held under their joint sponsorship in Kyoto, Japan, in August 2006. The Second World Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.

2000s

More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation. Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at UCLA have also made significant contributions in organizational behavior and decision-making. Since 1991, UCLA has arranged a conference at Lake Arrowhead, California, that has become another major gathering point for practitioners in this field.

2020 and later

After the advent of large language models, researchers began applying interacting language models to agent based modeling. In one widely cited paper, agentic language models interacted in a sandbox environment to perform activities like planning birthday parties and holding elections.

Theory

Most computational modeling research describes systems in equilibrium or as moving between equilibria. Agent-based modeling, however, using simple rules, can result in different sorts of complex and interesting behavior. The three ideas central to agent-based models are agents as objects, emergence, and complexity.

Agent-based models consist of dynamically interacting rule-based agents. The systems within which they interact can create real-world-like complexity. Typically agents are situated in space and time and reside in networks or in lattice-like neighborhoods. The location of the agents and their responsive behavior are encoded in algorithmic form in computer programs. In some cases, though not always, the agents may be considered as intelligent and purposeful. In ecological ABM (often referred to as "individual-based models" in ecology), agents may, for example, be trees in a forest, and would not be considered intelligent, although they may be "purposeful" in the sense of optimizing access to a resource (such as water). The modeling process is best described as inductive. The modeler makes those assumptions thought most relevant to the situation at hand and then watches phenomena emerge from the agents' interactions. Sometimes that result is an equilibrium. Sometimes it is an emergent pattern. Sometimes, however, it is an unintelligible mangle.

In some ways, agent-based models complement traditional analytic methods. Where analytic methods enable humans to characterize the equilibria of a system, agent-based models allow the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent-based modeling. Agent-based models can explain the emergence of higher-order patterns—network structures of terrorist organizations and the Internet, power-law distributions in the sizes of traffic jams, wars, and stock-market crashes, and social segregation that persists despite populations of tolerant people. Agent-based models also can be used to identify lever points, defined as moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency.

Rather than focusing on stable states, many models consider a system's robustness—the ways that complex systems adapt to internal and external pressures so as to maintain their functionalities. The task of harnessing that complexity requires consideration of the agents themselves—their diversity, connectedness, and level of interactions.

Framework

Recent work on the Modeling and simulation of Complex Adaptive Systems has demonstrated the need for combining agent-based and complex network based models. describe a framework consisting of four levels of developing models of complex adaptive systems described using several example multidisciplinary case studies:

  1. Complex Network Modeling Level for developing models using interaction data of various system components.
  2. Exploratory Agent-based Modeling Level for developing agent-based models for assessing the feasibility of further research. This can e.g. be useful for developing proof-of-concept models such as for funding applications without requiring an extensive learning curve for the researchers.
  3. Descriptive Agent-based Modeling (DREAM) for developing descriptions of agent-based models by means of using templates and complex network-based models. Building DREAM models allows model comparison across scientific disciplines.
  4. Validated agent-based modeling using Virtual Overlay Multiagent system (VOMAS) for the development of verified and validated models in a formal manner.

Other methods of describing agent-based models include code templates and text-based methods such as the ODD (Overview, Design concepts, and Design Details) protocol.

The role of the environment where agents live, both macro and micro, is also becoming an important factor in agent-based modelling and simulation work. Simple environment affords simple agents, but complex environments generate diversity of behavior.

Multi-scale modelling

One strength of agent-based modelling is its ability to mediate information flow between scales. When additional details about an agent are needed, a researcher can integrate it with models describing the extra details. When one is interested in the emergent behaviours demonstrated by the agent population, they can combine the agent-based model with a continuum model describing population dynamics. For example, in a study about CD4+ T cells (a key cell type in the adaptive immune system), the researchers modelled biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). In the resulting modular model, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models, cell population dynamics are described by an agent-based model, and systemic cytokine concentrations by ordinary differential equations. In this multi-scale model, the agent-based model occupies the central place and orchestrates every stream of information flow between scales.

Applications

In biology

Main article: Agent-based model in biology

Agent-based modeling has been used extensively in biology, including the analysis of the spread of epidemics, and the threat of biowarfare, biological applications including population dynamics, stochastic gene expression, plant-animal interactions, vegetation ecology, migratory ecology, landscape diversity, sociobiology, the growth and decline of ancient civilizations, evolution of ethnocentric behavior, forced displacement/migration, language choice dynamics, cognitive modeling, and biomedical applications including modeling 3D breast tissue formation/morphogenesis, the effects of ionizing radiation on mammary stem cell subpopulation dynamics, inflammation, and the human immune system, and the evolution of foraging behaviors. Agent-based models have also been used for developing decision support systems such as for breast cancer. Agent-based models are increasingly being used to model pharmacological systems in early stage and pre-clinical research to aid in drug development and gain insights into biological systems that would not be possible a priori. Military applications have also been evaluated. Moreover, agent-based models have been recently employed to study molecular-level biological systems. Agent-based models have also been written to describe ecological processes at work in ancient systems, such as those in dinosaur environments and more recent ancient systems as well.

In epidemiology

Agent-based models now complement traditional compartmental models, the usual type of epidemiological models. ABMs have been shown to be superior to compartmental models in regard to the accuracy of predictions. Recently, ABMs such as CovidSim by epidemiologist Neil Ferguson, have been used to inform public health (nonpharmaceutical) interventions against the spread of SARS-CoV-2. Epidemiological ABMs have been criticized for simplifying and unrealistic assumptions. Still, they can be useful in informing decisions regarding mitigation and suppression measures in cases when ABMs are accurately calibrated. The ABMs for such simulations are mostly based on synthetic populations, since the data of the actual population is not always available.

Examples of ABM use in epidemiology
Program Year Citation Description
Covasim 2021 SEIR model implemented in Python with an emphasis on features for studying the effects of interventions.
OpenABM-Covid19 2021 Epidemic model of the spread of COVID-19, simulating every individual in a population with both R and Python interfaces but using C for heavy computation.
OpenCOVID 2021 An individual-based transmission model of SARS-CoV-2 infection and COVID-19 disease dynamics, developed at the Swiss Tropical and Public Health Institute.

In business, technology and network theory

Agent-based models have been used since the mid-1990s to solve a variety of business and technology problems. Examples of applications include marketing, organizational behaviour and cognition, team working, supply chain optimization and logistics, modeling of consumer behavior, including word of mouth, social network effects, distributed computing, workforce management, and portfolio management. They have also been used to analyze traffic congestion.

Recently, agent based modelling and simulation has been applied to various domains such as studying the impact of publication venues by researchers in the computer science domain (journals versus conferences). In addition, ABMs have been used to simulate information delivery in ambient assisted environments. A November 2016 article in arXiv analyzed an agent based simulation of posts spread in Facebook. In the domain of peer-to-peer, ad hoc and other self-organizing and complex networks, the usefulness of agent based modeling and simulation has been shown. The use of a computer science-based formal specification framework coupled with wireless sensor networks and an agent-based simulation has recently been demonstrated.

Agent based evolutionary search or algorithm is a new research topic for solving complex optimization problems.

In team science

In the realm of team science, agent-based modeling has been utilized to assess the effects of team members' characteristics and biases on team performance across various settings. By simulating interactions between agents—each representing individual team members with distinct traits and biases—this modeling approach enables researchers to explore how these factors collectively influence the dynamics and outcomes of team performance. Consequently, agent-based modeling provides a nuanced understanding of team science, facilitating a deeper exploration of the subtleties and variabilities inherent in team-based collaborations.

In economics and social sciences

Main articles: Agent-based computational economics and Agent-based social simulation See also: Artificial financial market

Prior to, and in the wake of the 2008 financial crisis, interest has grown in ABMs as possible tools for economic analysis. ABMs do not assume the economy can achieve equilibrium and "representative agents" are replaced by agents with diverse, dynamic, and interdependent behavior including herding. ABMs take a "bottom-up" approach and can generate extremely complex and volatile simulated economies. ABMs can represent unstable systems with crashes and booms that develop out of non-linear (disproportionate) responses to proportionally small changes. A July 2010 article in The Economist looked at ABMs as alternatives to DSGE models. The journal Nature also encouraged agent-based modeling with an editorial that suggested ABMs can do a better job of representing financial markets and other economic complexities than standard models along with an essay by J. Doyne Farmer and Duncan Foley that argued ABMs could fulfill both the desires of Keynes to represent a complex economy and of Robert Lucas to construct models based on microfoundations. Farmer and Foley pointed to progress that has been made using ABMs to model parts of an economy, but argued for the creation of a very large model that incorporates low level models. By modeling a complex system of analysts based on three distinct behavioral profiles – imitating, anti-imitating, and indifferent – financial markets were simulated to high accuracy. Results showed a correlation between network morphology and the stock market index. However, the ABM approach has been criticized for its lack of robustness between models, where similar models can yield very different results.

ABMs have been deployed in architecture and urban planning to evaluate design and to simulate pedestrian flow in the urban environment and the examination of public policy applications to land-use. There is also a growing field of socio-economic analysis of infrastructure investment impact using ABM's ability to discern systemic impacts upon a socio-economic network. Heterogeneity and dynamics can be easily built in ABM models to address wealth inequality and social mobility.

ABMs have also been proposed as applied educational tools for diplomats in the field of international relations and for domestic and international policymakers to enhance their evaluation of public policy.

In water management

ABMs have also been applied in water resources planning and management, particularly for exploring, simulating, and predicting the performance of infrastructure design and policy decisions, and in assessing the value of cooperation and information exchange in large water resources systems.

Organizational ABM: agent-directed simulation

The agent-directed simulation (ADS) metaphor distinguishes between two categories, namely "Systems for Agents" and "Agents for Systems." Systems for Agents (sometimes referred to as agents systems) are systems implementing agents for the use in engineering, human and social dynamics, military applications, and others. Agents for Systems are divided in two subcategories. Agent-supported systems deal with the use of agents as a support facility to enable computer assistance in problem solving or enhancing cognitive capabilities. Agent-based systems focus on the use of agents for the generation of model behavior in a system evaluation (system studies and analyses).

Self-driving cars

Hallerbach et al. discussed the application of agent-based approaches for the development and validation of automated driving systems via a digital twin of the vehicle-under-test and microscopic traffic simulation based on independent agents. Waymo has created a multi-agent simulation environment Carcraft to test algorithms for self-driving cars. It simulates traffic interactions between human drivers, pedestrians and automated vehicles. People's behavior is imitated by artificial agents based on data of real human behavior. The basic idea of using agent-based modeling to understand self-driving cars was discussed as early as 2003.

Implementation

Many ABM frameworks are designed for serial von-Neumann computer architectures, limiting the speed and scalability of implemented models. Since emergent behavior in large-scale ABMs is dependent of population size, scalability restrictions may hinder model validation. Such limitations have mainly been addressed using distributed computing, with frameworks such as Repast HPC specifically dedicated to these type of implementations. While such approaches map well to cluster and supercomputer architectures, issues related to communication and synchronization, as well as deployment complexity, remain potential obstacles for their widespread adoption.

A recent development is the use of data-parallel algorithms on Graphics Processing Units GPUs for ABM simulation. The extreme memory bandwidth combined with the sheer number crunching power of multi-processor GPUs has enabled simulation of millions of agents at tens of frames per second.

Integration with other modeling forms

Since Agent-Based Modeling is more of a modeling framework than a particular piece of software or platform, it has often been used in conjunction with other modeling forms. For instance, agent-based models have also been combined with Geographic Information Systems (GIS). This provides a useful combination where the ABM serves as a process model and the GIS system can provide a model of pattern. Similarly, Social Network Analysis (SNA) tools and agent-based models are sometimes integrated, where the ABM is used to simulate the dynamics on the network while the SNA tool models and analyzes the network of interactions. Tools like GAMA provide a natural way to integrate system dynamics and GIS with ABM.

Verification and validation

Verification and validation (V&V) of simulation models is extremely important. Verification involves making sure the implemented model matches the conceptual model, whereas validation ensures that the implemented model has some relationship to the real-world. Face validation, sensitivity analysis, calibration, and statistical validation are different aspects of validation. A discrete-event simulation framework approach for the validation of agent-based systems has been proposed. A comprehensive resource on empirical validation of agent-based models can be found here.

As an example of V&V technique, consider VOMAS (virtual overlay multi-agent system), a software engineering based approach, where a virtual overlay multi-agent system is developed alongside the agent-based model. Muazi et al. also provide an example of using VOMAS for verification and validation of a forest fire simulation model. Another software engineering method, i.e. Test-Driven Development has been adapted to for agent-based model validation. This approach has another advantage that allows an automatic validation using unit test tools.

See also

References

  1. Grimm, Volker; Railsback, Steven F. (2005). Individual-based Modeling and Ecology. Princeton University Press. p. 485. ISBN 978-0-691-09666-7.
  2. ^ Niazi, Muaz; Hussain, Amir (2011). "Agent-based Computing from Multi-agent Systems to Agent-Based Models: A Visual Survey" (PDF). Scientometrics. 89 (2): 479–499. arXiv:1708.05872. doi:10.1007/s11192-011-0468-9. hdl:1893/3378. S2CID 17934527. Archived from the original (PDF) on October 12, 2013.
  3. Gustafsson, Leif; Sternad, Mikael (2010). "Consistent micro, macro, and state-based population modelling". Mathematical Biosciences. 225 (2): 94–107. doi:10.1016/j.mbs.2010.02.003. PMID 20171974.
  4. "Agent-Based Models of Industrial Ecosystems". Rutgers University. October 6, 2003. Archived from the original on July 20, 2011.
  5. Bonabeau, E. (May 14, 2002). "Agent-based modeling: Methods and techniques for simulating human systems". Proceedings of the National Academy of Sciences of the United States of America. 99 (Suppl 3): 7280–7. Bibcode:2002PNAS...99.7280B. doi:10.1073/pnas.082080899. PMC 128598. PMID 12011407.
  6. Schelling, Thomas C. (1971). "Dynamic Models of Segregation" (PDF). Journal of Mathematical Sociology. 1 (2): 143–186. doi:10.1080/0022250x.1971.9989794. Archived (PDF) from the original on December 1, 2016. Retrieved April 21, 2015.
  7. Hogeweg, Paulien (1983). "The ontogeny of the interaction structure in bumble bee colonies: a MIRROR model". Behavioral Ecology and Sociobiology. 12 (4): 271–283. Bibcode:1983BEcoS..12..271H. doi:10.1007/BF00302895. S2CID 22530183.
  8. Axelrod, Robert (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton: Princeton University Press. ISBN 978-0-691-01567-5.
  9. Holland, J.H.; Miller, J.H. (1991). "Artificial Adaptive Agents in Economic Theory" (PDF). American Economic Review. 81 (2): 365–71. Archived from the original (PDF) on October 27, 2005.
  10. Newell, Allen (January 1982). "The knowledge level". Artificial Intelligence. 18 (1): 87–127. doi:10.1016/0004-3702(82)90012-1. ISSN 0004-3702. S2CID 40702643.
  11. ^ Kohler, Timothy; Gumerman, George (2000). Dynamics in Human and Primate Societies: Agent-based Modeling of Social and Spatial Processes. New York, New York: Santa Fe Institute and Oxford University Press. ISBN 0-19-513167-3.
  12. Epstein, Joshua M.; Axtell, Robert (October 11, 1996). Growing artificial societies: social science from the bottom up. Brookings Institution Press. pp. 224. ISBN 978-0-262-55025-3.
  13. "Construct". Computational Analysis of Social Organizational Systems. Archived from the original on October 11, 2008. Retrieved February 19, 2008.
  14. "Springer Complex Adaptive Systems Modeling Journal (CASM)". Archived from the original on June 18, 2012. Retrieved July 1, 2012.
  15. Samuelson, Douglas A. (December 2000). "Designing Organizations". OR/MS Today. Archived from the original on June 17, 2019. Retrieved June 17, 2019.
  16. Samuelson, Douglas A. (February 2005). "Agents of Change". OR/MS Today. Archived from the original on June 17, 2019. Retrieved June 17, 2019.
  17. Samuelson, Douglas A.; Macal, Charles M. (August 2006). "Agent-Based Modeling Comes of Age". OR/MS Today. Archived from the original on June 17, 2019. Retrieved June 17, 2019.
  18. Sun, Ron, ed. (March 2006). Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press. ISBN 978-0-521-83964-8.
  19. "UCLA Lake Arrowhead Symposium: History". uclaarrowheadsymposium.org. UCLA Institute of Transportation Studies. Retrieved February 11, 2024.
  20. Park, Joon Sung; O'Brien, Joseph; Cai, Carrie; Morris, Meredith; Liang, Percey; Bernstein, Michael (2023). "Generative Agents: Interactive Simulacra of Human Behavior". arXiv:2304.03442 .
  21. Aditya Kurve; Khashayar Kotobi; George Kesidis (2013). "An agent-based framework for performance modeling of an optimistic parallel discrete event simulator". Complex Adaptive Systems Modeling. 1: 12. doi:10.1186/2194-3206-1-12.
  22. Niazi, Muaz A. K. (June 30, 2011). "Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems". hdl:1893/3365. {{cite journal}}: Cite journal requires |journal= (help) (PhD Thesis)
  23. Niazi, M.A. and Hussain, A (2012), Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods Cognitive Agent-based Computing Archived December 24, 2012, at the Wayback Machine
  24. "Swarm code templates for model comparison". Swarm Development Group. Archived from the original on August 3, 2008.
  25. Volker Grimm; Uta Berger; Finn Bastiansen; et al. (September 15, 2006). "A standard protocol for describing individual-based and agent-based models". Ecological Modelling. 198 (1–2): 115–126. Bibcode:2006EcMod.198..115G. doi:10.1016/j.ecolmodel.2006.04.023. S2CID 11194736. (ODD Paper)
  26. Ch'ng, E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, November 20–24, 2012, Kobe, Japan. Macro and Micro Environment Archived November 13, 2013, at the Wayback Machine
  27. Simon, Herbert A. The sciences of the artificial. MIT press, 1996.
  28. Wertheim, Kenneth Y.; Puniy, Bhanwar Lal; Fleur, Alyssa La; Shah, Ab Rauf; Barberis, Matteo; Helikar, Tomáš (August 3, 2021). "A multi-approach and multi-scale platform to model CD4+ T cells responding to infections". PLOS Computational Biology. 17 (8): e1009209. Bibcode:2021PLSCB..17E9209W. doi:10.1371/journal.pcbi.1009209. ISSN 1553-7358. PMC 8376204. PMID 34343169.
  29. Situngkir, Hokky (2004). "Epidemiology Through Cellular Automata: Case of Study Avian Influenza in Indonesia". arXiv:nlin/0403035.
  30. Caplat, Paul; Anand, Madhur; Bauch, Chris (March 10, 2008). "Symmetric competition causes population oscillations in an individual-based model of forest dynamics". Ecological Modelling. 211 (3–4): 491–500. Bibcode:2008EcMod.211..491C. doi:10.1016/j.ecolmodel.2007.10.002.
  31. Thomas, Philipp (December 2019). "Intrinsic and extrinsic noise of gene expression in lineage trees". Scientific Reports. 9 (1): 474. Bibcode:2019NatSR...9..474T. doi:10.1038/s41598-018-35927-x. ISSN 2045-2322. PMC 6345792. PMID 30679440.
  32. Fedriani JM, T Wiegand, D Ayllón, F Palomares, A Suárez-Esteban and V. Grimm. 2018. Assisting seed dispersers to restore old-fields: an individual-based model of the interactions among badgers, foxes, and Iberian pear trees. Journal of Applied Ecology 55: 600–611.
  33. Ch'ng, E. (2009) An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research, in Nature-Inspired informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering, R. Chiong, Editor. 2009, IGI Global: Hershey, PA. http://complexity.io/Publications/NII-alifeVeg-eCHNG.pdf Archived November 13, 2013, at the Wayback Machine
  34. Weller, F.G.; Webb, E.B.; Beatty, W.S.; Fogenburg, S.; Kesler, D.; Blenk, R.H.; Eadie, J.M.; Ringelman, K.; Miller, M. L. (2022). Agent-based modeling of movements and habitat selection by mid-continent mallards (Report). Cooperator Science Series. Washington, D. C: U.S. Department of Interior, Fish and Wildlife Service. doi:10.3996/css47216360. FWS/CSS-143-2022.
  35. Wirth, E.; Szabó, Gy.; Czinkóczky, A. (June 7, 2016). "Measure of Landscape Heterogeneity by Agent-Based Methodology". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. III-8: 145–151. Bibcode:2016ISPAnIII8..145W. doi:10.5194/isprs-annals-iii-8-145-2016.
  36. Lima, Francisco W.S.; Hadzibeganovic, Tarik; Stauffer., Dietrich (2009). "Evolution of ethnocentrism on undirected and directed Barabási-Albert networks". Physica A. 388 (24): 4999–5004. arXiv:0905.2672. Bibcode:2009PhyA..388.4999L. doi:10.1016/j.physa.2009.08.029. S2CID 18233740.
  37. Lima, Francisco W. S.; Hadzibeganovic, Tarik; Stauffer, Dietrich (2009). "Evolution of ethnocentrism on undirected and directed Barabási–Albert networks". Physica A. 388 (24): 4999–5004. arXiv:0905.2672. Bibcode:2009PhyA..388.4999L. doi:10.1016/j.physa.2009.08.029. S2CID 18233740.
  38. Edwards, Scott (June 9, 2009). The Chaos of Forced Migration: A Modeling Means to an Humanitarian End. VDM Verlag. p. 168. ISBN 978-3-639-16516-6.
  39. Hadzibeganovic, Tarik; Stauffer, Dietrich; Schulze, Christian (2009). "Agent-based computer simulations of language choice dynamics". Annals of the New York Academy of Sciences. 1167 (1): 221–229. Bibcode:2009NYASA1167..221H. doi:10.1111/j.1749-6632.2009.04507.x. PMID 19580569. S2CID 32790067.
  40. Tang, Jonathan; Enderling, Heiko; Becker-Weimann, Sabine; Pham, Christopher; Polyzos, Aris; Chen, Charlie; Costes, Sylvain (2011). "Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling". Integrative Biology. 3 (4): 408–21. doi:10.1039/c0ib00092b. PMC 4009383. PMID 21373705.
  41. Tang, Jonathan; Fernando-Garcia, Ignacio; Vijayakumar, Sangeetha; Martinez-Ruis, Haydeliz; Illa-Bochaca, Irineu; Nguyen, David; Mao, Jian-Hua; Costes, Sylvain; Barcellos-Hoff, Mary Helen (2014). "Irradiation of juvenile, but not adult, mammary gland increases stem cell self-renewal and estrogen receptor negative tumors". Stem Cells. 32 (3): 649–61. doi:10.1002/stem.1533. PMID 24038768. S2CID 32979016.
  42. Tang, Jonathan; Ley, Klaus; Hunt, C. Anthony (2007). "Dynamics of in silico leukocyte rolling, activation, and adhesion". BMC Systems Biology. 1 (14): 14. doi:10.1186/1752-0509-1-14. PMC 1839892. PMID 17408504.
  43. Tang, Jonathan; Hunt, C. Anthony (2010). "Identifying the rules of engagement enabling leukocyte rolling, activation, and adhesion". PLOS Computational Biology. 6 (2): e1000681. Bibcode:2010PLSCB...6E0681T. doi:10.1371/journal.pcbi.1000681. PMC 2824748. PMID 20174606.
  44. Castiglione, Filippo; Celada, Franco (2015). Immune System Modeling and Simulation. CRC Press, Boca Raton. p. 274. ISBN 978-1-4665-9748-8. Archived from the original on February 4, 2023. Retrieved December 17, 2017.
  45. Liang, Tong; Brinkman, Braden A. W. (March 14, 2022). "Evolution of innate behavioral strategies through competitive population dynamics". PLOS Computational Biology. 18 (3): e1009934. Bibcode:2022PLSCB..18E9934L. doi:10.1371/journal.pcbi.1009934. ISSN 1553-7358. PMC 8947601. PMID 35286315.
  46. Siddiqa, Amnah; Niazi, Muaz; Mustafa, Farah; Bokhari, Habib; Hussain, Amir; Akram, Noreen; Shaheen, Shabnum; Ahmed, Fouzia; Iqbal, Sarah (2009). "A new hybrid agent-based modeling & simulation decision support system for breast cancer data analysis" (PDF). 2009 International Conference on Information and Communication Technologies. pp. 134–139. doi:10.1109/ICICT.2009.5267202. ISBN 978-1-4244-4608-7. S2CID 14433449. Archived from the original (PDF) on June 14, 2011. (Breast Cancer DSS)
  47. Butler, James; Cosgrove, Jason; Alden, Kieran; Read, Mark; Kumar, Vipin; Cucurull-Sanchez, Lourdes; Timmis, Jon; Coles, Mark (2015). "Agent-Based Modeling in Systems Pharmacology". CPT: Pharmacometrics & Systems Pharmacology. 4 (11): 615–629. doi:10.1002/psp4.12018. PMC 4716580. PMID 26783498.
  48. Barathy, Gnana; Yilmaz, Levent; Tolk, Andreas (March 2012). "Agent Directed Simulation for Combat Modeling and Distributed Simulation". Engineering Principles of Combat Modeling and Distributed Simulation. Hoboken, NJ: Wiley. pp. 669–714. doi:10.1002/9781118180310.ch27. ISBN 9781118180310.
  49. Azimi, Mohammad; Jamali, Yousef; Mofrad, Mohammad R. K. (2011). "Accounting for Diffusion in Agent Based Models of Reaction-Diffusion Systems with Application to Cytoskeletal Diffusion". PLOS ONE. 6 (9): e25306. Bibcode:2011PLoSO...625306A. doi:10.1371/journal.pone.0025306. PMC 3179499. PMID 21966493.
  50. Azimi, Mohammad; Mofrad, Mohammad R. K. (2013). "Higher Nucleoporin-Importinβ Affinity at the Nuclear Basket Increases Nucleocytoplasmic Import". PLOS ONE. 8 (11): e81741. Bibcode:2013PLoSO...881741A. doi:10.1371/journal.pone.0081741. PMC 3840022. PMID 24282617.
  51. Azimi, Mohammad; Bulat, Evgeny; Weis, Karsten; Mofrad, Mohammad R. K. (November 5, 2014). "An agent-based model for mRNA export through the nuclear pore complex". Molecular Biology of the Cell. 25 (22): 3643–3653. doi:10.1091/mbc.E14-06-1065. PMC 4230623. PMID 25253717.
  52. Pahl, Cameron C.; Ruedas, Luis (2021). "Carnosaurs as Apex Scavengers: Agent-based simulations reveal possible vulture analogues in late Jurassic Dinosaurs". Ecological Modelling. 458: 109706. Bibcode:2021EcMod.45809706P. doi:10.1016/j.ecolmodel.2021.109706.
  53. Volmer; et al. (2017). "Did Panthera pardus (Linnaeus, 1758) become extinct in Sumatra because of competition for prey? Modeling interspecific competition within the Late Pleistocene carnivore guild of the Padang Highlands, Sumatra". Palaeogeography, Palaeoclimatology, Palaeoecology. 487: 175–186. Bibcode:2017PPP...487..175V. doi:10.1016/j.palaeo.2017.08.032.
  54. Hagen, Oskar; Flück, Benjamin; Fopp, Fabian; Cabral, Juliano C.; Hartig, Florian; Pontarp, Mikael; Rangel, Thiago F.; Pellissier, Loïc (2021). "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity". PLOS Biology. 19 (7): e3001340. doi:10.1371/journal.pbio.3001340. PMC 8384074. PMID 34252071. S2CID 235807562.
  55. Eisinger, Dirk; Thulke, Hans-Hermann (April 1, 2008). "Spatial pattern formation facilitates eradication of infectious diseases". The Journal of Applied Ecology. 45 (2): 415–423. Bibcode:2008JApEc..45..415E. doi:10.1111/j.1365-2664.2007.01439.x. ISSN 0021-8901. PMC 2326892. PMID 18784795.
  56. Railsback, Steven F.; Grimm, Volker (March 26, 2019). Agent-Based and Individual-Based Modeling. Princeton University Press. ISBN 978-0-691-19082-2. Archived from the original on October 24, 2020. Retrieved October 19, 2020.
  57. Adam, David (April 2, 2020). "Special report: The simulations driving the world's response to COVID-19". Nature. 580 (7803): 316–318. Bibcode:2020Natur.580..316A. doi:10.1038/d41586-020-01003-6. PMID 32242115. S2CID 214771531.
  58. Sridhar, Devi; Majumder, Maimuna S. (April 21, 2020). "Modelling the pandemic". BMJ. 369: m1567. doi:10.1136/bmj.m1567. ISSN 1756-1833. PMID 32317328. S2CID 216074714. Archived from the original on May 16, 2021. Retrieved October 19, 2020.
  59. Squazzoni, Flaminio; Polhill, J. Gareth; Edmonds, Bruce; Ahrweiler, Petra; Antosz, Patrycja; Scholz, Geeske; Chappin, Émile; Borit, Melania; Verhagen, Harko; Giardini, Francesca; Gilbert, Nigel (2020). "Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action". Journal of Artificial Societies and Social Simulation. 23 (2): 10. doi:10.18564/jasss.4298. hdl:10037/19057. ISSN 1460-7425. S2CID 216426533. Archived from the original on February 24, 2021. Retrieved October 19, 2020.
  60. Maziarz, Mariusz; Zach, Martin (2020). "Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal". Journal of Evaluation in Clinical Practice. 26 (5): 1352–1360. doi:10.1111/jep.13459. ISSN 1365-2753. PMC 7461315. PMID 32820573.
  61. Manout, O.; Ciari, F. (2021). "Assessing the Role of Daily Activities and Mobility in the Spread of COVID-19 in Montreal With an Agent-Based Approach". Frontiers in Built Environment. 7. doi:10.3389/fbuil.2021.654279.
  62. Kerr, Cliff; et al. (2021), "Covasim: an agent-based model of COVID-19 dynamics and interventions", medRxiv, vol. 17, no. 7, pp. e1009149, Bibcode:2021PLSCB..17E9149K, doi:10.1371/journal.pcbi.1009149, PMC 8341708, PMID 34310589
  63. Hinch, Robert; et al. (2021), "OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing", PLOS Computational Biology, 17 (7): e1009146, Bibcode:2021PLSCB..17E9146H, doi:10.1371/journal.pcbi.1009146, PMC 8328312, PMID 34252083
  64. Shattock, Andrew; Le Rutte, Epke; et al. (2021), "Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland", Epidemics, 38 (7): 100535, Bibcode:2021PLSCB..17E9146H, doi:10.1016/j.epidem.2021.100535, PMC 8669952, PMID 34923396
  65. "Git-repository with open access source-code for OpenCOVID". GitHub. Swiss TPH. January 31, 2022. Archived from the original on February 15, 2022. Retrieved February 15, 2022.
  66. Rand, William; Rust, Roland T. (2011). "Agent-based modeling in marketing: Guidelines for rigor". International Journal of Research in Marketing. 28 (3): 181–193. doi:10.1016/j.ijresmar.2011.04.002.
  67. Hughes, H. P. N.; Clegg, C. W.; Robinson, M. A.; Crowder, R. M. (2012). "Agent-based modelling and simulation: The potential contribution to organizational psychology". Journal of Occupational and Organizational Psychology. 85 (3): 487–502. doi:10.1111/j.2044-8325.2012.02053.x.
  68. Boroomand, Amin (2021). "Hard work, risk-taking, and diversity in a model of collective problem solving". Journal of Artificial Societies and Social Simulation. 24 (4). doi:10.18564/jasss.4704.
  69. Crowder, R. M.; Robinson, M. A.; Hughes, H. P. N.; Sim, Y. W. (2012). "The development of an agent-based modeling framework for simulating engineering team work". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 42 (6): 1425–1439. doi:10.1109/TSMCA.2012.2199304. S2CID 7985332.
  70. "Application of Agent Technology to Traffic Simulation". United States Department of Transportation. May 15, 2007. Archived from the original on January 1, 2011. Retrieved October 31, 2007.
  71. Niazi, M.; Baig, A. R.; Hussain, A.; Bhatti, S. (2008). "Simulation of the research process" (PDF). In Mason, S.; Hill, R.; Mönch, L.; Rose, O.; Jefferson, T.; Fowler, J. W. (eds.). 2008 Winter Simulation Conference. pp. 1326–1334. doi:10.1109/WSC.2008.4736206. hdl:1893/3203. ISBN 978-1-4244-2707-9. S2CID 6597668. Archived (PDF) from the original on June 1, 2011. Retrieved June 7, 2009.
  72. Niazi, Muaz A. (2008). "Self-organized customized content delivery architecture for ambient assisted environments" (PDF). Proceedings of the third international workshop on Use of P2P, grid and agents for the development of content networks. pp. 45–54. doi:10.1145/1384209.1384218. ISBN 9781605581552. S2CID 16916130. Archived from the original (PDF) on June 14, 2011.
  73. Nasrinpour, Hamid Reza; Friesen, Marcia R.; McLeod, Robert D. (November 22, 2016). "An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network". arXiv:1611.07454 .
  74. Niazi, Muaz; Hussain, Amir (March 2009). "Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks" (PDF). IEEE Communications Magazine. 47 (3): 163–173. doi:10.1109/MCOM.2009.4804403. hdl:1893/2423. S2CID 23449913. Archived from the original (PDF) on December 4, 2010.
  75. Niazi, Muaz; Hussain, Amir (2011). "A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments" (PDF). IEEE Sensors Journal. 11 (2): 404–412. arXiv:1708.05875. Bibcode:2011ISenJ..11..404N. doi:10.1109/JSEN.2010.2068044. hdl:1893/3398. S2CID 15367419. Archived from the original (PDF) on July 25, 2011.
  76. Sarker, R. A.; Ray, T. (2010). "Agent Based Evolutionary Approach: An Introduction". Agent-Based Evolutionary Search. Adaptation, Learning, and Optimization. Vol. 5. pp. 1–11. doi:10.1007/978-3-642-13425-8_1. ISBN 978-3-642-13424-1.
  77. Boroomand, Amin; Smaldino, Paul E. (2023). "Superiority bias and communication noise can enhance collective problem solving". Journal of Artificial Societies and Social Simulation. 26 (3). doi:10.18564/jasss.5154.
  78. Page, Scott E. (2008). Agent-Based Models (2 ed.). Archived from the original on February 10, 2018. Retrieved October 3, 2011. {{cite book}}: |work= ignored (help)
  79. Testfatsion, Leigh; Judd, Kenneth, eds. (May 2006). Handbook of Computational Economics. Vol. 2. Elsevier. p. 904. ISBN 978-0-444-51253-6. Archived from the original on March 6, 2012. Retrieved January 29, 2012. (Chapter preview)
  80. ^ "Agents of change". The Economist. July 22, 2010. Archived from the original on January 23, 2011. Retrieved February 16, 2011.
  81. "A model approach". Nature. 460 (7256): 667. August 6, 2009. Bibcode:2009Natur.460Q.667.. doi:10.1038/460667a. PMID 19661863.
  82. Farmer & Foley 2009, p. 685.
  83. Farmer & Foley 2009, p. 686.
  84. Stefan, F., & Atman, A. (2015). Is there any connection between the network morphology and the fluctuations of the stock market index? Physica A: Statistical Mechanics and Its Applications, (419), 630-641.
  85. Dawid, Herbert; Gatti, Delli (January 2018). "Agent-based macroeconomics". Handbook of Computational Economics. 4: 63–156. doi:10.1016/bs.hescom.2018.02.006.
  86. Rand, William; Rust, Roland T. (July 2011). "Agent-based modeling in marketing: Guidelines for rigor". International Journal of Research in Marketing. 28 (3): 181–193. doi:10.1016/j.ijresmar.2011.04.002.
  87. Aschwanden, G.D.P.A; Wullschleger, Tobias; Müller, Hanspeter; Schmitt, Gerhard (2009). "Evaluation of 3D city models using automatic placed urban agents". Automation in Construction. 22: 81–89. doi:10.1016/j.autcon.2011.07.001.
  88. Brown, Daniel G.; Page, Scott E.; Zellner, Moira; Rand, William (2005). "Path dependence and the validation of agent-based spatial models of land use". International Journal of Geographical Information Science. 19 (2): 153–174. Bibcode:2005IJGIS..19..153B. doi:10.1080/13658810410001713399.
  89. Smetanin, Paul; Stiff, David (2015). Investing in Ontario's Public Infrastructure: A Prosperity at Risk Perspective, with an analysis of the Greater Toronto and Hamilton Area (PDF) (Report). The Canadian Centre for Economic Analysis. Archived (PDF) from the original on November 18, 2016. Retrieved November 17, 2016.
  90. Yang, Xiaoliang; Zhou, Peng (April 2022). "Wealth inequality and social mobility: A simulation-based modelling approach". Journal of Economic Behavior & Organization. 196: 307–329. doi:10.1016/j.jebo.2022.02.012. hdl:10419/261231. S2CID 247143315.
  91. Butcher, Charity; Njonguo, Edwin (December 22, 2021). "Simulating Diplomacy: Learning Aid or Business as Usual?". Journal of Political Science Education. 17 (sup1): 185–203. doi:10.1080/15512169.2020.1803080. ISSN 1551-2169.
  92. Gilbert, Nigel; Ahrweiler, Petra; Barbrook-Johnson, Pete; Narasimhan, Kavin Preethi; Wilkinson, Helen (2018). "Computational Modelling of Public Policy: Reflections on Practice". Journal of Artificial Societies and Social Simulation. 21 (1). doi:10.18564/jasss.3669. hdl:10044/1/102075. ISSN 1460-7425.
  93. Berglund, Emily Zechman (November 2015). "Using Agent-Based Modeling for Water Resources Planning and Management". Journal of Water Resources Planning and Management. 141 (11): 04015025. doi:10.1061/(ASCE)WR.1943-5452.0000544. ISSN 0733-9496. Archived from the original on January 19, 2022. Retrieved September 18, 2021.
  94. Giuliani, M.; Castelletti, A. (July 2013). "Assessing the value of cooperation and information exchange in large water resources systems by agent-based optimization: MAS Framework for Large Water Resources Systems". Water Resources Research. 49 (7): 3912–3926. doi:10.1002/wrcr.20287. S2CID 128659104.
  95. "Agent-Directed Simulation". Archived from the original on September 27, 2011. Retrieved August 9, 2011.
  96. Hallerbach, S.; Xia, Y.; Eberle, U.; Koester, F. (2018). "Simulation-Based Identification of Critical Scenarios for Cooperative and Automated Vehicles". SAE International Journal of Connected and Automated Vehicles. 1 (2). SAE International: 93–106. doi:10.4271/2018-01-1066.
  97. Madrigal, Story by Alexis C. "Inside Waymo's Secret World for Training Self-Driving Cars". The Atlantic. Archived from the original on August 14, 2020. Retrieved August 14, 2020.
  98. Connors, J.; Graham, S.; Mailloux, L. (2018). "Cyber Synthetic Modeling for Vehicle-to-Vehicle Applications". International Conference on Cyber Warfare and Security. Academic Conferences International Limited: 594-XI.
  99. Yang, Guoqing; Wu, Zhaohui; Li, Xiumei; Chen, Wei (2003). "SVE: Embedded agent based smart vehicle environment". Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems. Vol. 2. pp. 1745–1749 vol.2. doi:10.1109/ITSC.2003.1252782. ISBN 0-7803-8125-4. S2CID 110177067. Archived from the original on January 31, 2022. Retrieved August 19, 2021.
  100. ^ Lysenko, Mikola; D'Souza, Roshan M. (2008). "A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units". Journal of Artificial Societies and Social Simulation. 11 (4): 10. ISSN 1460-7425. Archived from the original on April 26, 2019. Retrieved April 16, 2019.
  101. Gulyás, László; Szemes, Gábor; Kampis, George; de Back, Walter (2009). "A Modeler-Friendly API for ABM Partitioning". Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2009. 2. San Diego, California, US: 219–226. Archived from the original on April 16, 2019. Retrieved April 16, 2019.
  102. Collier, N.; North, M. (2013). "Parallel agent-based simulation with Repast for High Performance Computing". Simulation. 89 (10): 1215–1235. doi:10.1177/0037549712462620. S2CID 29255621.
  103. Fujimoto, R. (2015). "Parallel and distributed simulation". 2015 Winter Simulation Conference (WSC). Huntington Beach, CA, US. pp. 45–59. doi:10.1109/WSC.2015.7408152. ISBN 978-1-4673-9743-8. S2CID 264924790. Archived from the original on February 4, 2023. Retrieved September 6, 2020.{{cite book}}: CS1 maint: location missing publisher (link)
  104. Shook, E.; Wang, S.; Tang, W. (2013). "A communication-aware framework for parallel spatially explicit agent-based models". International Journal of Geographical Information Science. 27 (11). Taylor & Francis: 2160–2181. Bibcode:2013IJGIS..27.2160S. doi:10.1080/13658816.2013.771740. S2CID 41702653.
  105. Jonas, E.; Pu, Q.; Venkataraman, S.; Stoica, I.; Recht, B. (2017). "Occupy the cloud: Distributed computing for the 99%". Proceedings of the 2017 Symposium on Cloud Computing. ACM. pp. 445–451. arXiv:1702.04024. doi:10.1145/3127479.3128601. ISBN 978-1-4503-5028-0. S2CID 854354.
  106. Isaac Rudomin; et al. (2006). "Large Crowds in the GPU". Monterrey Institute of Technology and Higher Education. Archived from the original on January 11, 2014.
  107. Richmond, Paul; Romano, Daniela M. (2008). "Agent Based GPU, a Real-time 3D Simulation and Interactive Visualisation Framework for Massive Agent Based Modelling on the GPU" (PDF). Proceedings International Workshop on Super Visualisation (IWSV08). Archived from the original (PDF) on January 15, 2009. Retrieved April 27, 2012.
  108. Brown, Daniel G.; Riolo, Rick; Robinson, Derek T.; North, Michael; Rand, William (2005). "Spatial Process and Data Models: Toward Integration of agent-based models and GIS". Journal of Geographical Systems. 7 (1). Springer: 25–47. Bibcode:2005JGS.....7...25B. doi:10.1007/s10109-005-0148-5. hdl:2027.42/47930. S2CID 14059768.
  109. Zhang, J.; Tong, L.; Lamberson, P.J.; Durazo-Arvizu, R.A.; Luke, A.; Shoham, D.A. (2015). "Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks". Social Science & Medicine. 125. Elsevier BV: 203–213. doi:10.1016/j.socscimed.2014.05.049. ISSN 0277-9536. PMC 4306600. PMID 24951404.
  110. Sargent, R. G. (2000). "Verification, validation and accreditation of simulation models". 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165). Vol. 1. pp. 50–59. CiteSeerX 10.1.1.17.438. doi:10.1109/WSC.2000.899697. ISBN 978-0-7803-6579-7. S2CID 57059217.
  111. Klügl, F. (2008). "A validation methodology for agent-based simulations". Proceedings of the 2008 ACM symposium on Applied computing - SAC '08. pp. 39–43. doi:10.1145/1363686.1363696. ISBN 9781595937537. S2CID 9450992.
  112. Fortino, G.; Garro, A.; Russo, W. (2005). "A Discrete-Event Simulation Framework for the Validation of Agent-Based and Multi-Agent Systems" (PDF). Archived (PDF) from the original on June 26, 2011. Retrieved September 27, 2009. {{cite journal}}: Cite journal requires |journal= (help)
  113. Tesfatsion, Leigh. "Empirical Validation: Agent-Based Computational Economics". Iowa State University. Archived from the original on June 26, 2020. Retrieved June 24, 2020.
  114. Niazi, Muaz; Hussain, Amir; Kolberg, Mario. "Verification and Validation of Agent-Based Simulations using the VOMAS approach" (PDF). Proceedings of the Third Workshop on Multi-Agent Systems and Simulation '09 (MASS '09), as Part of MALLOW 09, Sep 7–11, 2009, Torino, Italy. Archived from the original (PDF) on June 14, 2011.
  115. Niazi, Muaz; Siddique, Qasim; Hussain, Amir; Kolberg, Mario (April 11–15, 2010). "Verification & Validation of an Agent-Based Forest Fire Simulation Model" (PDF). Proceedings of the Agent Directed Simulation Symposium 2010, as Part of the ACM SCS Spring Simulation Multiconference: 142–149. Archived from the original (PDF) on July 25, 2011.
  116. Niazi, Muaz A. K. (June 11, 2011). "Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems". University of Stirling. hdl:1893/3365. {{cite journal}}: Cite journal requires |journal= (help) PhD Thesis
  117. Onggo, B.S.; Karatas, M. (2016). "Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation". European Journal of Operational Research. 254 (2): 517–531. doi:10.1016/j.ejor.2016.03.050. Archived from the original on June 30, 2020.

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