Concepts and Methodologies for Modeling and Simulation

Yılmaz, Levent

doi:10.1007/978-3-319-15096-3

Cited by 13 publications

(3 citation statements)

References 76 publications

(125 reference statements)

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“…The Kalman filter, particle filter, and their variants (sequential methods), as well as variational methods such as 3D-Var, and 4D-Var, are among the melding schemes for data assimilation [54]. DA has been widely used for weather prediction, oil and gas pipeline, and ocean and climate modeling [14]. However, more attention should be paid to data assimilation techniques for agent-based models (ABMs) such as pedestrian models.…”

Section: Traditional Crowd Monitoring Systemsmentioning

confidence: 99%

“…Data assimilation (DA), which is a framework created for integrating a model with realtime data to make use of "all available information" [13], exactly addresses the challenges for online agent-based simulation. DA has been extensively used during past decades in Earth sciences (e.g., meteorology) and is incorporated in dynamic data-driven simulation (DDDS) [14,15]. Recently, there have been efforts to replicate the approach for agentbased simulations (e.g., pedestrian simulation) [2,[16][17][18][19][20], however, far fewer attempts have been made to move the existing methodology for application in a real-world scenario [1].…”

Section: Introductionmentioning

confidence: 99%

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Data Assimilation for Agent-Based Models

Ghorbani,

Nazari-Heris

et al. 2023

Mathematics

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This article presents a comprehensive review of the existing literature on the topic of data assimilation for agent-based models, with a specific emphasis on pedestrians and passengers within the context of transportation systems. This work highlights a plethora of advanced techniques that may have not been previously employed for online pedestrian simulation, and may therefore offer significant value to readers in this domain. Notably, these methods often necessitate a sophisticated understanding of mathematical principles such as linear algebra, probability theory, singular value decomposition, optimization, machine learning, and compressed sensing. Despite this complexity, this article strives to provide a nuanced explanation of these mathematical underpinnings. It is important to acknowledge that the subject matter under study is still in its nascent stages, and as such, it is highly probable that new techniques will emerge in the coming years. One potential avenue for future exploration involves the integration of machine learning with Agent-based Data Assimilation (ABDA, i.e., data assimilation methods used for agent-based models) methods.

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Section: Traditional Crowd Monitoring Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Assimilation for Agent-Based Models

Ghorbani,

Nazari-Heris

et al. 2023

Mathematics

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“…Below, we summarize the computational architectures and experimental results of two of our models that are particularly relevant for this special issue. The overall goal of our collaboration is to introduce M&S—which has been called the “third pillar” of science, alongside theory and experimentation (Yilmaz, 2015)—into the heart of the scientific study of (non)religion.…”

Section: Computer Modeling and Simulation Of (Non)religionmentioning

confidence: 99%

Why do the godless prosper? Modeling the cognitive and coalitional mechanisms that promote atheism.

Shults

Gore

Lemos

et al. 2018

Psychology of Religion and Spirituality

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This article offers an affirmative construal of atheism: the attempt to make sense of the world with naturalist explanations and to act sensibly in society following secularist principles (i.e., without relying on supernatural agents or complying with supernatural authorities). After briefly describing the conceptual framework behind this positive conception of a nonreligious worldview, we outline the construction and present the findings of two computational models that simulate some of the cognitive and coalitional mechanisms that engender and nurture religious and nonreligious worldviews. These models allow us to explore the causal dynamics within complex adaptive systems involving (dis)belief in supernatural agents and (dis)affiliation from religious institutions.

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Over Fifty Years of My Involvement in Simulation

Ören

2019

Simulation Foundations, Methods and Applications

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Concepts and Methodologies for Modeling and Simulation

Cited by 13 publications

References 76 publications

Data Assimilation for Agent-Based Models

Data Assimilation for Agent-Based Models

Why do the godless prosper? Modeling the cognitive and coalitional mechanisms that promote atheism.

Over Fifty Years of My Involvement in Simulation

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