Disk Graph-Based Model: A Graph Theoretical Approach for Linking Agent-Based Models and Dynamical Systems

Nguyen, Ngoc Doanh; Phan, Thi Ha Duong; Nguyễn, Thị Ngọc Anh; Drogoul, Alexis; Zucker, Jean-Daniel

doi:10.1109/rivf.2010.5633033

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…One way to overcome the limitation is to use a graphrepresentation as an intermediate level which allows more flexible information transfer from different levels of a complex system either as bottom-up or top-down information flow through levels. This idea was introduced in Nguyen [30] and in some related and extended work [31][32][33]. This methodology was first tested with the theoretical case study in population systems/ecology; and later rapidly applied to fishery systems, epidemiology systems [34], soil systems [35], and waste management systems [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Brugière

Nguyen-Ngoc

Drogoul

2022

Front. Appl. Math. Stat.

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Agent-based modeling (ABM) has been successfully used, since its emergence in the 1990s, to model and simulate the dynamics at work in complex socio-environmental systems, in many domains and applications where interactions between people and their environments give rise to emergent phenomena that are difficult to study otherwise (urban planning, land-use change, adaptation to environmental changes, biodiversity protection in socio-ecosystems, environmental pollution control, etc.). The inclusion of multiple levels of analysis, abstraction, and representation in these models, however, is much more recent and is still the subject of many proposals and discussions within a relatively informal field, Multilevel Agent-Based Modeling (ML-ABM), which is most often presented as an approach that extends the classical ABM paradigm to include multilevel concepts. Over the past decade, ML-ABM has been increasingly adopted and explored by researchers as an effective paradigm for framing and defining the mechanisms underlying multilevel dynamics. However, due to the youth of the field, no single definition, methodology, or tool unifies studies in this rapidly expanding area. This review will begin with an introduction to socio-environmental systems (SES) and the challenges that modeling approaches face in representing them properly, especially regarding the complexity of human behaviors and organizations. ABM presents opportunities for modeling SESs with respect to these challenges, including the simulation of individual and social behavior and their ability to provide a descriptive and generative representation of the simulated system. However, ABM is limited in its ability to represent levels and scales, as these concepts are absent from the classical ABM metamodel. A complete review of the ML-ABM literature will be carried out, structured around a continuum that emerged during the review: that of the distribution of behaviors (and thus, from a software engineering perspective, of control) across the levels, from approaches that allow only one level to be active at a time, to approaches that rely on simultaneous activity and feedback loops between several levels. Different design choices will, thus, be presented to meet the different needs of multi-level representation, focusing on the interest on modelers and the strengths and limitations of each. In particular, we will highlight a limitation shared by all the reviewed approaches, namely their inability to represent several parallel hierarchies of levels and their interactions, a capability that appears more and more crucial to finely represent social behaviors in SES. A new perspective on the interest that the AGR approach could represent to allow this representation of hierarchies allows us to conclude on the research perspectives are still open.

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Section: Literature Reviewmentioning

confidence: 99%

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Brugière

Nguyen-Ngoc

Drogoul

2022

Front. Appl. Math. Stat.

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“…In [13 ], it is proposed to use onl y one appropria te modeling formalism instead of tw o approaches, and inf er an EBM from an ABM SIR model by expl oring the ded ucible par ameters like number of individ ual in popula tion, rates of inter actions base on dimension of environmen t. They have done a study with the measure based on disk graph theories [12 ] to link ABM with EBM dynamical systems applied to theoretical popula tion ecol ogy.…”

Section: Coupling Ebm and Abmmentioning

confidence: 99%

Coupling equation based models and agent-based models: example of a multi-strains and switch SIR toy model

Huynh¹,

en-Huu²,

Grignard³

et al. 2017

EAI Endorsed Transactions on Context-aware Systems and Applicat

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Modeling in ecol ogy or epidemiol ogy gener all y opposes tw o classes of models, Equa tion Based Models and Agent Based Models. Mathema tical models all ow predicting the long-term dynamics of the studied systems. How ev er, the variability betw een individ uals is di fficul t to represen t, wha t makes these more suitable models for larg e and homog eneous popula tions. Mul ti-ag ent models all ow represen ting the attributes and beha vior of each individ ual and theref ore provide a grea ter lev el of detail. In return, these systems are more di fficul t to anal yze. These approaches have often been compared, but rarel y used sim ul taneousl y. We propose a hybrid approach to couple equa tions models and ag ent-based models, as well as its implemen tation on the modeling pla tf orm Gama [8]. We focus on the represen tation of a classical theoretical epidemiol ogical model (SIR model) and we ill ustr ate the construction of a class of models based on it.

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Toward an Agent-Based and Equation-Based Coupling Framework

Nghi¹,

Nguyen-Huu²,

Grignard³

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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International audienceThe ecology modeling generally opposes two class of models, equations based models and multi-agents based models. Mathematical models allow predicting the long-term dynamics of the studied systems. However, the variability between individuals is difficult to represent, what makes these more suitable models for large and homogeneous populations. Multi-agent models allow representing the attributes and behavior of each individual and therefore provide a greater level of detail. In return, these systems are more difficult to analyze. These approaches have often been compared, but rarely used simultaneously. We propose a hybrid approach to couple equations models and agent-based models, as well as its implementation on the modeling platform Gama [7]. We focus on the representation of a classical theoretical epidemiological model (SIR model) and we illustrate the construction of a class of models based on it

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Disk Graph-Based Model: A Graph Theoretical Approach for Linking Agent-Based Models and Dynamical Systems

Cited by 3 publications

References 15 publications

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Handling multiple levels in agent-based models of complex socio-environmental systems: A comprehensive review

Coupling equation based models and agent-based models: example of a multi-strains and switch SIR toy model

Toward an Agent-Based and Equation-Based Coupling Framework

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