Multi-level agent-based modeling - A literature survey
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paper_title: Generative Social Science: Studies in Agent-Based Computational Modeling

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Generative Social Science: Studies in Agent-Based Computational Modeling JOSHUA M. EPSTEIN PRINCETON UNIVERSITY PRESS, PRINCETON, NJ, 2007 352 PP. CLOTH $49.50 REVIEWED BY ERIC C. JONES This book calls for a generative social science. Generative social science rests on the idea that you cannot explain current phenomena without describing the rules or preceding conditions that produced these current phenomena. In other words, the author believes that we must not only explore causality in terms of 'A affects B,' but also in terms of how a specific suite of physical, biological, social or cultural tendencies play out across time for a given population, producing some observed state or phenomenon. Epstein argues that anything short of being able to model the flow between prior and present conditions is mere description. He says his naming of the Generative approach took inspiration from Chomsky's generative syntactic structures. Generative social science is tightly wed to the methodology of Agent-Based Modeling made more feasible lately by faster computers. However, Epstein warns against its identification solely as a computer-driven technique. His point is that past behavior of individuals, households, firms or other agents must be accounted for when understanding a phenomenon. Following the lead of mathematicians and most modelers, the author seeks parsimonious or small sets of rules to explain the arrival at any current condition. This 'new' kind of social science is probably too mathematical for most ethnographically oriented social scientists to adopt, although this historicist/ evolutionary approach is one that must regularly be injected into the social sciences in order to augment the complimentary yet more dominant functionalist and ideationist approaches. Ecosystem researchers would certainly be able to make use of the agentbased modeling approach, perhaps even being able to better account for the individual agents in their systems. Population researchers similarly could better develop models and parameters for animal/plant/ agent behaviors. Generative Social Science is generally an update to the 1996 book Growing Artificial Societies (Brookings Institution and MIT Press) by Epstein and Robert Axtell, although this new book is a compilation of works with all but three chapters (Introduction, Chapters 2 and 13) published separately elsewhere in books or journals. Preludes by Epstein for each chapter make the flow awkward, but provide contextual insights or connections between chapters. All chapters have Epstein as an author-typically the primary author-and half of the chapters are single-authored by Epstein; as such, the publisher considers the book a single-authored work. A CD with several of the models accompanies the book, so that you can change a few of the parameters and graphically view the results (hundreds of colored pixels on a square space). The agent-based modeling technique is one way to bridge the micro-macro gulf, producing non-intuitive macro results along the way. Epstein is careful to define such emergence as the computable result of agent actions, and not as the old (and even contemporary, in some cases) idea of emergence as something that can never be reduced to its parts. Despite proposing this form of reductionism, the book allows that emergent properties maybe something that the individuals themselves might not possess, so emergence is not so much a sum of parts as a product of parts. Different agent-based models with different suites of variables might produce the same social phenomena, in which case field data and theoretical plausibility assist in determining which model to pursue. Models can also be used to find out which rules will not account for observed behavior. The first three chapters constitute the introductory material, primarily advocacy for the approach as well as delimiting the domain. The domain of generative social science is based upon the following: heterogeneous agents, bounded rationality, explicit/ geographic space, local interactions, non-equilibrium dynamics and initial autonomy of agents. …
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paper_title: An architecture for modular distributed simulation with agent-based models

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Agent-based simulations are an increasingly popular means of exploring and understanding complex social systems. In order to be useful, these simulations must capture a range of aspects of the modeled situation, each possibly requiring distinct expertise. Moreover, different paradigms may be useful in modelling, ranging from those that use many lightweight reactive agents, to those that use cognitive agents, to those that focus on agent teams and organisational structures. There is need for an architecture which supports the development of a large simulation, through the integration of separately developed modules. This paper describes a framework and architecture which facilitates the integration of multiple agent-based simulations into a single global simulation. This architecture naturally supports distributed simulation and incremental development, which are ways of addressing the computational and conceptual complexity of such systems. In this paper we focus particularly on how to ensure proper management of simulation data that is affected by agents in different modules, at the same logical time. We also provide some preliminary performance evaluation addressing scalability, as well as a comparison of how other available systems handle the issue of shared data.
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paper_title: Turtles, termites, and traffic jams: explorations in massively parallel microworlds

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Part 1 Foundations: introduction the era of decentralization. Part 2 Constructions: constructionism LEGO/logo StarLogo objects and parallelism. Part 3 Explorations: simulations and stimulations slime mould artificial ants traffic jams termites turtles and frogs turtle ecology new turtle geometry forest fire recursive trees. Part 4 Reflections: the centralized mindset beyond the centralized mindset. Part 5 Projections: growing up. Appendices: student participants StarLogo overview.
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paper_title: Multi-agent systems: An introduction to distributed artificial intelligence

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From the Publisher: ::: What are multi-agent systems? How do they work? What do they do? If you are looking for the answers to these questions, read on; for Jacques Ferber's authoritative book is the first to provide a single, coherent overview of multi-agent systems. Introduces and defines key concepts throughout the text; provides numerous examples to illustrate core principles; draws on contributions from different disciplines to present a holistic, comprehensive picture of state-of-the art agent technology; and describes all the latest developments in the field and encourages the reader to reflect on possibilities for the future.
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paper_title: Agent-Based and Individual-Based Modeling: A Practical Introduction

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Agent-based modeling is a new technique for understanding how the dynamics of biological, social, and other complex systems arise from the characteristics and behaviors of the agents making up these systems. This innovative textbook gives students and scientists the skills to design, implement, and analyze agent-based models. It starts with the fundamentals of modeling and provides an introduction to NetLogo, an easy-to-use, free, and powerful software platform. Nine chapters then each introduce an important modeling concept and show how to implement it using NetLogo. The book goes on to present strategies for finding the right level of model complexity and developing theory for agent behavior, and for analyzing and learning from models.
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paper_title: Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units

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Multiscale agent-based modeling (MABM) has been widely used to simulate Glioblastoma Multiforme (GBM) and its progression. At the intracellular level, the MABM approach employs a system of ordinary differential equations to describe quantitatively specific intracellular molecular pathways that determine phenotypic switches among cells (e.g. from migration to proliferation and vice versa). At the intercellular level, MABM describes cell-cell interactions by a discrete module. At the tissue level, partial differential equations are employed to model the diffusion of chemoattractants, which are the input factors of the intracellular molecular pathway. Moreover, multiscale analysis makes it possible to explore the molecules that play important roles in determining the cellular phenotypic switches that in turn drive the whole GBM expansion. However, owing to limited computational resources, MABM is currently a theoretical biological model that uses relatively coarse grids to simulate a few cancer cells in a small slice of brain cancer tissue. In order to improve this theoretical model to simulate and predict actual GBM cancer progression in real time, a graphics processing unit (GPU)-based parallel computing algorithm was developed and combined with the multi-resolution design to speed up the MABM. The simulated results demonstrated that the GPU-based, multi-resolution and multiscale approach can accelerate the previous MABM around 30-fold with relatively fine grids in a large extracellular matrix. Therefore, the new model has great potential for simulating and predicting real-time GBM progression, if real experimental data are incorporated.
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paper_title: A hybrid simulation model for studying acute inflammatory response

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The modeling of complex biological systems presents a significant challenge. Central to this challenge is striking a balance between the degree of abstraction required to facilitate analysis and understanding, and the degree of comprehensiveness required for fidelity of the model to its reference-system. It is likely necessary to utilize multiple modeling methods in order to achieve this balance. Our research created a hybrid simulation model by melding an agent-based model of acute local infection with a system dynamics model that reflects key systemic properties. The agent based model was originally developed to simulate global inflammation in response to injury or infection, and has been used to simulate clinical drug trials. The long term objective is to develop models than can be scaled up to represent organ and system level phenomena such as multiple organ failure associated with severe sepsis. The work described in this paper is an initial proof of concept of the ability to combine these two modeling methods into a hybrid model, the type of which will almost certainly be needed to accomplish the ultimate objective of comprehensive in silico research platforms.
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paper_title: A hybrid macro-micro pedestrians evacuation model to speed up simulation in road networks

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The major methodologies of crowd simulation in a dynamic environments are either based on micro or macro models. These two types of models represent the trade-off between the level of detail and efficiency. The domain of pedestrian flow simulation on road networks is no exception and theories rely either on equation based model or agent based models. There is a growing interest in hybrid modeling that combines both of these types. This paper addresses the problem of combining both micro and macro models of pedestrians movement to speedup simulations. Hybrid model uses efficient macro modeling in part of the road networks that do not require a fine grained model and more detailed but less efficient micro modeling in critical locations. One key issue raised by such an approach and discussed is the consistency of the resulting hybrid model. Preliminary results presented in this article is a proof of concept that the use of hybrid model to simulate evacuation plan in road networks may be more efficient than the use of micro model alone.
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paper_title: On Interpreting Scale (or View) and Emergence in Complex Systems Engineering

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A human-centric treatment of the concepts of multi-scale analysis and emergence in complex systems engineering (CSE) is offered. This includes an attempt to characterize what an individual might do in conceptualizing a given systems engineering situation she/he is facing. The goal is to suggest fresh interpretations of the terms scale and emergence that will contribute to a more collaborative approach to improving the CSE practice. Because other authors use "scale" in several different ways, potentially causing confusion, this author proposes "view" instead. Here a given view is defined as a combination of "scope", "granularity", "mindset", and "timeframe". Although "emergence" has a rich spectrum of definitions in the literature, this author prefers to emphasize the unexpected, especially "surprising", flavor of emergence.
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paper_title: In virtuo Experiments Based on the Multi-Interaction System Framework: the RéISCOP Meta-Model.

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Virtual reality can enable computer scientists and domain experts to perform in virtuo experiments of numerical models of complex systems. Such dynamical and interactive experiments are indeed needed when it comes to com- plex systems with complex dynamics and structures. In this context, the question of the modeling tool to study such models is crucial. Such tool, called a virtuo- scope, must enable the virtual experimentation of models inside a conceptual and experimental framework for imagining, modeling and experimenting the complex- ity of the studied systems. This article describes a conceptual framework and a meta model, called ReISCOP, that enable the construction and simulation of mod- els of biological, chemical or physical systems. The multi-interaction conceptual framework, based on the reification of interactions, is built upon the concepts of autonomy, structural coupling and synchronous scheduling of those reified inter- actions. Applications and virtual reality experiments described in the last section show the expressiveness of this approach and its capacity to actually formulate het- erogeneous models in heterogeneous time and space scales, which is required for studying biological complex systems.
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paper_title: MACRO-MICRO SIMULATION OF TRAFFIC FLOW

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Abstract This paper presents the recent progress in the development of the hybrid concept for the traffic simulation. The main objective is to couple two traffic representation models with different scale levels. The first allows a global traffic representation providing the possibility to simulate a large network. The second model focuses on the individual interactions of vehicles and allows then to understand the phenomena of the microscopic point of view. The paper shows that the proposed approach is generic allowing the cohabitation of various macroscopic models with the microscopic model developed using the agent paradigm. It presents also some simulation results that illustrate the relevance of the elaborated approach.
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paper_title: Hybrid models of tumor growth

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Cancer is a complex, multiscale process in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches that can handle multiple intracellular and extracellular factors acting on different time and space scales. Hybrid models provide a way to integrate both discrete and continuous variables that are used to represent individual cells and concentrationordensityfields,respectively.Eachdiscretecellcanalsobeequipped with submodels that drive cell behavior in response to microenvironmental cues. Moreover, the individual cells can interact with one another to form and act as an integrated tissue. Hybrid models form part of a larger class of individualbased models that can naturally connect with tumor cell biology and allow for the integration of multiple interacting variables both intrinsically and extrinsically and are therefore perfectly suited to a systems biology approach to tumor
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paper_title: Multi-resolution spatial simulation for molecular crowding

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Spatial phenomena attract increasingly interest in computational biology. Molecular crowding, i.e. a dense population of macromolecules, is known to have a significant impact on the kinetics of molecules. However, an in-detail inspection of cell behavior in time and space is extremely costly. To balance between cost and accuracy, multi-resolution approaches offer one solution. Particularly, a combination of individual and lattice-population based algorithms promise an adequate treatment of phenomena like macromolecular crowding. In realizing such an approach, central questions are how to specify and synchronize the interaction between population and individual spatial level, and to decide what is best treated at a specific level, respectively. Based on an algorithm which combines the next subvolume method and a simple, individual-based spatial approach, we will present possible answers to these questions, and will discuss first experimental results.
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paper_title: Layering Social Interaction Scenarios on Environmental Simulation

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For an integrated simulation such as the natural environment affected by human society, it is indispensable to provide an integrated simulator that incorporates multiple computational models. We proposed a multi-layer socio-environmental simulation by layering the social interaction scenario on environmental simulation. For this simulation, we connect two different systems. One is a scenario description language Q, which is suitable for describing social interactions. Another is CORMAS, which models interactions between a natural environment and humans. The key idea is to realize a mapping between agents in different systems. This integration becomes possible by the salient feature of Q: users can write scenarios for controlling legacy agents in other systems. Moreover, we find that controlling the flow of information between the two systems can create various types of simulations. We also confirm the capability of CORMAS/Q, in the well-known Fire-Fighter domain.
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paper_title: A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis

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Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared. We have developed several models that spatially characterize the dynamics of the host-mycobacterial interaction, and identified mechanisms that control granuloma formation and development. In particular, we published several agent-based models (ABMs) of granuloma formation in TB that include many subtypes of T cell populations, macrophages as well as key cytokine and chemokine effector molecules. These ABM studies emphasize the important role of T-cell related mechanisms in infection progression, such as magnitude and timing of T cell recruitment, and macrophage activation. In these models, the priming and recruitment of T cells from the lung draining lymph node (LN) was captured phenomenologically. In addition to these ABM studies, we have also developed several multi-organ models using ODEs to examine trafficking of cells between, for example, the lung and LN. While we can predict temporal dynamic behaviors, those models are not coupled to the spatial aspects of granuloma. To this end, we have developed a multi-organ model that is hybrid: an ABM for the lung compartment and a non-linear system of ODE representing the lymph node compartment. This hybrid multi-organ approach to study TB granuloma formation in the lung and immune priming in the LN allows us to dissect protective mechanisms that cannot be achieved using the single compartment or multi-compartment ODE system. The main finding of this work is that trafficking of important cells known as antigen presenting cells from the lung to the lymph node is a key control mechanism for protective immunity: the entire spectrum of infection outcomes can be regulated by key immune cell migration rates. Our hybrid multi-organ implementation suggests that effector CD4+ T cells can rescue the system from a persistent infection and lead to clearance once a granuloma is fully formed. This could be effective as an immunotherapy strategy for latently infected individuals.
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paper_title: The IRM4S model: the influence/reaction principle for multiagent based simulation

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The IRM4S model (Influence Reaction Model for Simulation) is an adaptation of the formalism of [2] for multiagent based simulations (MABS). The goal of IRM4S is to provide a framework that eases the use of the Influence/Reaction principle within MABS.
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paper_title: GEAMAS: A Generic Architecture for Agent-Oriented Simulations of Complex Processes

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This paper's object is to present the results of the GEAMAS project which aims at modeling and simulating natural complex systems. GEAMAS is a generic architecture of agents used to study the behavior emergence in such systems. It is a multiagent program meant to develop simulation applications. Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agent-oriented approach, where interactions lead to a global behavior. This approach helps in understanding how non-determinist behavior can emerge from interactions between agents, which is near of self-organized criticality used to explain natural phenomena. In the Applied Artificial Intelligence context, this paper presents an agent software architecture using a model of agent. This architecture is composed of three abstract levels over which the complexity is distributed and reduced. The architecture is implemented in ReActalk, an open agent-oriented development tool, which was developed on top of Smalltalk-80. To illustrate our purpose and to validate the architecture, a simulation program to help in predicting volcanic eruptions was investigated. This program was run over a period of one year and has given many satisfying results unattainable up to there with more classical approaches.
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paper_title: Modeling agents and their environment in multi-level-DEVS

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Environments play an important role in multi-agent systems. They present the context agents operate in. When testing multi-agent systems by simulation, the environment and partly agents have to be modeled. We explore the potential of Multi-Level-DEVS to serve as a modeling formalism for agents, their environment, and the interaction between them. Multi-Level-DEVS combines a modular, hierarchical modeling with variable structures, dynamic interfaces, and explicit means for describing up- and downward causation between different levels of the compositional hierarchy. The modeling in Multi-Level-DEVS emphasizes the role of the environment to provide information for and enforce constrains on the situated agents. A smart meeting room scenario is modeled, and an approach aimed at recognizing user activities in smart environments is tested and evaluated in a simulation study.
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paper_title: The Ghost in the Machine

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Koestler examines the notion that the parts of the human brain-structure which account for reason and emotion are not fully coordinated. This kind of deficiency may explain the paranoia, violence, and insanity that are central parts of human history, according to Koestler's challenging analysis of the human predicament.
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paper_title: Scale transfer modeling: using emergent computation for coupling an ordinary differential equation system with a reactive agent model

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This article deals with the coupling of analytical models with individual based models design with the reactive agents paradigm. Such a coupling of models of different natures is motivated by the need to find a way to model scale transfer in large complex systems, i.e. to model how low level of organization can be made to influence upper level and vice versa. This is a fundamental issue, and more particularly in ecological modeling where models are a real scientific tool of investigation. Individuals and populations are not described at the same scale of time and space but it is known that they act on each others. Based on this example, we model individuals in their environment and the population dynamics. While behavior is best modeled using an algorithmic framework (the reactive agent paradigm), population dynamics (because of the number of interacting entities) is best modeled using numerical models. We propose the use of the concept of emergent computation as a framework for coupling heterogeneous formalisms. In the same time, it is crucial to be aware of the consequences of the simplifications and of the choices that are made in the reactive agent model, such as the topology of space and various parameters. In this article, we discuss these issues and our approach on a case study drawn from marine ecology and we show that it is possible to find classical mathematical functional responses with a reactive agent system. Then, we propose a methodology to deal with the coupling of heterogeneous formalism useful in any kind of system modeling.
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paper_title: Interaction-Oriented Agent Simulations: From Theory to Implementation

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This paper deals with the software architecture for individual-centered simulations, i.e. involving many entities interacting together. Many software architectures have been developped in this context, especially many advanced --but domain specific --frameworks. Yet those frameworks imply tight software dependencies between agents, behaviors and action selection mechanisms, which leads to many difficulties in modelling and programming. We propose a method and an architecture where interactions are reified regardless of agents, in order to obtain a complete interaction-oriented design process for simulations. Then, an agent is only an entity that can perform or undergo a set of interactions, even not specifically developped for it. Thus most interactions can be re-used in many contexts. In addition, our method clearly separates knowledge about behaviors from its processing, and thus makes the design of simulations easier. Moreover, this new and user-friendly approach helps programmers to build simulations with a large number of different behaviors at the same time, especially in the context of large-scale simulations.
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paper_title: Agents and artefacts for multiple models co-evolution Building complex system simulation as a set of interacting models

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Complex systems simulations generally involve the interaction of different scientific fields. Human economies, ecosystems or dynamic computer networks such as P2P are good examples. Since models and simulators already exist in those fields, designing the simulation as a society of interacting and co-evolving models appears attractive. Beyond the technical issues to make different simulators cooperate, the challenges are to make the co-evolution design and implementation easier for the scientist that rarely know intricate modelling and simulation tools, and to facilitate the collaboration of different experts. Agents and artefacts (A&A) paradigm simplifies the design and the implementation of a society of interacting and co-evolving models. That is, the addition, the removal or the interchange of models require less effort. Contrary to classical approaches, we have built a decentralized co-evolution architecture based upon A&A and a data-driven coordination model. In this article, beyond the architecture presentation, we focus on the benefit provided by A&A used for multiple models co-evolution.
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paper_title: IRM4MLS: the influence reaction model for multi-level simulation

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In this paper, a meta-model called IRM4MLS, that aims to be a generic ground to specify and execute multi-level agent-based models is presented. It relies on the influence/reaction principle and more specifically on IRM4S [13,14]. Simulation models for IRM4MLS are defined. The capabilities and possible extensions of the meta-model are discussed.
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paper_title: An Interaction-Oriented Model for Multi-Scale Simulation

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The design of multiagent simulations devoted to complex systems, addresses the issue of modeling behaviors that are involved at different space, time, behavior scales, each one being relevant so as to represent a feature of the phenomenon. We propose here a generic formalismintended to represent multiple environments, endowed with their own spatiotemporal scales and with behavioral rules for the agents they contain. An environment can be nested inside any agent, which itself is situated in one or more environments. This leads to a lattice decomposition of the global system, which appears to be necessary for an accurate design of multi-scale systems. This uniform representation of entities and behaviors at each abstraction level relies upon an interaction-oriented approach for the design of agent simulations, which clearly separates agents from interactions, from the modeling to the code. We also explain the implementation of our formalism within an existing interaction-based platform.
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paper_title: Combining micro and macro-modeling in DEVS for computational biology

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In computational biology there is an increasing need to combine micro and macro views of the system of interest. Therefore, explicit means to describe micro and macro level and the downward and upward causation that link both are required. Multi-Level-DEVS (or m^-DEVS) supports an explicit description of macro and micro level, information at macro level can be accessed from micro level and vice versa, micro models can be synchronously activated by the macro model and also the micro models can trigger the dynamics at macro level. To link both levels, different methods are combined, to those belong, value coupling, synchronous activations, variable ports, and invariants. The execution semantic of the formalism is given by an abstract simulator and its use is illustrated based on an small extract of the Wnt pathway.
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paper_title: An architecture for modular distributed simulation with agent-based models

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Agent-based simulations are an increasingly popular means of exploring and understanding complex social systems. In order to be useful, these simulations must capture a range of aspects of the modeled situation, each possibly requiring distinct expertise. Moreover, different paradigms may be useful in modelling, ranging from those that use many lightweight reactive agents, to those that use cognitive agents, to those that focus on agent teams and organisational structures. There is need for an architecture which supports the development of a large simulation, through the integration of separately developed modules. This paper describes a framework and architecture which facilitates the integration of multiple agent-based simulations into a single global simulation. This architecture naturally supports distributed simulation and incremental development, which are ways of addressing the computational and conceptual complexity of such systems. In this paper we focus particularly on how to ensure proper management of simulation data that is affected by agents in different modules, at the same logical time. We also provide some preliminary performance evaluation addressing scalability, as well as a comparison of how other available systems handle the issue of shared data.
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paper_title: Towards a formal semantics of event-based multi-agent simulations

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The aim of this paper is to define a non-ambiguous operational semantics for event-based multi-agent modeling and simulation, applied to complex systems. A number of features common to most multi-agent systems have been retained: 1) agent proactive as well as reactive behavior, 2) concurrency : events can arrive simultaneously to an agent, an environment or any simulated entity and the actual change only depends on the target according to the influence/reaction paradigm [1], 3) instantaneity : if reaction takes time, perception as well as information diffusion is instantaneous and should be processed separately, 4) structure dynamics : the interaction structure (who is talking to whom) changes over time, and the agents as well as any simulated entity may be created or destroyed in the course of the simulation. ::: ::: For each of these features, a solution inspired by the work on $\mathit{DEVS}$ (Discrete EVent Systems, [2]) is proposed. Proactive/reactive behavior is naturally taken into account by $\mathit{DEVS}$. Concurrency is dealt with using $/\!/\!\!-\!\!\mathit{DEVS}$ (in [2]), a variant of the pure $\mathit{DEVS}$. Instantaneity is managed by distinguishing the physical events producing state transitions and the logical events realizing only perception and information diffusion. The structure dynamics is achieved by using a variant of ρ -$\mathit{DEVS}$ (cf. [3]) where the expressiveness allows to manage hierarchical structures. The operational semantics is given as abstract algorithms and the expressive power of this formalism is illustrated on a simple example.
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paper_title: Theory of Modeling and Simulation

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From the Publisher: ::: Although twenty-five years have passed since the first edition of this classical text, the world has seen many advances in modeling and simulation, the need for a widely accepted framework and theoretical foundation is even more necessary today. Methods of modeling and simulation are fragmented across disciplines making it difficult to re-use ideas from other disciplines and work collaboratively in multidisciplinary teams. Model building and simulation has been made easier and faster by riding piggyback on advances in software and hardware. However, difficult and fundamental issues such as model credibility and interoperation have received less attention. These issues are now front and center under the impetus of the High Level Architecture (HLA) standard mandated by the U.S. DoD for all contractors and agencies. ::: This book concentrates on integrating the continuous and discrete paradigms for modeling and simulation. A second major theme is that of distributed simulation and its potential to support the co-existence of multiple formalisms in multiple model components. Prominent throughout are the fundamental concepts of modular and hierarchical model composition. ::: This edition presents a rigorous mathematical foundation for modeling and simulation. Also, it now provides a comprehensive framework for integrating the various simulation approaches employed in practice. Including such popular modeling methods as cellular automata, chaotic systems, hierarchical block diagrams, and Petri nets. A unifying concept, called the DEVS Bus, enables models, as expressed in their native formalisms, to be transparently mapped into the Discrete Event System Specification (DEVS). The book shows how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. If you are doing integrative simulations, whether or not they are HLA compliant, this is the only book available to provide the foundation to understand, simplify and successfully accomplish your task. ::: Herbert Praehofer is an Assistant Professor at the Johannes Kepler University in Linz, Austria. He has over 50 publications in international journals and conference proceedings on Modeling and Computer Simulation, Systems Theory, and Software Engineering. ::: Tag Gon Kim is a Professor of Electrical Engineering at the Korea Advanced Institutes of Science and Technology (KAIST), Taejon, Korea. His research interests include discrete event systems modeling/simulation, computer/communication systems analysis, and object-oriented simulation engineering. He is a senior member of IEEE and SCS, and a member of ACM. ::: * Provides a comprehensive framework for continuous and discrete event modeling and simulation ::: * Explores the mathematical foundation of simulation modeling ::: * Discusses system morphisms for model abstraction and simplification ::: * Presents a new approach to discrete event simulation of continuous processes ::: * Includes parallel and distributed simulation of discrete event models ::: * Presentation of a concept to achieve simulator interoperability in the form of the DEVS-Bus ::: * Complete coverage necessary for compliance with High Level Architecture (HLA) standards ::: ::: Bernard P Zeigler, is a Professor of Electrical & Computer Engineering at the University of Arizona and heads the Artificial Intelligence Simulation Research Group. He is the author of numerous books and publications, and he is the Editor-in-Chief of the Transactions of the Society for Computer Simulation International.
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paper_title: GAMA : bringing GIS and multi-level capabilities to multi-agent simulation

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The agent-based modeling is now widely used to study complex systems. Its ability to represent several levels of interaction along a detailed (complex) environment representation favored such a development. However, in many models, these capabilities are not fully used. Indeed, only simple, usually discrete, environment representation and one level of interaction (rarely two or three) are considered in most of the agent-based models. The major reason behind this fact is the lack of simulation platforms assisting the work of modelers in these domains. To tackle this problem, we developed a new simulation platform, GAMA. This platform allows modelers to define spatially explicit and multi-level models. In particular, it integrates powerful tools coming from Geographic Information Systems (GIS) and Data Mining easing the modeling and analysis efforts. In this paper, we present how this platform addresses these issues and how such tools are available right out of the box to modelers.
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paper_title: SPARK: a framework for multi-scale agent-based biomedical modeling

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Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. In this work, we describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a standalone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster. We are currently utilizing SPARK to develop multi-scale inflammation models in diverse settings such as cancer, viral infection, and spinal cord injury.
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paper_title: A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

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This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.
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paper_title: Hierarchical modeling for computational biology

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Diverse hierarchies play a role in modeling and simulation for computational biology, e.g. categories, abstraction hierarchies, and composition hierarchies. Composition hierarchies seem a natural and straightforward focus for our exploration. What are model components and the requirements for a composite approach? How far do they support the quest for building blocks in computational biology? Modeling formalisms provide different means for composing a model. We will illuminate this with DEVS (Discrete event systems specification) and the π calculus. Whereas in DEVS distinctions are emphasized, e.g. between a system and its environment, between properties attributed to a system and the system itself, these distinctions become fluent in the compact description of the π calculus. However, both share the problem that in order to support a comfortable modeling, a series of extensions have been developed which also influence their possibility to support a hierarchical modeling. Thus, not individual formalisms but two families of formalisms and how they support a composite modeling will be presented. In computational biology one type of composite model deserves a closer inspection, as it brings together the wish to compose models and the need to describe a system at different levels in a unique manner, i.e. multi-level models.
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paper_title: Multi-perspective modelling of complex phenomena

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This conceptual paper discusses the limitations of a single-perspective hierarchical approach to modelling and proposes multi-perspective modelling as a way to overcome them. As it turns out, multi-perspective modelling is primarily a new methodology, using existing modelling techniques but extending the modelling hierarchy with a new epistemological level which integrates the different perspectives. The methodology will be presented in some detail, and its use will be demonstrated by analyzing an example taken from a socio-political context.
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paper_title: An Operational Meta-Model for Handling Multiple Scales in Agent-Based Simulations

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There is a growing interest for multi-scale agent-based modeling. The research community has made several efforts in proposing agent-based simulators or meta-models, which accommodate for multi-scale agent-based modeling. Unfortunately, the proposed simulators are often tight to a specific model. And the proposed meta-models exist only as formal proposals without a concrete implementation supported by an operational modeling language. The lack of a common operational meta-model and an associated operational modeling language raises the question of reusability and increase the danger of “re-inventing the wheel” when one develops new models. Moreover, the operational semantics of such multi-scale models may differ without having defined a solid theoretical ground. Our research aims at proposing a generic meta-model for multi-scale agent-based modeling. In our approach, we revisit the fundamental notions of agent modeling (agent/environment/scheduler) in order to support a recursive representation of these notions in an agent-based model. To test the operational semantic of the proposed model, we implement this meta-model as an agent-based modeling language in the GAMA simulation platform. This paper presents such a metamodel and how its concepts can be used to formalize multi-scale agent-based models. Keywords-Agent-Based Modelling; Simulation; Multi-Scale; GAMA
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paper_title: GAMA: a simulation platform that integrates geographical information data, agent-based modeling and multi-scale control

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The agent-based modeling is now widely used to study complex systems. Its ability to represent several levels of interaction along a detailed (complex) environment representation favored such a development. However, in many models, these capabilities are not fully used. Indeed, only simple, usually discrete, environment representation and one level of interaction (rarely two or three) are considered in most of the agent-based models. The major reason behind this fact is the lack of simulation platforms assisting the work of modelers in these domains. To tackle this problem, we developed a new simulation platform, GAMA. This platform allows modelers to define spatially explicit and multi-levels models. In particular, it integrates powerful tools coming from Geographic Information Systems (GIS) and Data Mining easing the modeling and analysis efforts. In this paper, we present how this platform addresses these issues and how such tools are available right out of the box to modelers.
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paper_title: Multi-level agent-based modeling with the Influence Reaction principle

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This paper deals with the specification and the implementation of multi-level agent-based models, using a formal model, IRM4MLS (an Influence Reaction Model for Multi-Level Simulation), based on the Influence Reaction principle. Proposed examples illustrate forms of top-down control in (multi-level) multi-agent based-simulations.
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paper_title: Automatic characterization of emergent phenomena in complex systems

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One of the main characteristics of complex systems is that the interrelations between the entities composing the system are not permanently established but evolve along time. As opposed to complicated systems, the structure of complex systems also evolve in a dynamic organizational process. When studying complex systems, self-organization and emergent phenomena must therefore be taken into account and studied carefully. In this paper, we propose to provide tools in order to automatically detect and characterize the emergent phenomena occurring in agent-based simulations. To this end, we consider the interactions between the entities at the lower level as the main organizational forces that shape the structure of the system at a higher level. These interactions are detected during the simulation and represented as dynamic graphs. Measures can then be made on various properties of the graph so as to detect the occurrence of structuring processes. Groups detection and tracking techniques are then introduced so as to characterize more precisely the exact nature of these processes.
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paper_title: Vortex methods for flow simulation

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Abstract Recent progress in the development of vortex methods and their applications to the numerical simulation of incompressible fluid flows are reviewed. Emphasis is on recent results concerning the accuracy of these methods, improvements in computational efficiency, and the development of three-dimensional methods. Simulations of several example flows which display some of the strengths and weaknesses of vortex methods are presented.
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paper_title: Hybrid Traffic Model Coupling Macro- and Behavioral Microsimulation

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The dynamics of traffic flow are affected by phenomena occurring and interacting on a wide range of spatial dimensions. The scales involved range from individual vehicles through collective effects and up to the network level. A promising approach to this problem is the coupling of two different traffic models able to handle a wide range of phenomena. The result of this approach is a “hybrid” model. After the introduction of the general description and the fundamental properties of hybrid models, a hybrid model is presented, mixing ARCHISIM, a microscopic behavioural model, and SSMT, a shock-wave based macroscopic model. After a short description of the two models the coupling scheme is illustrated; it has been developed in order to allow a proper transmission and translation of the information between the presented models. Finally, a validation of the resulting model is proposed for three different traffic conditions and some possible future extensions to this model are shown.
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paper_title: Artificial Societies: Multiagent Systems and the Micro-Macro Link in Sociological Theory

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This article introduces a general sociological readership to multiagent systems (MAS), a new computer simulation technology that has increasingly been used to describe and explain sociological phen...
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paper_title: Simulation modelling of ecological hierarchies in constructive dynamical systems

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Organized complexity is a characteristic feature of ecological systems with heterogeneous components interacting at several spatio-temporal scales. The hierarchy theory is a powerful epistemological framework to describe such systems by decomposing them vertically into levels and horizontally into holons. It was at first developed in a temporal and functional perspective and then, in the context of landscape ecology, extended to a spatial and structural approach. So far, most ecological applications of this theory were restricted to observational purposes, using multi-scale analysis to describe hierarchies. In spite of an increasing attention to dynamics of hierarchically structured ecological systems, current simulation models are still very limited in their representation of self-organization in complex adaptive systems. An ontological conceptualization of the hierarchy theory is outlined, focusing on key concepts, such as levels of organization and the compound and component faces of the holons. Various existing formalisms are currently used in simulation modelling, such as system dynamics, discrete event and agent based paradigms. Their ability to express the hierarchical organization of dynamical ecological systems is discussed. It turns out that a multi-modelling approach linking all these formalisms and oriented toward the specification of a constructive dynamical system would be able to express the dynamical structure of the hierarchy (creation, destruction and change of holons) and the functional and structural links between levels of organization.
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paper_title: 2-Way evaluation of the distributed BeeJamA vehicle routing approach

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We present and evaluate our adaptive and distributed vehicle routing approach, termed BeeJamA, which provides drivers safely with routing directions well before each intersection. Our approach is based on a multi-agent system which is inspired by the honey bee behavior and relies on a V2I architecture. We report on our extensive simulation experiments verifying for very large systems that BeeJamA substantially outperforms all A∗-based algorithms relying on global information systems, in particular under all degrees of penetration rates as well as considering reactive flexibility and easy scalability.
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paper_title: Scale transfer modeling: using emergent computation for coupling an ordinary differential equation system with a reactive agent model

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This article deals with the coupling of analytical models with individual based models design with the reactive agents paradigm. Such a coupling of models of different natures is motivated by the need to find a way to model scale transfer in large complex systems, i.e. to model how low level of organization can be made to influence upper level and vice versa. This is a fundamental issue, and more particularly in ecological modeling where models are a real scientific tool of investigation. Individuals and populations are not described at the same scale of time and space but it is known that they act on each others. Based on this example, we model individuals in their environment and the population dynamics. While behavior is best modeled using an algorithmic framework (the reactive agent paradigm), population dynamics (because of the number of interacting entities) is best modeled using numerical models. We propose the use of the concept of emergent computation as a framework for coupling heterogeneous formalisms. In the same time, it is crucial to be aware of the consequences of the simplifications and of the choices that are made in the reactive agent model, such as the topology of space and various parameters. In this article, we discuss these issues and our approach on a case study drawn from marine ecology and we show that it is possible to find classical mathematical functional responses with a reactive agent system. Then, we propose a methodology to deal with the coupling of heterogeneous formalism useful in any kind of system modeling.
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paper_title: Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: Incorporating EGFR signaling pathway and angiogenesis

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BackgroundThe epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment.ResultsThe novel angiogenesis module integrated into the agent-based tumor model is based on a set of reaction–diffusion equations that describe the spatio-temporal evolution of the distributions of micro-environmental factors such as glucose, oxygen, TGFα, VEGF and fibronectin. These molecular species regulate tumor growth during angiogenesis. Each tumor cell is equipped with an EGFR signaling pathway linked to a cell-cycle pathway to determine its phenotype. EGFR TKIs are delivered through the blood vessels of tumor microvasculature and the response to treatment is studied.ConclusionsOur simulations demonstrated that entire tumor growth profile is a collective behaviour of cells regulated by the EGFR signaling pathway and the cell cycle. We also found that angiogenesis has a dual effect under TKI treatment: on one hand, through neo-vasculature TKIs are delivered to decrease tumor invasion; on the other hand, the neo-vasculature can transport glucose and oxygen to tumor cells to maintain their metabolism, which results in an increase of cell survival rate in the late simulation stages.
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paper_title: Multiscale MAS modelling to simulate the soil environment: Application to soil ecology

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Abstract Soils are important components of ecosystem and their functioning is of great importance for human well-being. Describing, understanding, foreseeing, and controlling biological activities and functions in soil in the context of global change are major objectives for scientists. Modelling soil bioturbation by soil engineers is of great importance although it is faced with the difficulty to describe the soil physical environment. Creating a model of a soil leads to complexity problems: a soil is a multi-scale heterogeneous, three-dimensional and dynamic environment that cannot be modelled and simulated without defining a suitable and optimized representation and taking assumptions about the studied system. An approach based on fractal theory (often used in soil sciences) was chosen to model such a real complex environment; it was integrated into a multi-agent system (MAS). MAS allows to simulate situated agents (earthworms) in an virtual world. The originality of this present MAS is that it is based on a dynamic environment which builds itself, on demand, according to an abstract canvas tree and agent movements. The aim of this paper is to present this approach and its originality, and to describe the model and the simulator. A theoretical view of the approach is given and applied to a case study: the impact of earthworms on soil structure and organic matter dynamics.
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paper_title: A multiscale mechanobiological modelling framework using agent-based models and finite element analysis: application to vascular tissue engineering

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Computational models of mechanobiological systems have been widely used to provide insight into these systems and also to predict their behaviour. In this context, vascular tissue engineering benefits from further attention given the challenges involved in developing functional low calibre vascular grafts with long-term patency. In this study, a novel multiscale mechanobiological modelling framework is presented, which takes advantage of lattice-free agent-based models coupled with the finite element method to investigate the dynamics of VSMC growth in vascular tissue engineering scaffolds. The results illustrate the ability of the mechanobiological modelling approach to capture complex multiscale mechanobiological phenomena. Specifically, the framework enabled the study of the influence of scaffold compliance and loading regime in regulating the growth of VSMCs in vascular scaffolds and their role in development of intimal hyperplasia (IH). The model demonstrates that low scaffold compliance compared to host arteries leads to increased luminal ingrowth and IH development. In addition, culture of a tissue-engineered blood vessel under a pulsatile luminal pressure reduced luminal ingrowth and enhanced collagen synthesis within the scaffold compared to non-pulsatile culture. The mechanobiological framework presented provides a robust platform for testing hypotheses in vascular tissue engineering and lends itself to use as an optimisation design tool.
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paper_title: Interactive hybrid simulation of large-scale traffic

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We present a novel, real-time algorithm for modeling large-scale, realistic traffic using a hybrid model of both continuum and agent-based methods for traffic simulation. We simulate individual vehicles in regions of interest using state-of-the-art agent-based models of driver behavior, and use a faster continuum model of traffic flow in the remainder of the road network. Our key contributions are efficient techniques for the dynamic coupling of discrete vehicle simulation with the aggregated behavior of continuum techniques for traffic simulation. We demonstrate the flexibility and scalability of our interactive visual simulation technique on extensive road networks using both real-world traffic data and synthetic scenarios. These techniques demonstrate the applicability of hybrid techniques to the efficient simulation of large-scale flows with complex dynamics.
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paper_title: Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units

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Multiscale agent-based modeling (MABM) has been widely used to simulate Glioblastoma Multiforme (GBM) and its progression. At the intracellular level, the MABM approach employs a system of ordinary differential equations to describe quantitatively specific intracellular molecular pathways that determine phenotypic switches among cells (e.g. from migration to proliferation and vice versa). At the intercellular level, MABM describes cell-cell interactions by a discrete module. At the tissue level, partial differential equations are employed to model the diffusion of chemoattractants, which are the input factors of the intracellular molecular pathway. Moreover, multiscale analysis makes it possible to explore the molecules that play important roles in determining the cellular phenotypic switches that in turn drive the whole GBM expansion. However, owing to limited computational resources, MABM is currently a theoretical biological model that uses relatively coarse grids to simulate a few cancer cells in a small slice of brain cancer tissue. In order to improve this theoretical model to simulate and predict actual GBM cancer progression in real time, a graphics processing unit (GPU)-based parallel computing algorithm was developed and combined with the multi-resolution design to speed up the MABM. The simulated results demonstrated that the GPU-based, multi-resolution and multiscale approach can accelerate the previous MABM around 30-fold with relatively fine grids in a large extracellular matrix. Therefore, the new model has great potential for simulating and predicting real-time GBM progression, if real experimental data are incorporated.
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paper_title: Exploring Hypotheses of the Actions of TGF-β1 in Epidermal Wound Healing Using a 3D Computational Multiscale Model of the Human Epidermis

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In vivo and in vitro studies give a paradoxical picture of the actions of the key regulatory factor TGF-β1 in epidermal wound healing with it stimulating migration of keratinocytes but also inhibiting their proliferation. To try to reconcile these into an easily visualized 3D model of wound healing amenable for experimentation by cell biologists, a multiscale model of the formation of a 3D skin epithelium was established with TGF-β1 literature–derived rule sets and equations embedded within it. At the cellular level, an agent-based bottom-up model that focuses on individual interacting units (keratinocytes) was used. This was based on literature-derived rules governing keratinocyte behavior and keratinocyte/ECM interactions. The selection of these rule sets is described in detail in this paper. The agent-based model was then linked with a subcellular model of TGF-β1 production and its action on keratinocytes simulated with a complex pathway simulator. This multiscale model can be run at a cellular level only or at a combined cellular/subcellular level. It was then initially challenged (by wounding) to investigate the behavior of keratinocytes in wound healing at the cellular level. To investigate the possible actions of TGF-β1, several hypotheses were then explored by deliberately manipulating some of these rule sets at subcellular levels. This exercise readily eliminated some hypotheses and identified a sequence of spatial-temporal actions of TGF-β1 for normal successful wound healing in an easy-to-follow 3D model. We suggest this multiscale model offers a valuable, easy-to-visualize aid to our understanding of the actions of this key regulator in wound healing, and provides a model that can now be used to explore pathologies of wound healing.
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paper_title: A hybrid simulation model for studying acute inflammatory response

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The modeling of complex biological systems presents a significant challenge. Central to this challenge is striking a balance between the degree of abstraction required to facilitate analysis and understanding, and the degree of comprehensiveness required for fidelity of the model to its reference-system. It is likely necessary to utilize multiple modeling methods in order to achieve this balance. Our research created a hybrid simulation model by melding an agent-based model of acute local infection with a system dynamics model that reflects key systemic properties. The agent based model was originally developed to simulate global inflammation in response to injury or infection, and has been used to simulate clinical drug trials. The long term objective is to develop models than can be scaled up to represent organ and system level phenomena such as multiple organ failure associated with severe sepsis. The work described in this paper is an initial proof of concept of the ability to combine these two modeling methods into a hybrid model, the type of which will almost certainly be needed to accomplish the ultimate objective of comprehensive in silico research platforms.
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paper_title: Simulating Emergence and Downward Causation in Small Groups

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Emergence has been a central issue not only in computational models of social systems, but also throughout 20th century sociological theory. In this paper, I first define the key concepts of emergence, downward causation, and bi-directional causation, and I identify these processes in an example of improvised dialog. I then draw on emergentist trends in sociological theory to propose a computational model of collaborative emergence among small groups of improvising agents.
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paper_title: A hybrid macro-micro pedestrians evacuation model to speed up simulation in road networks

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The major methodologies of crowd simulation in a dynamic environments are either based on micro or macro models. These two types of models represent the trade-off between the level of detail and efficiency. The domain of pedestrian flow simulation on road networks is no exception and theories rely either on equation based model or agent based models. There is a growing interest in hybrid modeling that combines both of these types. This paper addresses the problem of combining both micro and macro models of pedestrians movement to speedup simulations. Hybrid model uses efficient macro modeling in part of the road networks that do not require a fine grained model and more detailed but less efficient micro modeling in critical locations. One key issue raised by such an approach and discussed is the consistency of the resulting hybrid model. Preliminary results presented in this article is a proof of concept that the use of hybrid model to simulate evacuation plan in road networks may be more efficient than the use of micro model alone.
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paper_title: Multiscale cancer modeling.

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Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.
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paper_title: A Multiscale Mathematical Model for Oncolytic Virotherapy

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One of the most promising strategies to treat cancer is attacking it with viruses. Oncolytic viruses can kill tumor cells specifically or induce anticancer immune response. A multiscale model for virotherapy of cancer is investigated through simulations. It was found that, for intratumoral virus administration, a solid tumor can be completely eradicated or keep growing after a transient remission. Furthermore, the model reveals undamped oscillatory dynamics of tumor cells and virus populations, which demands new in vivo and in vitro quantitative experiments aiming to detect this oscillatory response. The conditions for which each one of the different tumor responses dominates, as well as the occurrence probabilities for the other nondominant therapeutic outcomes, were determined. From a clinical point of view, our findings indicate that a successful, single agent virotherapy requires a strong inhibition of the host immune response and the use of potent virus species with a high intratumoral mobility. Moreover, due to the discrete and stochastic nature of cells and their responses, an optimal range for viral cytotoxicity is predicted because the virotherapy fails if the oncolytic virus demands either a too short or a very large time to kill the tumor cell. This result suggests that the search for viruses able to destroy tumor cells very fast does not necessarily lead to a more effective control of tumor growth. [Cancer Res 2009;69(3):1205–11]
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paper_title: Hybrid Model Based on Second-Order Traffic Model

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The behaviour of traffic phenomena is described through three types of approaches. The first approach is macroscopic, based on the analogy with fluid mechanics through three basic variables: the flow rate q, the density p and the speed v. The second approach is microscopic, enabling one to point out the interaction phenomena between vehicles, through the vehicle speed v, the position x and the acceleration y. The third approach, which is the hybrid approach, is less known in the traffic domain. It makes it possible to combine the macroscopic and microscopic approaches. This paper proposes a hybrid model, based on a second order macroscopic modelling principle. As a first step, the models to be coupled will be chosen, and the analytical solution of the second order macroscopic model will be carried out. This model will be reformulated under the form of a supply and demand function, similar to the model by Lighthill-Witham-Richards. More precisely, two scenarios will be studied: first a hybrid model in the case of traffic at equilibrium, which will enable us to do a comparative study with an existing hybrid model in the literature. As a second step, the transition from a macroscopic representation of traffic to a microscopic representation in the conditions of an out of equilibrium traffic will be studied. The validation of the proper transmission of information from one traffic vision to another will be done, studying the propagation of congestion from upstream to downstream.
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paper_title: Multiscale agent-based consumer market modeling

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Consumer markets have been studied in great depth, and many techniques have been used to represent them. These have included regression-based models, logit models, and theoretical market-level models, such as the NBD-Dirichlet approach. Although many important contributions and insights have resulted from studies that relied on these models, there is still a need for a model that could more holistically represent the interdependencies of the decisions made by consumers, retailers, and manufacturers. When the need is for a model that could be used repeatedly over time to support decisions in an industrial setting, it is particularly critical. Although some existing methods can, in principle, represent such complex interdependencies, their capabilities might be outstripped if they had to be used for industrial applications, because of the details this type of modeling requires. However, a complementary method—agent-based modeling—shows promise for addressing these issues. Agent-based models use business-driven rules for individuals (e.g., individual consumer rules for buying items, individual retailer rules for stocking items, or individual firm rules for advertizing items) to determine holistic, system-level outcomes (e.g., to determine if brand X's market share is increasing). We applied agent-based modeling to develop a multi-scale consumer market model. We then conducted calibration, verification, and validation tests of this model. The model was successfully applied by Procter & Gamble to several challenging business problems. In these situations, it directly influenced managerial decision making and produced substantial cost savings. © 2010 Wiley Periodicals, Inc. Complexity, 2010
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paper_title: Translational Systems Biology of Inflammation

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Inflammation is a complex, multi-scale biologic response to stress that is also required for repair and regeneration after injury. Despite the repository of detailed data about the cellular and molecular processes involved in inflammation, including some understanding of its pathophysiology, little progress has been made in treating the severe inflammatory syndrome of sepsis. To address the gap between basic science knowledge and therapy for sepsis, a community of biologists and physicians is using systems biology approaches in hopes of yielding basic insights into the biology of inflammation. "Systems biology" is a discipline that combines experimental discovery with mathematical modeling to aid in the understanding of the dynamic global organization and function of a biologic system (cell to organ to organism). We propose the term translational systems biology for the application of similar tools and engineering principles to biologic systems with the primary goal of optimizing clinical practice. We describe the efforts to use translational systems biology to develop an integrated framework to gain insight into the problem of acute inflammation. Progress in understanding inflammation using translational systems biology tools highlights the promise of this multidisciplinary field. Future advances in understanding complex medical problems are highly dependent on methodological advances and integration of the computational systems biology community with biologists and clinicians.
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paper_title: Multiscale agent-based cancer modeling

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Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.
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paper_title: Modeling Dynamic Multiscale Social Processes in Agent-Based Models

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Identity-related issues play central roles in many current events, including those involving factional politics, sectarianism, and tribal conflicts. Two popular models from the computational-social-science (CSS) literature - the threat anticipation program and SharedID models - incorporate notions of identity (individual and collective) and processes of identity formation. A multiscale conceptual framework that extends some ideas presented in these models and draws other capabilities from the broader CSS literature is useful in modeling the formation of political identities. The dynamic, multiscale processes that constitute and transform social identities can be mapped to expressive structures of the framework.
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paper_title: Development of a Three-Dimensional Multiscale Agent-Based Tumor Model: Simulating Gene-Protein Interaction Profiles, Cell Phenotypes&Multicellular Patterns in Brain Cancer

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Experimental evidence suggests that epidermal growth factor receptor (EGFR)-mediated activation of the signaling protein phospholipase C gamma plays a critical role in a cancer cell's phenotypic decision to either proliferate or to migrate at a given point in time. Here, we present a novel three-dimensional multiscale agent-based model to simulate this cellular decision process in the context of a virtual brain tumor. Each tumor cell is equipped with an EGFR gene-protein interaction network module that also connects to a simplified cell cycle description. The simulation results show that over time proliferative and migratory cell populations not only oscillate but also directly impact the spatio-temporal expansion patterns of the entire cancer system. The percentage change in the concentration of the sub-cellular interaction network's molecular components fluctuates, and, for the proliferation-to-migration switch we find that the phenotype triggering molecular profile to some degree varies as the tumor system grows and the microenvironment changes. We discuss potential implications of these findings for experimental and clinical cancer research.
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paper_title: Theoretical considerations on the combined use of System Dynamics and individual-based modeling in ecology

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Modeling could be summed up as the task of reproducing the structure and imitating the behavior of complex real-life systems with components interacting with one another at different scales. In many disciplines of ecology, System Dynamics and more recently Individual-Based modeling have emerged as the major tools to support this task. These techniques have usually been considered until now as exclusive alternatives instead of synergistic tools. The present paper starts by presenting the two approaches, and compares them to identify their strong and weak points depending on the type of components constituting the system under consideration. Then we isolate a class of systems difficult or in some cases impossible to model dynamically using any of these approaches alone, because of conceptual limitations. We further point out the usefulness of merging the two paradigms inside of a hybrid modeling framework to handle this class of systems, and present what we consider as the elementary combination patterns of System Dynamics and Individual-Based modeling. Since the power of this promising approach has been unexplored in most fields of ecology, we suggest some possible applications illustrating its usefulness.
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paper_title: Simulating non-small cell lung cancer with a multiscale agent-based model

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Background The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silcio modeling is considered to be an increasingly promising tool to add useful insights into the dynamics of the EGFR signal transduction pathway. However, most of the previous modeling work focused on the molecular or the cellular level only, neglecting the crucial feedback between these scales as well as the interaction with the heterogeneous biochemical microenvironment. ::: Results We developed a multiscale model for investigating expansion dynamics of NSCLC within a two-dimensional in silico microenvironment. At the molecular level, a specific EGFR-ERK intracellular signal transduction pathway was implemented. Dynamical alterations of these molecules were used to trigger phenotypic changes at the cellular level. Examining the relationship between extrinsic ligand concentrations, intrinsic molecular profiles and microscopic patterns, the results confirmed that increasing the amount of available growth factor leads to a spatially more aggressive cancer system. Moreover, for the cell closest to nutrient abundance, a phase-transition emerges where a minimal increase in extrinsic ligand abolishes the proliferative phenotype altogether. ::: Conclusions Our in silico results indicate that, in NSCLC, in the presence of a strong extrinsic chemotactic stimulus, and depending on the cell's location, downstream EGFR-ERK signaling may be processed more efficiently, thereby yielding a migration-dominant cell phenotype and overall, an accelerated spatio-temporal expansion rate.
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paper_title: The Epitheliome Project: multiscale agent-based modeling of epithelial cells

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The aim of the Epitheliome Project is to develop a computational model of cell behaviour within the context of tissue architecture, differentiation, wound repair and malignancy. Integration is central to tissue development for skin, integration between mesenchymal and epithelial tissue; at the physical level from focal adhesions and adherens junctions to cytoskeleton to whole tissue; at the signaling level from control of gene expression through to development of gradients controlling histodifferentiation; and integration in the sense of the development of tissues as an emergent property of cell behaviour. An individual-based model of the cell is used, and tissue development is thus a result of the interaction of individual cells. The limit of a large number of individual cells is a continuum model of tissue, and individual cells can contain continuum or differential equation models of cellular mechanisms. The hierarchical modeling paradigm is described.
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paper_title: A Multi-scale Agent-Based Distributed Simulation Framework for Groundwater Pollution Management

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Groundwater is like dark matter -- we know very little apart from the fact that it is hugely important. Given the scarcity of data, mathematical modelling can come to the rescue but existing groundwater models are mainly restricted to simulate the transport and degradation of contaminants on the scale of whole contaminated field sites by averaging out the effect of spatial heterogeneity on the availability of the pollutant to the degrading organisms. These coarse-scale mean-field models therefore tend to rely on fitting to data rather than being predictive. Also, they are less suited to incorporate spatial variability and non-linear kinetics and feedbacks. We propose to solve the two mutually exacerbating problems of environmental patchiness and data scarcity by developing a flexible and robust distributed simulation framework that uses an ensemble of small scale simulations running on different processors/computers to scale-up, i.e. to feed the effect of small-scale patchiness into a concurrent site-scale simulation of the dynamics of groundwater pollutant degradation. Our scaling approach solves problem #1 by simulating dynamics also on the small scale where some of the patchiness resides, and problem #2 by enabling rigorous validation of our small-scale model and scaling approach with laboratory data, which are high quality at low cost.
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paper_title: Dynamic level of detail for large scale agent-based urban simulations

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Large scale agent-based simulations typically face a trade-off between the level of detail in the representation of each agent and the scalability seen as the number of agents that can be simulated with the computing resources available. In this paper, we aim at bypassing this trade-off by considering that the level of detail is itself a parameter that can be adapted automatically and dynamically during the simulation, taking into account elements such as user focus, or specific events. We introduce a framework for such a methodology, and detail its deployment within an existing simulator dedicated to the simulation of urban infrastructures. We evaluate the approach experimentally along two criteria: (1) the impact of our methodology on the resources (CPU use), and (2) an estimate of the dissimilarity between the two modes of simulation, i.e. with and without applying our methodology. Initial experiments show that a major gain in CPU time can be obtained for a very limited loss of consistency.
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paper_title: Combining farmers’ decision rules and landscape stochastic regularities for landscape modelling

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Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called “landscape agronomy”, aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.
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paper_title: Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation

paper_content:

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems.
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paper_title: Hybrid Equation-based and Agent-based Modeling of Crowd Evacuation on Road Network

paper_content:

The major methodologies of crowd simulation in dynamic environment are either based on micro and macro models. Each of the two types of model represent choices in the trade-off between level of details and efficiency. The domain of pedestrian flow simulation in road networks is no exception and theories rely either on equation based model (LWR) or agent based models. There is a growing interest for hybrid modeling that combines both models together. This paper addresses the problem of combining both micro and macro models of pedestrians to speedup identification of optimal evacuation plan. The goal is therefor to use efficient macro modeling in part of the road networks that do not require fine grained model and less efficient but more detailed micro modeling elsewhere. The key issue raised by such an approach is to demonstrate the consistency of the resulting hybrid model. Preliminary results presented in this article are a proof of concept of how important speed up may be obtained using hybrid model to simulate evacuation plan in road networks.
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paper_title: Two-level modeling framework for pedestrian route choice and walking behaviors

paper_content:

Abstract A microscopic two-level simulation modeling framework is proposed to analyze both decision-making processes at a crosswalk as well as physical interactions among pedestrians when they cross a street. The model at the higher level is based on Decision Field Theory to represent the psychological preferences of pedestrians with respect to different route choice options during their deliberation process after evaluating current surroundings. At the lower level, physical interactions among pedestrians and consequent congestions are represented using a Cellular Automata model, in which pedestrians are allowed biased random-walking without back step towards their destination that has been given by the higher level model. A typical crosswalk with split sidewalks in the Chicago Loop area is employed as a case study, which has been implemented in AnyLogic® software. Weekday pedestrian counts on the 15-min basis near the studied crosswalk have been collected and used to construct and validate the simulation models. Experiments have been conducted to investigate the impact of corresponding environment parameters, such as pedestrian types and green/red phase length, as well as social parameters such as leadership in group decision making, on the average pedestrian waiting time at the crosswalk. Initial results look quite interesting. An extension on coupling the proposed pedestrian model with a transportation simulation model is also briefly discussed.
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paper_title: Agent-based modeling of competence phenotype switching in Bacillus subtilis

paper_content:

BackgroundIt is a fascinating phenomenon that in genetically identical bacteria populations of Bacillus subtilis, a distinct DNA uptake phenotype called the competence phenotype may emerge in 10–20% of the population. Many aspects of the phenomenon are believed to be due to the variable expression of critical genes: a stochastic occurrence termed “noise” which has made the phenomenon difficult to examine directly by lab experimentation.MethodsTo capture and model noise in this system and further understand the emergence of competence both at the intracellular and culture levels in B. subtilis, we developed a novel multi-scale, agent-based model. At the intracellular level, our model recreates the regulatory network involved in the competence phenotype. At the culture level, we simulated growth conditions, with our multi-scale model providing feedback between the two levels.ResultsOur model predicted three potential sources of genetic “noise”. First, the random spatial arrangement of molecules may influence the manifestation of the competence phenotype. In addition, the evidence suggests that there may be a type of epigenetic heritability to the emergence of competence, influenced by the molecular concentrations of key competence molecules inherited through cell division. Finally, the emergence of competence during the stationary phase may in part be due to the dilution effect of cell division upon protein concentrations.ConclusionsThe competence phenotype was easily translated into an agent-based model – one with the ability to illuminate complex cell behavior. Models such as the one described in this paper can simulate cell behavior that is otherwise unobservable in vivo, highlighting their potential usefulness as research tools.
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paper_title: Multi-scale agent-based modeling on melanoma and its related angiogenesis analysis

paper_content:

BackgroundRecently, melanoma has become the most malignant and commonly occurring skin cancer. Melanoma is not only the major source (75%) of deaths related to skin cancer, but also it is hard to be treated by the conventional drugs. Recent research indicated that angiogenesis is an important factor for tumor initiation, expansion, and response to therapy. Thus, we proposed a novel multi-scale agent-based computational model that integrates the angiogenesis into tumor growth to study the response of melanoma cancer under combined drug treatment.ResultsOur multi-scale agent-based model can simulate the melanoma tumor growth with angiogenesis under combined drug treatment. The significant synergistic effects between drug Dox and drug Sunitinib demonstrated the clinical potential to interrupt the communication between melanoma cells and its related vasculatures. Also, the sensitivity analysis of the model revealed that diffusivity related to the micro-vasculatures around tumor tissues closely correlated with the spread, oscillation and destruction of the tumor.ConclusionsSimulation results showed that the 3D model can represent key features of melanoma growth, angiogenesis, and its related micro-environment. The model can help cancer researchers understand the melanoma developmental mechanism. Drug synergism analysis suggested that interrupting the communications between melanoma cells and the related vasculatures can significantly increase the drug efficacy against tumor cells.
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paper_title: Multi-scale simulations of the dynamics of in-stent restenosis: impact of stent deployment and design

paper_content:

Neointimal hyperplasia, a process of smooth muscle cell re-growth, is the result of a natural wound healing response of the injured artery after stent deployment. Excessive neointimal hyperplasia following coronary artery stenting results in in-stent restenosis (ISR). Regardless of recent developments in the field of coronary stent design, ISR remains a significant complication of this interventional therapy. The influence of stent design parameters such as strut thickness, shape and the depth of strut deployment within the vessel wall on the severity of restenosis has already been highlighted but the detail of this influence is unclear. These factors impact on local haemodynamics and vessel structure and affect the rate of neointima formation. This paper presents the first results of a multi-scale model of ISR. The development of the simulated restenosis as a function of stent deployment depth is compared with an in vivo porcine dataset. Moreover, the influence of strut size and shape is investigated, and the effect of a drug released at the site of injury, by means of a drug-eluting stent, is also examined. A strong correlation between strut thickness and the rate of smooth muscle cell proliferation has been observed. Simulation results also suggest that the growth of the restenotic lesion is strongly dependent on the stent strut cross-sectional profile.
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paper_title: Toward a Spatially-Centered Approach to Integrate Heterogeneous and Multi-scales Urban Component Models

paper_content:

This article addresses a model coupling based approach (i.e reusing and combining spatial models) for modeling and simulating complex systems. Our research is conducted by a land use program of Metouia city (Tunisia) for which administration would study (by simulations) different planning scenarios to identify strategies of industrial development. These simulations should take into account demographic, socio-economic and environmental factors. Many urban models are available but they do not integrate these three aspects. This limitation could be solved by a model coupling based approach. In this paper, from an analysis of models and approaches presented in the literature, we identify key points, needs and the basis of an approach to couple models. Then, we introduce an original approach, based on agent paradigm, in which space is the coupling factor to interconnect heterogeneous models (mathematical models, stochastic models, individual based models, and so on). The pertinence of this coupling approach is also raised by the correlation to observe the impact of models on each other.
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paper_title: An individual-based process model to simulate landscape-scale forest ecosystem dynamics

paper_content:

a b s t r a c t Forest ecosystem dynamics emerges from nonlinear interactions between adaptive biotic agents (i.e., individual trees) and their relationship with a spatially and temporally heterogeneous abiotic environ- ment. Understanding and predicting the dynamics resulting from these complex interactions is crucial for the sustainable stewardship of ecosystems, particularly in the context of rapidly changing environmental conditions. Here we present iLand (the individual-based forest landscape and disturbance model), a novel approach to simulating forest dynamics as an emergent property of environmental drivers, ecosystem processes and dynamic interactions across scales. Our specific objectives were (i) to describe the model, in particular its novel approach to simulate spatially explicit individual-tree competition for resources over large scales within a process-based framework of physiological resource use, and (ii) to present a suite of evaluation experiments assessing iLands ability to simulate tree growth and mortality for a wide range of forest ecosystems. Adopting an approach rooted in ecological field theory, iLand calculates a continuous field of light availability over the landscape, with every tree represented by a mechanistically derived, size- and species-dependent pattern of light interference. Within a hierarchical multi-scale framework productivity is derived at stand-level by means of a light-use efficiency approach, and downscaled to indi- viduals via local light availability. Allocation (based on allometric ratios) and mortality (resulting from carbon starvation) are modeled at the individual-tree level, accounting for adaptive behavior of trees in response to their environment. To evaluate the model we conducted simulations over the extended envi- ronmental gradient of a longitudinal transect in Oregon, USA, and successfully compared results against independently observed productivity estimates (63.4% of variation explained) and mortality patterns in even-aged stands. This transect experiment was furthermore replicated for a different set of species and ecosystems in the Austrian Alps, documenting the robustness and generality of our approach. Model performance was also successfully evaluated for structurally and compositionally complex old-growth forests in the western Cascades of Oregon. Finally, the ability of our approach to address forest ecosystem dynamics at landscape scales was demonstrated by a computational scaling experiment. In simulating the emergence of ecosystem patterns and dynamics as a result of complex process interactions across scales our approach has the potential to contribute crucial capacities to understanding and fostering forest ecosystem resilience under changing climatic conditions.
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paper_title: Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis

paper_content:

Background ::: There is a growing realization that alterations in host-pathogen interactions (HPI) can generate disease phenotypes without pathogen invasion. The gut represents a prime region where such HPI can arise and manifest. Under normal conditions intestinal microbial communities maintain a stable, mutually beneficial ecosystem. However, host stress can lead to changes in environmental conditions that shift the nature of the host-microbe dialogue, resulting in escalation of virulence expression, immune activation and ultimately systemic disease. Effective modulation of these dynamics requires the ability to characterize the complexity of the HPI, and dynamic computational modeling can aid in this task. Agent-based modeling is a computational method that is suited to representing spatially diverse, dynamical systems. We propose that dynamic knowledge representation of gut HPI with agent-based modeling will aid in the investigation of the pathogenesis of gut-derived sepsis.
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paper_title: MACRO-MICRO SIMULATION OF TRAFFIC FLOW

paper_content:

Abstract This paper presents the recent progress in the development of the hybrid concept for the traffic simulation. The main objective is to couple two traffic representation models with different scale levels. The first allows a global traffic representation providing the possibility to simulate a large network. The second model focuses on the individual interactions of vehicles and allows then to understand the phenomena of the microscopic point of view. The paper shows that the proposed approach is generic allowing the cohabitation of various macroscopic models with the microscopic model developed using the agent paradigm. It presents also some simulation results that illustrate the relevance of the elaborated approach.
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paper_title: A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

paper_content:

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.
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paper_title: Multi-perspective modelling of complex phenomena

paper_content:

This conceptual paper discusses the limitations of a single-perspective hierarchical approach to modelling and proposes multi-perspective modelling as a way to overcome them. As it turns out, multi-perspective modelling is primarily a new methodology, using existing modelling techniques but extending the modelling hierarchy with a new epistemological level which integrates the different perspectives. The methodology will be presented in some detail, and its use will be demonstrated by analyzing an example taken from a socio-political context.
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paper_title: Hybrid models of tumor growth

paper_content:

Cancer is a complex, multiscale process in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches that can handle multiple intracellular and extracellular factors acting on different time and space scales. Hybrid models provide a way to integrate both discrete and continuous variables that are used to represent individual cells and concentrationordensityfields,respectively.Eachdiscretecellcanalsobeequipped with submodels that drive cell behavior in response to microenvironmental cues. Moreover, the individual cells can interact with one another to form and act as an integrated tissue. Hybrid models form part of a larger class of individualbased models that can naturally connect with tumor cell biology and allow for the integration of multiple interacting variables both intrinsically and extrinsically and are therefore perfectly suited to a systems biology approach to tumor
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paper_title: A multiscale modeling approach to inflammation: A case study in human endotoxemia

paper_content:

Inflammation is a critical component in the body's response to injury. A dysregulated inflammatory response, in which either the injury is not repaired or the inflammatory response does not appropriately self-regulate and end, is associated with a wide range of inflammatory diseases such as sepsis. Clinical management of sepsis is a significant problem, but progress in this area has been slow. This may be due to the inherent nonlinearities and complexities in the interacting multiscale pathways that are activated in response to systemic inflammation, motivating the application of systems biology techniques to better understand the inflammatory response. Here, we review our past work on a multiscale modeling approach applied to human endotoxemia, a model of systemic inflammation, consisting of a system of compartmentalized differential equations operating at different time scales and through a discrete model linking inflammatory mediators with changing patterns in the beating of the heart, which has been correlated with outcome and severity of inflammatory disease despite unclear mechanistic underpinnings. Working towards unraveling the relationship between inflammation and heart rate variability (HRV) may enable greater understanding of clinical observations as well as novel therapeutic targets.
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paper_title: Multi-resolution spatial simulation for molecular crowding

paper_content:

Spatial phenomena attract increasingly interest in computational biology. Molecular crowding, i.e. a dense population of macromolecules, is known to have a significant impact on the kinetics of molecules. However, an in-detail inspection of cell behavior in time and space is extremely costly. To balance between cost and accuracy, multi-resolution approaches offer one solution. Particularly, a combination of individual and lattice-population based algorithms promise an adequate treatment of phenomena like macromolecular crowding. In realizing such an approach, central questions are how to specify and synchronize the interaction between population and individual spatial level, and to decide what is best treated at a specific level, respectively. Based on an algorithm which combines the next subvolume method and a simple, individual-based spatial approach, we will present possible answers to these questions, and will discuss first experimental results.
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paper_title: Feedback loops and types of adaptation in the modelling of land-use decisions in an agent-based simulation

paper_content:

A key challenge of land-use modelling for supporting sustainable land management is to understand how environmental feedback that emerges from land-use actions can reshape land-use decisions in the long term. To investigate this issue, we apply the Human-Environment System framework formulated by Scholz (2011) as a conceptual guide to read typical feedback loops in land-use systems. We use an agent-based land-use change model (LUDAS) developed by Le et al. (2008, 2010) to test the sensitivity of long-term land-use dynamics to the inclusion of secondary feedback loop learning with respect to different system performance indicators at different levels of aggregation. Simulation experiments were based on a case study that was carried out in the Hong Ha watershed (Vietnam). We specified two model versions that represent two mechanisms of human adaptation in land-use decisions to environmental changes that emerged from land-use actions. The first mechanism includes only primary feedback loop learning, i.e. households adapt to the annual change in socio-ecological conditions and direct environmental response to land-use activities. The second mechanism includes the first one and secondary feedback loop learning, in which households can change their behavioural model in response to changes in socio-ecological conditions at the landscape-community level in the longer term. Spatial-temporal patterns of land-use and interrelated community income changes driven from the two feedback mechanisms are compared in order to evaluate the added value of the inclusion of secondary feedback loop learning. The results demonstrate that the effect of the added secondary feedback loop learning on land-use dynamics depends on domain type, time scale, and aggregation level of the impact indicators.
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paper_title: Multiscale Agent-based Model of Tumor Angiogenesis

paper_content:

Computational models of cancer complement the biological study of tumor growth. However, existing modeling approaches can be both inefficient and inaccurate due to the difficulties of representing the complex interactions between cells and tissues. We present a three-dimensional multiscale agent-based model of tumor growth with angiogenesis. The model is designed to easily adapt to various cancer types, although we focus on breast cancer. It includes cellular (genetic control), tissue (cells, blood vessels, angiogenesis), and molecular (VEGF, diffusion) levels of representation. Unlike in most cancer models, both normally functioning tissue cells and tumor cells are included in the model. Tumors grow following the expected spheroid cluster pattern, with growth limited by available oxygen. Angiogenesis, the process by which tumors may encourage new vessel growth for nutrient diffusion, is modeled with a new discrete approach that we propose will decrease computational cost. Our results show that despite proposing these new abstractions, we see similar results to previously accepted angiogenesis models. This may indicate that a more discrete approach should be considered by modelers in the future.
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paper_title: A hybrid multi-compartment model of granuloma formation and T cell priming in Tuberculosis

paper_content:

Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared. We have developed several models that spatially characterize the dynamics of the host-mycobacterial interaction, and identified mechanisms that control granuloma formation and development. In particular, we published several agent-based models (ABMs) of granuloma formation in TB that include many subtypes of T cell populations, macrophages as well as key cytokine and chemokine effector molecules. These ABM studies emphasize the important role of T-cell related mechanisms in infection progression, such as magnitude and timing of T cell recruitment, and macrophage activation. In these models, the priming and recruitment of T cells from the lung draining lymph node (LN) was captured phenomenologically. In addition to these ABM studies, we have also developed several multi-organ models using ODEs to examine trafficking of cells between, for example, the lung and LN. While we can predict temporal dynamic behaviors, those models are not coupled to the spatial aspects of granuloma. To this end, we have developed a multi-organ model that is hybrid: an ABM for the lung compartment and a non-linear system of ODE representing the lymph node compartment. This hybrid multi-organ approach to study TB granuloma formation in the lung and immune priming in the LN allows us to dissect protective mechanisms that cannot be achieved using the single compartment or multi-compartment ODE system. The main finding of this work is that trafficking of important cells known as antigen presenting cells from the lung to the lymph node is a key control mechanism for protective immunity: the entire spectrum of infection outcomes can be regulated by key immune cell migration rates. Our hybrid multi-organ implementation suggests that effector CD4+ T cells can rescue the system from a persistent infection and lead to clearance once a granuloma is fully formed. This could be effective as an immunotherapy strategy for latently infected individuals.
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paper_title: Holonic Multilevel Simulation of Complex Systems Application to real-time pedestrians simulation in virtual urban environment

paper_content:

Abstract Simulation, which creates abstractions of the system is an appropriate approach for studying complex systems that are inaccessible through direct observation and measurement. The problem with simulation of great numbers of interacting entities is that it is difficult to create a reliable and tractable abstraction of the real system. Indeed, simulating large numbers of entities requires great computing resources. A solution to avoid this problem is to use macroscopic models. However, this type of model may be unavailable or not reliable for the problem at hand and it does not allow the observation of individual behaviours. In this paper, a multilevel simulation model is proposed to allow the use of both microscopic and macroscopic techniques. This model is based upon Holonic Multi-Agent Systems which offer a promising approach for developing applications in complex domains characterised by a hierarchical structure. The proposed approach provides a generic scheduling model for multilevel simulations: dynamically adapting the level of simulated behaviours while being as faithful as possible to the simulated model. It does not only manage the level of entities’ behaviour but also of behaviours classically assigned to the environmental part of a simulation. A set of physics-based indicators is also introduced to dynamically determine the most suitable level for each entity and to maintain the best trade-off between simulation accuracy and constraints (dependent on the model or the experimental context).
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paper_title: Artificial Societies: Multiagent Systems and the Micro-Macro Link in Sociological Theory

paper_content:

This article introduces a general sociological readership to multiagent systems (MAS), a new computer simulation technology that has increasingly been used to describe and explain sociological phen...
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paper_title: Simulating Emergence and Downward Causation in Small Groups

paper_content:

Emergence has been a central issue not only in computational models of social systems, but also throughout 20th century sociological theory. In this paper, I first define the key concepts of emergence, downward causation, and bi-directional causation, and I identify these processes in an example of improvised dialog. I then draw on emergentist trends in sociological theory to propose a computational model of collaborative emergence among small groups of improvising agents.
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paper_title: Multi-perspective modelling of complex phenomena

paper_content:

This conceptual paper discusses the limitations of a single-perspective hierarchical approach to modelling and proposes multi-perspective modelling as a way to overcome them. As it turns out, multi-perspective modelling is primarily a new methodology, using existing modelling techniques but extending the modelling hierarchy with a new epistemological level which integrates the different perspectives. The methodology will be presented in some detail, and its use will be demonstrated by analyzing an example taken from a socio-political context.
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paper_title: Families of models that cross levels of resolution: issues for design, calibration and management

paper_content:

This review paper summarizes an ARPA-sponsored project to study variable-resolution modeling (VRM) and the connection of models across levels of resolution. We describe work introducing basic concepts, highlighting a design approach called integrated hierarchical variable-resolution modeling (IHVR), exploring mathematically some long-standing issues of aggregation in ground-combat modeling, and experimenting with cross-organizational efforts to develop and compare models created with different techniques and perspectives. We also describe highlights of a conference held in May, 1992 and touch briefly on some subsequent work. Despite the substantial progress, we conclude that the VRM problem is central, difficult, and greatly under invested. It should be approached as a matter of military science rather than technology, although we discuss new software tools that can be quite valuable.
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paper_title: Interactive hybrid simulation of large-scale traffic

paper_content:

We present a novel, real-time algorithm for modeling large-scale, realistic traffic using a hybrid model of both continuum and agent-based methods for traffic simulation. We simulate individual vehicles in regions of interest using state-of-the-art agent-based models of driver behavior, and use a faster continuum model of traffic flow in the remainder of the road network. Our key contributions are efficient techniques for the dynamic coupling of discrete vehicle simulation with the aggregated behavior of continuum techniques for traffic simulation. We demonstrate the flexibility and scalability of our interactive visual simulation technique on extensive road networks using both real-world traffic data and synthetic scenarios. These techniques demonstrate the applicability of hybrid techniques to the efficient simulation of large-scale flows with complex dynamics.
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paper_title: A hybrid macro-micro pedestrians evacuation model to speed up simulation in road networks

paper_content:

The major methodologies of crowd simulation in a dynamic environments are either based on micro or macro models. These two types of models represent the trade-off between the level of detail and efficiency. The domain of pedestrian flow simulation on road networks is no exception and theories rely either on equation based model or agent based models. There is a growing interest in hybrid modeling that combines both of these types. This paper addresses the problem of combining both micro and macro models of pedestrians movement to speedup simulations. Hybrid model uses efficient macro modeling in part of the road networks that do not require a fine grained model and more detailed but less efficient micro modeling in critical locations. One key issue raised by such an approach and discussed is the consistency of the resulting hybrid model. Preliminary results presented in this article is a proof of concept that the use of hybrid model to simulate evacuation plan in road networks may be more efficient than the use of micro model alone.
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paper_title: Hybrid Equation-based and Agent-based Modeling of Crowd Evacuation on Road Network

paper_content:

The major methodologies of crowd simulation in dynamic environment are either based on micro and macro models. Each of the two types of model represent choices in the trade-off between level of details and efficiency. The domain of pedestrian flow simulation in road networks is no exception and theories rely either on equation based model (LWR) or agent based models. There is a growing interest for hybrid modeling that combines both models together. This paper addresses the problem of combining both micro and macro models of pedestrians to speedup identification of optimal evacuation plan. The goal is therefor to use efficient macro modeling in part of the road networks that do not require fine grained model and less efficient but more detailed micro modeling elsewhere. The key issue raised by such an approach is to demonstrate the consistency of the resulting hybrid model. Preliminary results presented in this article are a proof of concept of how important speed up may be obtained using hybrid model to simulate evacuation plan in road networks.
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paper_title: Dynamic level of detail for large scale agent-based urban simulations

paper_content:

Large scale agent-based simulations typically face a trade-off between the level of detail in the representation of each agent and the scalability seen as the number of agents that can be simulated with the computing resources available. In this paper, we aim at bypassing this trade-off by considering that the level of detail is itself a parameter that can be adapted automatically and dynamically during the simulation, taking into account elements such as user focus, or specific events. We introduce a framework for such a methodology, and detail its deployment within an existing simulator dedicated to the simulation of urban infrastructures. We evaluate the approach experimentally along two criteria: (1) the impact of our methodology on the resources (CPU use), and (2) an estimate of the dissimilarity between the two modes of simulation, i.e. with and without applying our methodology. Initial experiments show that a major gain in CPU time can be obtained for a very limited loss of consistency.
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paper_title: A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

paper_content:

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.
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paper_title: A flexible approach to multi-level agent-based simulation with the mesoscopic representation

paper_content:

Large-scale simulations often use multiple agent representations to permit the study of specific multi-agent phenomena, and to find a balance between run-time performance and level of detail of the simulation. Although these approaches are effective, they do not always offer the desired level of analysis, especially when this level is between the resolutions of the models available. In this paper, we aim at offering a finer resolution in exploring this trade-off by introducing an intermediate level between two given resolutions, which can apply to all agent models and allows a more progressive transition to offer the desired level of analysis. We introduce a framework for such a methodology and evaluate it through the extension of an existing approach, along two criteria: its impact on computational resources, and an estimate of the dissimilarity between a simulation using our methodology and one without. Initial experiments show that consistency is almost maintained while CPU gain varies from low to significant depending on the context.
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paper_title: Autopoiesis and Cognition: The Realization of the Living

paper_content:

Editorial Preface General Table Of Contents Foreword Introduction (by Professor Maturana) Biology Of Cognition Dedication Table of Contents I. Introduction II. The Problem III. Cognitive Function in General A. The Observer B. The Living System C. Evolution D. The Cognitive Process IV. Cognitive Function in Particular A. Nerve Cells B. Architecture C. Function D. Representation E. Description F. Thinking G. Natural Language H. Memory and Learning I. The Observer V. Problems in the Neurophysiology of Cognition VI. Conclusions VII. Post Scriptum Autopoiesis: The Organization Of The Living Preface (by Sir Stafford Beer) Introduction I. On Machines, living and Otherwise 1. Machines 2. Living Machines II. Dispensability of Teleonomy 1. Purposelessness 2. Individuality III. Embodiments of Autopoiesis 1. Descriptive and Causal Notions 2. Molecular Embodiments 3. Origin IV. Diversity of Autopoiesis 1. Subordination to the Condition of Unity 2. Plasticity of Ontogeny 3. Reproduction, a Complication of the Unity 4. Evolution, a Historical Network 5. Second and Third Order Autopoietic Systems V. Presence of Autopoiesis 1. Biological Implications 2. Epistemological Implications 3. Cognitive Implications Appendix: The Nervous System Glossary Bibliography Index Of Names
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paper_title: Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units

paper_content:

Multiscale agent-based modeling (MABM) has been widely used to simulate Glioblastoma Multiforme (GBM) and its progression. At the intracellular level, the MABM approach employs a system of ordinary differential equations to describe quantitatively specific intracellular molecular pathways that determine phenotypic switches among cells (e.g. from migration to proliferation and vice versa). At the intercellular level, MABM describes cell-cell interactions by a discrete module. At the tissue level, partial differential equations are employed to model the diffusion of chemoattractants, which are the input factors of the intracellular molecular pathway. Moreover, multiscale analysis makes it possible to explore the molecules that play important roles in determining the cellular phenotypic switches that in turn drive the whole GBM expansion. However, owing to limited computational resources, MABM is currently a theoretical biological model that uses relatively coarse grids to simulate a few cancer cells in a small slice of brain cancer tissue. In order to improve this theoretical model to simulate and predict actual GBM cancer progression in real time, a graphics processing unit (GPU)-based parallel computing algorithm was developed and combined with the multi-resolution design to speed up the MABM. The simulated results demonstrated that the GPU-based, multi-resolution and multiscale approach can accelerate the previous MABM around 30-fold with relatively fine grids in a large extracellular matrix. Therefore, the new model has great potential for simulating and predicting real-time GBM progression, if real experimental data are incorporated.
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paper_title: Multiscale agent-based cancer modeling

paper_content:

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.
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paper_title: Simulation modelling of ecological hierarchies in constructive dynamical systems

paper_content:

Organized complexity is a characteristic feature of ecological systems with heterogeneous components interacting at several spatio-temporal scales. The hierarchy theory is a powerful epistemological framework to describe such systems by decomposing them vertically into levels and horizontally into holons. It was at first developed in a temporal and functional perspective and then, in the context of landscape ecology, extended to a spatial and structural approach. So far, most ecological applications of this theory were restricted to observational purposes, using multi-scale analysis to describe hierarchies. In spite of an increasing attention to dynamics of hierarchically structured ecological systems, current simulation models are still very limited in their representation of self-organization in complex adaptive systems. An ontological conceptualization of the hierarchy theory is outlined, focusing on key concepts, such as levels of organization and the compound and component faces of the holons. Various existing formalisms are currently used in simulation modelling, such as system dynamics, discrete event and agent based paradigms. Their ability to express the hierarchical organization of dynamical ecological systems is discussed. It turns out that a multi-modelling approach linking all these formalisms and oriented toward the specification of a constructive dynamical system would be able to express the dynamical structure of the hierarchy (creation, destruction and change of holons) and the functional and structural links between levels of organization.
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paper_title: Scale transfer modeling: using emergent computation for coupling an ordinary differential equation system with a reactive agent model

paper_content:

This article deals with the coupling of analytical models with individual based models design with the reactive agents paradigm. Such a coupling of models of different natures is motivated by the need to find a way to model scale transfer in large complex systems, i.e. to model how low level of organization can be made to influence upper level and vice versa. This is a fundamental issue, and more particularly in ecological modeling where models are a real scientific tool of investigation. Individuals and populations are not described at the same scale of time and space but it is known that they act on each others. Based on this example, we model individuals in their environment and the population dynamics. While behavior is best modeled using an algorithmic framework (the reactive agent paradigm), population dynamics (because of the number of interacting entities) is best modeled using numerical models. We propose the use of the concept of emergent computation as a framework for coupling heterogeneous formalisms. In the same time, it is crucial to be aware of the consequences of the simplifications and of the choices that are made in the reactive agent model, such as the topology of space and various parameters. In this article, we discuss these issues and our approach on a case study drawn from marine ecology and we show that it is possible to find classical mathematical functional responses with a reactive agent system. Then, we propose a methodology to deal with the coupling of heterogeneous formalism useful in any kind of system modeling.
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paper_title: The Importance of Being Discrete - Life Always Wins on the Surface

paper_content:

Many systems in chemistry, biology, finance, and social sciences present emerging features that are not easy to guess from the elementary interactions of their microscopic individual components. In the past, the macroscopic behavior of such systems was modeled by assuming that the collective dynamics of microscopic components can be effectively described collectively by equations acting on spatially continuous density distributions. It turns out that, to the contrary, taking into account the actual individual/discrete character of the microscopic components of these systems is crucial for explaining their macroscopic behavior. In fact, we find that in conditions in which the continuum approach would predict the extinction of all of the population (respectively the vanishing of the invested capital or the concentration of a chemical substance, etc.), the microscopic granularity insures the emergence of macroscopic localized subpopulations with collective adaptive properties that allow their survival and development. In particular it is found that in two dimensions "life" (the localized proliferating phase) always prevails.
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paper_title: Agents and artefacts for multiple models co-evolution Building complex system simulation as a set of interacting models

paper_content:

Complex systems simulations generally involve the interaction of different scientific fields. Human economies, ecosystems or dynamic computer networks such as P2P are good examples. Since models and simulators already exist in those fields, designing the simulation as a society of interacting and co-evolving models appears attractive. Beyond the technical issues to make different simulators cooperate, the challenges are to make the co-evolution design and implementation easier for the scientist that rarely know intricate modelling and simulation tools, and to facilitate the collaboration of different experts. Agents and artefacts (A&A) paradigm simplifies the design and the implementation of a society of interacting and co-evolving models. That is, the addition, the removal or the interchange of models require less effort. Contrary to classical approaches, we have built a decentralized co-evolution architecture based upon A&A and a data-driven coordination model. In this article, beyond the architecture presentation, we focus on the benefit provided by A&A used for multiple models co-evolution.
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paper_title: Multi-perspective modelling of complex phenomena

paper_content:

This conceptual paper discusses the limitations of a single-perspective hierarchical approach to modelling and proposes multi-perspective modelling as a way to overcome them. As it turns out, multi-perspective modelling is primarily a new methodology, using existing modelling techniques but extending the modelling hierarchy with a new epistemological level which integrates the different perspectives. The methodology will be presented in some detail, and its use will be demonstrated by analyzing an example taken from a socio-political context.
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paper_title: The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations

paper_content:

Swarm is a multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of si8mulation is the swarm, a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling approaches whereby agents can be composed of swarms of other agents in nested structures. Swarm provides object oriented libraries of reusable components for building models and analyzing, displaying, and controlling experiments on those models. Swarm is currently available as a beta version in full, free source code form. It requires the GNU C Compiler, Unix, and X Windows. More information about Swarm can be obtained from our web pages, [ Swarm ].
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paper_title: Scale transfer modeling: using emergent computation for coupling an ordinary differential equation system with a reactive agent model

paper_content:

This article deals with the coupling of analytical models with individual based models design with the reactive agents paradigm. Such a coupling of models of different natures is motivated by the need to find a way to model scale transfer in large complex systems, i.e. to model how low level of organization can be made to influence upper level and vice versa. This is a fundamental issue, and more particularly in ecological modeling where models are a real scientific tool of investigation. Individuals and populations are not described at the same scale of time and space but it is known that they act on each others. Based on this example, we model individuals in their environment and the population dynamics. While behavior is best modeled using an algorithmic framework (the reactive agent paradigm), population dynamics (because of the number of interacting entities) is best modeled using numerical models. We propose the use of the concept of emergent computation as a framework for coupling heterogeneous formalisms. In the same time, it is crucial to be aware of the consequences of the simplifications and of the choices that are made in the reactive agent model, such as the topology of space and various parameters. In this article, we discuss these issues and our approach on a case study drawn from marine ecology and we show that it is possible to find classical mathematical functional responses with a reactive agent system. Then, we propose a methodology to deal with the coupling of heterogeneous formalism useful in any kind of system modeling.
---
paper_title: Multiscale MAS modelling to simulate the soil environment: Application to soil ecology

paper_content:

Abstract Soils are important components of ecosystem and their functioning is of great importance for human well-being. Describing, understanding, foreseeing, and controlling biological activities and functions in soil in the context of global change are major objectives for scientists. Modelling soil bioturbation by soil engineers is of great importance although it is faced with the difficulty to describe the soil physical environment. Creating a model of a soil leads to complexity problems: a soil is a multi-scale heterogeneous, three-dimensional and dynamic environment that cannot be modelled and simulated without defining a suitable and optimized representation and taking assumptions about the studied system. An approach based on fractal theory (often used in soil sciences) was chosen to model such a real complex environment; it was integrated into a multi-agent system (MAS). MAS allows to simulate situated agents (earthworms) in an virtual world. The originality of this present MAS is that it is based on a dynamic environment which builds itself, on demand, according to an abstract canvas tree and agent movements. The aim of this paper is to present this approach and its originality, and to describe the model and the simulator. A theoretical view of the approach is given and applied to a case study: the impact of earthworms on soil structure and organic matter dynamics.
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paper_title: Inferring equation-based models from agent-based models: a case study in competition dynamics

paper_content:

Two types of model, equation-based models (EBMs) and agent-based models (ABMs) are now widely used in modeling ecological complex systems and seem not to be reconciled. While ABMs can help in exploring and explaining the local causes of global phenomena, EBMs are useful for predicting their long-term evolution without having to explore them through simulated experiments. In this paper, we show that it is possible to use an ABM to infer an EBM. Base on the case study, a dynamics of two competing species, we illustrate our methodology through the presentation of two models: an ABM and an EBM. We also show that the two models give the same results on coexistence of the two competing species.
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paper_title: Multi-level agent-based modeling with the Influence Reaction principle

paper_content:

This paper deals with the specification and the implementation of multi-level agent-based models, using a formal model, IRM4MLS (an Influence Reaction Model for Multi-Level Simulation), based on the Influence Reaction principle. Proposed examples illustrate forms of top-down control in (multi-level) multi-agent based-simulations.
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Title: Multi-level agent-based modeling - A literature survey

Section 1: Agent-based modeling
Description 1: Introduce the computational modeling paradigm of Agent-based modeling (ABM) and discuss its limitations.

Section 2: Multi-level agent-based modeling 1.2.1 Definitions
Description 2: Define key concepts related to Multi-Level Agent-based Modeling (ML-ABM) and provide the necessary theoretical background.

Section 3: Types of problems solved using multi-level agent-based approaches
Description 3: Outline the main types of problems that ML-ABM is used to solve.

Section 4: Terminology issues
Description 4: Discuss the terminology variations in the literature regarding levels in ML-ABM.

Section 5: Bibliography
Description 5: Survey the existing literature on ML-ABM and mention other notable surveys.

Section 6: Theoretical issues
Description 6: Explore the main theoretical issues addressed in ML-ABM research, such as meta-models, simulation engines, and representation of emergent phenomena.

Section 7: Meta-models, simulation engines and platforms
Description 7: Present various meta-models, simulation engines, and platforms that support ML-ABM.

Section 8: Observation, detection and agentification of emergent phenomena
Description 8: Analyze the methods proposed to observe, detect, and reify emergent phenomena in ML-ABM.

Section 9: Representation of aggregated entities
Description 9: Examine approaches to develop generic representations for aggregated entities in ML-ABM.

Section 10: ML-ABM has been used in various fields such as
Description 10: List and describe different application domains where ML-ABM has been employed, such as biomedical research, flow modeling, biology, ecology, etc.

Section 11: Social simulation
Description 11: Dive into the use of ML-ABM in social simulation and the integration of different social theories.

Section 12: Flow modeling
Description 12: Discuss ML-ABM applications in flow modeling, such as traffic and crowd simulations.

Section 13: Biological and biomedical models
Description 13: Describe how ML-ABM is applied in biological research, including cell biology and cancer modeling.

Section 14: Ecology
Description 14: Explore the use of ML-ABM in ecological modeling, addressing hierarchical systems and heterogeneous environments.

Section 15: Discussion
Description 15: Discuss different forms of level integration and the use of ML-ABM to solve technical problems.

Section 16: Conclusion
Description 16: Summarize the main findings of the survey, highlight the importance of ML-ABM, and suggest future research directions.