Multi-level agent-based simulations: Four design patterns

Mathieu, Philippe; Morvan, Gildąs; Picault, Sébastien

doi:10.1016/j.simpat.2017.12.015

Cited by 26 publications

(17 citation statements)

References 26 publications

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“…Other AI methods can improve model flexibility and modularity. Autonomous software agents enable to represent various levels of abstraction and organisation [ 55 ], helping modellers go more easily back and forth within small and larger scales, and ensure that all relevant mechanisms are adequately formalised at proper scales (i.e., scale-dependency of determinants and drivers in hierarchical living systems). Combining knowledge representation (through a DSL) and such a multi-level agent-based simulation architecture (e.g., in EMULSION, Figure 2 , [ 56 ]) enables to encompass several types of models (e.g., compartmental, individual-based) and scales (e.g., individual, population, territory), and it tackles simultaneously the recurring needs for transparency, reliability and flexibility in modelling contagious diseases.…”

Section: Contribution Of Ai To Better Understand Animal Epidemiologicmentioning

confidence: 99%

Research perspectives on animal health in the era of artificial intelligence

Ezanno

Picault

Beaunée

et al. 2021

Vet Res

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Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

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Section: Contribution Of Ai To Better Understand Animal Epidemiologicmentioning

confidence: 99%

Research perspectives on animal health in the era of artificial intelligence

Ezanno

Picault

Beaunée

et al. 2021

Vet Res

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“…Often, there is emphasis on one or the other (experiments or modeling) with no experiment-and-modeling iterations. That is, experiments are emphasized and there are no iterations [9], or modeling is emphasized and there are no iterations [19][20][21].…”

Section: Background and Motivationmentioning

confidence: 99%

“…There are many frameworks for developing simulations. In [ 19 ] four design patterns systematize and simplify the modeling and the implementation of multi-level agent-based simulations. In [ 20 ] a framework for developing agent-based simulators as mobile apps and online tools is presented.…”

Section: Related Workmentioning

confidence: 99%

Data analysis and modeling pipelines for controlled networked social science experiments

Cedeno-Mieles

Ren

et al. 2020

PLoS ONE

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There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

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“…The first one is a DSL designed for the description of all components of an epidemiological model, to make them explicit in a human-readable form as a structured text file, so that scientists from different fields can better interact with modellers throughout the modelling process, and discuss, assess or revise model structure, assumptions, parameters at any moment without having to read or write any line of simulation code. The second one is the use of a generic simulation engine, whose core architecture relies upon a multi-level agent-based system [21]. This allows several scales (individuals, groups, populations, metapopulations) and modelling paradigms (compartment- or individual-based models) to be encompassed within a homogeneous software interface, as agents act as wrappers which can be dynamically combined regardless of what they have to compute and the scale at which they operate.…”

Section: Design and Implementationmentioning

confidence: 99%

“…In the last decade, multi-level agent-based systems emerged using agents to explicitly represent intermediary abstraction levels (groups, sub-populations, organizations…) with behaviours of their own, between individuals and the whole system [25–28]. Recent advances in this field [21] led to design patterns, i.e. systematic solutions for recurrent modelling issues.…”

Section: Design and Implementationmentioning

confidence: 99%

EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology

et al. 2019

Self Cite

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Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from within-host to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup.renater.fr/www/emulsion-public.

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Multi-level agent-based simulations: Four design patterns

Cited by 26 publications

References 26 publications

Research perspectives on animal health in the era of artificial intelligence

Research perspectives on animal health in the era of artificial intelligence

Data analysis and modeling pipelines for controlled networked social science experiments

EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology

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