scholarly journals Stability of subsystem solutions in agent-based models

2017 ◽  
Author(s):  
Matjaž Perc
Keyword(s):  
Phase Transitions ◽  
Complex Systems ◽  
System Size ◽  
Systems Science ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Solid State Physics ◽  
Agent Based ◽  
Correct Determination ◽  
Predictive Understanding

The fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behavior when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modeling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behavior of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: When can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterized by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.

Agent-Based Macroeconomic Modeling and Policy Analysis

2018 ◽  
Author(s):  
Herbert Dawid ◽  
Simon Gemkow ◽  
Philipp Harting ◽  
Sander van der Hoog ◽  
Michael Neugart
Keyword(s):  
Policy Analysis ◽  
Simulation Models ◽  
Proof Of Concept ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Modeling Approaches ◽  
Macroeconomic Modeling ◽  
Spatial Policy

This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.

scholarly journals Accurate and efficient discretizations for stochastic models providing near agent-based spatial resolution at low computational cost

2019 ◽  
Vol 16 (159) ◽  
pp. 20190421 ◽  
Author(s):  
Nabil T. Fadai ◽  
Ruth E. Baker ◽  
Matthew J. Simpson
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Computational Cost ◽  
Kolmogorov Equation ◽  
Single Cell Level ◽  
Agent Based Model ◽  
Computationally Efficient ◽  
Agent Based Models ◽  
Cell Level ◽  
Agent Based

Understanding how cells proliferate, migrate and die in various environments is essential in determining how organisms develop and repair themselves. Continuum mathematical models, such as the logistic equation and the Fisher–Kolmogorov equation, can describe the global characteristics observed in commonly used cell biology assays, such as proliferation and scratch assays. However, these continuum models do not account for single-cell-level mechanics observed in high-throughput experiments. Mathematical modelling frameworks that represent individual cells, often called agent-based models, can successfully describe key single-cell-level features of these assays but are computationally infeasible when dealing with large populations. In this work, we propose an agent-based model with crowding effects that is computationally efficient and matches the logistic and Fisher–Kolmogorov equations in parameter regimes relevant to proliferation and scratch assays, respectively. This stochastic agent-based model allows multiple agents to be contained within compartments on an underlying lattice, thereby reducing the computational storage compared to existing agent-based models that allow one agent per site only. We propose a systematic method to determine a suitable compartment size. Implementing this compartment-based model with this compartment size provides a balance between computational storage, local resolution of agent behaviour and agreement with classical continuum descriptions.

scholarly journals The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dale Larie ◽  
Gary An ◽  
R. Chase Cockrell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multi Layer Perceptron ◽  
State Of Health ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Clinical Sepsis ◽  
Agent Based ◽  
Artificial Neural

Introduction: Disease states are being characterized at finer and finer levels of resolution via biomarker or gene expression profiles, while at the same time. Machine learning (ML) is increasingly used to analyze and potentially classify or predict the behavior of biological systems based on such characterization. As ML applications are extremely data-intensive, given the relative sparsity of biomedical data sets ML training of artificial neural networks (ANNs) often require the use of synthetic training data. Agent-based models (ABMs) that incorporate known biological mechanisms and their associated stochastic properties are a potential means of generating synthetic data. Herein we present an example of ML used to train an artificial neural network (ANN) as a surrogate system used to predict the time evolution of an ABM focusing on the clinical condition of sepsis.Methods: The disease trajectories for clinical sepsis, in terms of temporal cytokine and phenotypic dynamics, can be interpreted as a random dynamical system. The Innate Immune Response Agent-based Model (IIRABM) is a well-established model that utilizes known cellular and molecular rules to simulate disease trajectories corresponding to clinical sepsis. We have utilized two distinct neural network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to take a time sequence of five measurements of eleven IIRABM simulated serum cytokine concentrations as input and to return both the future cytokine trajectories as well as an aggregate metric representing the patient’s state of health.Results: The ANNs predicted model trajectories with the expected amount of error, due to stochasticity in the simulation, and recognizing that the mapping from a specific cytokine profile to a state-of-health is not unique. The Multi-Layer Perceptron neural network, generated predictions with a more accurate forecasted trajectory cone.Discussion: This work serves as a proof-of-concept for the use of ANNs to predict disease progression in sepsis as represented by an ABM. The findings demonstrate that multicellular systems with intrinsic stochasticity can be approximated with an ANN, but that forecasting a specific trajectory of the system requires sequential updating of the system state to provide a rolling forecast horizon.

scholarly journals Novel participatory methods for co-building an agent-based model of physical activity with youth

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241108
Author(s):  
Leah Frerichs ◽  
Natalie Smith ◽  
Jill A. Kuhlberg ◽  
Gretchen Mason ◽  
Damie Jackson-Diop ◽  
...  
Keyword(s):  
Physical Activity ◽  
Basic Structure ◽  
Quantitative Model ◽  
Agent Based Model ◽  
Dynamics Modeling ◽  
Agent Based Models ◽  
Technical Aspects ◽  
System Science ◽  
Agent Based ◽  
Engagement Strategies

Public health scholarship has increasingly called for the use of system science approaches to understand complex problems, including the use of participatory engagement to inform the modeling process. Some system science traditions, specifically system dynamics modeling, have an established participatory practice tradition. Yet, there remains limited guidance on engagement strategies using other modeling approaches like agent-based models. Our objective is to describe how we engaged adolescent youth in co-building an agent-based model about physical activity. Specifically, we aim to describe how we communicated technical aspects of agent-based models, the participatory activities we developed, and the resulting visual diagrams that were produced. We implemented six sessions with nine adolescent participants. To make technical aspects more accessible, we used an analogy that linked core components of agent-based models to elements of storytelling. We also implemented novel, facilitated activities that engaged youth in the development, annotation, and review of graphs over time, geographical maps, and state charts. The process was well-received by the participants and helped inform the basic structure of an agent-based model. The resulting visual diagrams created space for deeper discussion among participants about patterns of daily activity, important places for physical activity, and interactions between social and built environments. This work lays a foundation to develop and refine engagement strategies, especially for translating qualitative insights into quantitative model specifications such as ‘decision rules’.

scholarly journals How new concepts become universal scientific approaches: insights from citation network analysis of agent-based complex systems science

2018 ◽  
Vol 285 (1874) ◽  
pp. 20172360 ◽  
Author(s):  
Christian E. Vincenot
Keyword(s):  
Network Analysis ◽  
Complex Systems ◽  
Broad Spectrum ◽  
Citation Network ◽  
System Level ◽  
Systems Science ◽  
Agent Based ◽  
Science Integration ◽  
Citation Network Analysis ◽  
Citation Metrics

Progress in understanding and managing complex systems comprised of decision-making agents, such as cells, organisms, ecosystems or societies, is—like many scientific endeavours—limited by disciplinary boundaries. These boundaries, however, are moving and can actively be made porous or even disappear. To study this process, I advanced an original bibliometric approach based on network analysis to track and understand the development of the model-based science of agent-based complex systems (ACS). I analysed research citations between the two communities devoted to ACS research, namely agent-based (ABM) and individual-based modelling (IBM). Both terms refer to the same approach, yet the former is preferred in engineering and social sciences, while the latter prevails in natural sciences. This situation provided a unique case study for grasping how a new concept evolves distinctly across scientific domains and how to foster convergence into a universal scientific approach. The present analysis based on novel hetero-citation metrics revealed the historical development of ABM and IBM, confirmed their past disjointedness, and detected their progressive merger. The separation between these synonymous disciplines had silently opposed the free flow of knowledge among ACS practitioners and thereby hindered the transfer of methodological advances and the emergence of general systems theories. A surprisingly small number of key publications sparked the ongoing fusion between ABM and IBM research. Beside reviews raising awareness of broad-spectrum issues, generic protocols for model formulation and boundary-transcending inference strategies were critical means of science integration. Accessible broad-spectrum software similarly contributed to this change. From the modelling viewpoint, the discovery of the unification of ABM and IBM demonstrates that a wide variety of systems substantiate the premise of ACS research that microscale behaviours of agents and system-level dynamics are inseparably bound.

scholarly journals Learning differential equation models from stochastic agent-based model simulations

2021 ◽  
Vol 18 (176) ◽  
Author(s):  
John T. Nardini ◽  
Ruth E. Baker ◽  
Matthew J. Simpson ◽  
Kevin B. Flores
Keyword(s):  
Differential Equation ◽  
Cell Biology ◽  
Coarse Grained ◽  
Disease Spread ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Model Simulations ◽  
Differential Equation Models ◽  
Model Dynamics

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth–death–migration model commonly used to explore cell biology experiments and a susceptible–infected–recovered model of infectious disease spread.

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