A Systematic Approach for Aligning ODE and Agent-Based Models: Application to Epidemiological Modeling

2008 ◽  
Author(s):  
Michael J. Leamy
Keyword(s):  
Autonomous Agents ◽  
Systematic Approach ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Ode Model ◽  
Epidemiological Modeling ◽  
Agent Based ◽  
Agent Behavior ◽  
Time Histories ◽  
Good Agreement

Recent researchers active in the field of agent-based modeling have called for the alignment, or ‘docking,’ of models which simulate the same system using different techniques. Addressing this need, the present article details a systematic approach for docking models described by (nonlinear) ordinary differential equations with analogous models employing autonomous agents — i.e., agent-based models (ABMs). In particular, the approach is demonstrated by example for an epidemiological SEIR (Susceptible, Exposed, Infectious, Recovered) ODE model with a newly-developed agent-based model. The ABM is designed such that the assumptions present in the ODE model are matched by the actions of the ABM agents and the model. In addition, less-than-transparent coefficients present in the ODE model are examined via difference equations and then mapped to appropriate agent behavior. The result is very good agreement in comparisons made between ODE and ABM model-generated time-histories — i.e., successful alignment. It is anticipated that the systematic alignment approach described herein should be useful for aligning ODE and ABM models in other fields of study — e.g., Lanchester ODE combat models vice ABM combat models.

Agent-Based Models

2018 ◽  
Author(s):  
Brenda Heaton ◽  
Abdulrahman El-Sayed ◽  
Sandro Galea
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Or Groups ◽  
Complex Adaptive ◽  
Adaptive Processes ◽  
Neighborhoods And Health

Agent-based modeling is a newer approach to the study of neighborhoods and health. In brief, an agent-based model is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities, such as organizations or groups) with a view to assessing their effects on the system as a whole. Neighborhood characteristics and resources evolve and adapt as the individuals living within them change and vice versa. In this way, neighborhoods reflect a complex adaptive system. In this chapter, we introduce agent-based models as a tool for modeling these interactive and adaptive processes that occur within a system, such as a neighborhood. The chapter provides a basic introduction to this method, drawing on examples from the neighborhoods and health literature.

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

The Simulation Presented in the ODD Protocol

2017 ◽  
pp. 52-67
Keyword(s):  
Model Simulation ◽  
Behavioral Outcomes ◽  
Behavioral Model ◽  
Agent Based Modeling ◽  
State Variables ◽  
Agent Based Models ◽  
Agent Based ◽  
Design Concepts ◽  
Modeling Simulation ◽  
Area Of Interest

The ODD Protocol has become a standard for documenting and describing agent based models. The protocol is organized around three main elements, from which the ODD acronym originates: Overview, Design concepts, and Details. This chapter is organized around these primary elements and further broken down into seven sub-elements to provide a clear purpose and understanding of the model simulation. The sub-elements are: Purpose, State Variables and Scales, Process Overview and Scheduling, Design Concepts, Initialization, Input, and Sub-models. The model presented is a proto-agent behavioral model and is utilized in an agent based modeling simulation to help identify possible emergent behavioral outcomes of the populations in the area of interest. By varying the rules governing the interactions of the multinational and incumbent government proto-agents, different strategies can be identified for increasing the effectiveness of those proto-agents and the utilization of resources.

From KISS to TASS Modeling: A Preliminary Analysis of the Segregation Model Incorporated with Spatial Data on Chicago

2015 ◽  
Vol 16 (4) ◽  
pp. 553-573 ◽  
Author(s):  
GAKU ITO ◽  
SUSUMU YAMAKAGE
Keyword(s):  
Residential Segregation ◽  
Spatial Data ◽  
Demographic Data ◽  
Theoretical Models ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
The Us ◽  
Us Census ◽  
The City

AbstractThe ‘keep it simple, stupid’ slogan, or the KISS principle has been the basic guideline in agent-based modeling (ABM). While the KISS principle or parsimony is vital in modeling attempts, conventional agent-based models remain abstract and are rarely incorporated or validated with empirical data, leaving the links between theoretical models and empirical phenomena rather loose. This article reexamines the KISS principle and discusses the recent modeling attempts that incorporate and validate agent-based models with spatial (geo-referenced) data, moving beyond the KISS principle. This article also provides a working example of such time and space specified (TASS) agent-based models that incorporates Schelling's (1971) classic model of residential segregation with detailed geo-referenced demographic data on the city of Chicago derived from the US Census 2010.

Agent-Based Modeling of Flood Insurance Futures

2020 ◽  
Author(s):  
Linda Geaves
Keyword(s):  
Autonomous Agents ◽  
Insurance Industry ◽  
Model Development ◽  
Policy Instruments ◽  
Flood Insurance ◽  
Agent Based Models ◽  
Online Data ◽  
Agent Based ◽  
Artificial Intelligence Technology ◽  
The Impact

Agent-based models have facilitated greater understanding of flood insurance futures, and will continue to advance this field as modeling technology develops further. As the pressures of climate-change increase and global populations grow, the insurance industry will be required to adapt to a less predictable operating environment. Complicating the future of flood insurance is the role flood insurance plays within a state, as well as how insurers impact the interests of other stakeholders, such as mortgage providers, property developers, and householders. As such, flood insurance is inextricably linked with the politics, economy, and social welfare of a state, and can be considered as part of a complex system of changing environments and diverse stakeholders. Agent-based models are capable of modeling complex systems, and, as such, have utility for flood insurance systems. These models can be considered as a platform in which the actions of autonomous agents, both individuals and collectives, are simulated. Cellular automata are the lowest level of an agent-based model and are discrete and abstract computational systems. These automata, which operate within a local and/or universal environment, can be programmed with characteristics of stakeholders and can act independently or interact collectively. Due to this, agent-based models can capture the complexities of a multi-stakeholder environment displaying diversity of behavior and, concurrently, can cater for the changing flood environment. Agent-based models of flood insurance futures have primarily been developed for predictive purposes, such as understanding the impact of introductions of policy instruments. However, the ways in which these situations have been approached by researchers have varied; some have focused on recreating consumer behavior and psychology, while others have sought to recreate agent interactions within a flood environment. The opportunities for agent-based models are likely to become more pronounced as online data becomes more readily available and artificial intelligence technology supports model development.

COMPUTATIONAL FEATURES OF AGENT-BASED MODELS

2005 ◽  
Vol 02 (01) ◽  
pp. 33-48 ◽  
Author(s):  
MASSIMO BERNASCHI ◽  
FILIPPO CASTIGLIONE
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Agent Based Modeling ◽  
System Response ◽  
Agent Based Models ◽  
Design Data ◽  
Agent Based ◽  
Data Structures And Algorithms ◽  
Immune System Response ◽  
Large Scale Simulations

Agent-based modeling allows the description of very complex systems. To run large scale simulations of agent-based models in a reasonable time, it is crucial to carefully design data structures and algorithms. We describe the main computational features of agent-based models and report about the solutions we adopted in two applications: The simulation of the immune system response and the simulation of the stock market dynamics.

Using SysML for Conceptual Representation of Agent-Based Models

2011 ◽  
Author(s):  
Zhenghui Sha ◽  
Qize Le ◽  
Jitesh H. Panchal
Keyword(s):  
Conceptual Models ◽  
Agent Based Modeling ◽  
Modeling Language ◽  
Automatic Extraction ◽  
Systems Modeling ◽  
Conceptual Representation ◽  
Agent Based Models ◽  
Agent Based ◽  
Collaborative Processes ◽  
Limited Support

Agent-based modeling (ABM) is a technique used to simulate systems consisting of autonomous interacting entities called agents. It has shown great advantages in modeling complex systems with independent but interacting actors. ABM has been successfully applied to a variety of systems. Despite the availability of a large number of tools for ABM, there is limited support for the conceptual design of agent-based models. Further, the currently available tools capture both the model information and the tool-specific execution information in an integrated manner. This limits model reusability, which is an impediment to systematic validation of models. In this paper, we use the systems modeling language (SysML) for building conceptual models of agent-based models. We discuss how the different diagrams in the SysML language can be used to represent different aspects of agent-based models. Further, we propose an approach for automatically generating executable agent-based models from their conceptual SysML representations. The proposed approach is illustrated using a model of mass-collaborative processes as an example. The proposed approach for conceptual representation of agent-based models in SysML and automatic extraction of executable models has the potential to greatly improve reuse, reconfiguration, and validation of agent-based models.

Book Reviews

2017 ◽  
Vol 55 (2) ◽  
pp. 644-647
Keyword(s):  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Practical Guide ◽  
Interacting Agents ◽  
Hamilton College ◽  
Heterogeneous Interacting Agents

Christophre Georges of the Department of Economics, Hamilton College reviews “Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling,” edited by Alessandro Caiani, Alberto Russo, Antonio Palestrini, and Mauro Gallegati. The Econlit abstract for this book begins: “Text for graduate and PhD students, as well as undergraduates with some knowledge of computers and economics comprises four papers emerging from a workshop on agent-based modeling held by the Dipartimento di Scienze Economiche e Sociali at the Università Politecnica delle Marche. Presents a guide to agent-based models (ABM) and the technicalities that need to be solved in order to evaluate the effect of different rules and their switching.”

Agent-Based Modeling and Simulation on Multi-Stage Supply Chain Operation

2011 ◽  
Vol 291-294 ◽  
pp. 3216-3220 ◽  
Author(s):  
Can Can Zhao ◽  
Xiao Dong Zhang ◽  
Shao Juan Lei ◽  
Jun Jiang Qiu
Keyword(s):  
Supply Chain ◽  
Modeling And Simulation ◽  
Cooperative Behavior ◽  
Agent Based Modeling ◽  
Individual Agent ◽  
Agent Based ◽  
Agent Behavior ◽  
Chain Operation ◽  
Multi Stage ◽  
Supply Chain Operation

Supply chain simulation is a fundamental approach for supply chain prediction, management, evaluation, and improvement. In order to simulate member behavior, organizational strategy, and management strategy of the supply chain, an agent-based modeling and simulation approach on multi-stage supply chain operation was proposed in this paper. First, the model structure of multi-stage supply chain operation was introduced. Then, the individual agent behavior model was emphatically studied, and hierarchical colored Petri-nets were used to describe the agent behavior process and cooperative behavior process. Finally, the model was validated using a series of simulation which focused on the agent inventory strategy.

scholarly journals Improving agent-based models of diffusion

2016 ◽  
Vol 50 (3/4) ◽  
pp. 639-646 ◽  
Author(s):  
Robert East ◽  
Mark D. Uncles ◽  
Jenni Romaniuk ◽  
Wendy Lomax
Keyword(s):  
Design Methodology ◽  
Agent Based Modeling ◽  
Diffusion Modeling ◽  
Agent Based Models ◽  
Content Type ◽  
Agent Based ◽  
Made In

Purpose This paper aims to review the validation of assumptions made in agent-based modeling of diffusion and the sufficiency (completeness) of the mechanisms assumed to operate. Design/methodology/approach One well-cited paper is examined. Findings Evidence is presented that casts doubt on the assumptions and mechanisms used. A range of mechanisms is suggested that should be evaluated for inclusion in diffusion modeling. Originality/value The need for validation of assumptions has been stressed elsewhere but there has been a lack of examples. This paper provides examples. The stress on the sufficiency of the mechanisms used is new.

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