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.

scholarly journals System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation in Construction Waste Management

2018 ◽  
Vol 10 (7) ◽  
pp. 2484 ◽  
Author(s):  
Zhikun Ding ◽  
Wenyan Gong ◽  
Shenghan Li ◽  
Zezhou Wu
Keyword(s):  
Waste Management ◽  
System Dynamics ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Effective Management ◽  
Construction Waste ◽  
Agent Based ◽  
Complex Adaptive ◽  
Research Findings ◽  
Novel Model

The environmental impacts caused by construction waste have attracted increasing attention in recent years. The effective management of construction waste is essential in order to reduce negative environmental influences. Construction waste management (CWM) can be viewed as a complex adaptive system, as it involves not only various factors (e.g., social, economic, and environmental), but also different stakeholders (such as developers, contractors, designers, and governmental departments) simultaneously. System dynamics (SD) and agent-based modeling (ABM) are the two most popular approaches to deal with the complexity in CWM systems. However, the two approaches have their own advantages and drawbacks. The aim of this research is to conduct a comprehensive review and develop a novel model for combining the advantages of both SD and ABM. The research findings revealed that two options can be considered when combining SD with ABM; the two options are discussed.

scholarly journals Organizational information security as a complex adaptive system: insights from three agent-based models

2015 ◽  
Vol 19 (3) ◽  
pp. 509-524 ◽  
Author(s):  
A. J. Burns ◽  
Clay Posey ◽  
James F. Courtney ◽  
Tom L. Roberts ◽  
Prabhashi Nanayakkara
Keyword(s):  
Information Security ◽  
Adaptive System ◽  
Complex Adaptive System ◽  
Agent Based Models ◽  
Agent Based ◽  
Organizational Information ◽  
Complex Adaptive

Agent‐Based Modeling

2009 ◽  
Author(s):  
Scott de Marchi ◽  
Scott E. Page
Keyword(s):  
Mathematical Models ◽  
Computational Methods ◽  
Computational Models ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Interactions ◽  
Game Theoretic ◽  
Mathematical Analyses ◽  
The Stability

This article provides a discussion on agent-based modeling. Two examples that show the ability of computational methods to extend game-theoretic results are presented. It then discusses modeling agents, modeling agent interactions, and system behaviour. In addition, it describes how agent-based models differ from and complement mathematical models and concludes with some suggestions for how one might best leverage the strengths of agent-based models to advance political science. Most mathematical analyses of game-theoretic models do not look into the stability and attainability of their equilibria and would be made richer by complementing them with agent-based models that explored those properties. The ability of computational models to test the robustness of formal results would be reason alone to add them to tool kits. As a methodology, agent-based modeling should be considered as in its infancy, its enormous potential limited only by the scientific and creative talents of its practitioners.

scholarly journals A Novel Formal Agent-Based Simulation Modeling Framework of an AIDS Complex Adaptive System

2013 ◽  
Vol 5 (3) ◽  
pp. 33-53 ◽  
Author(s):  
Amnah Siddiqa ◽  
Muaz Niazi
Keyword(s):  
Complex Adaptive Systems ◽  
Formal Specification ◽  
Simulation Modeling ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Modeling Framework ◽  
Agent Based ◽  
Complex Adaptive

HIV/AIDS spread depends upon complex patterns of interaction among various subsets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS). While agent-based models are well-known to effectively model CAS, often times models can tend to be ambiguous and using only using text-based specifications (such as ODD) making models difficult to be replicated. Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS. However, to the best of the authors’ knowledge, no such model has been developed in conjunction with AIDS. In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition. The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.

Agent-based models of bounded rationality

2017 ◽  
Vol 23 (1/2) ◽  
pp. 2-12 ◽  
Author(s):  
Davide Secchi
Keyword(s):  
Bounded Rationality ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Special Issue ◽  
Agent Based Models ◽  
Content Type ◽  
Agent Based ◽  
Team Research ◽  
Complex Adaptive ◽  
To Come

Purpose The purpose of this editorial is to introduce the Special Issue “Agent-Based Models of Bounded Rationality” and to provide an overview of its rationale and main objectives. Design/methodology/approach After outlining the overall framework to justify the choice of agent-based modeling in relation to bounded rationality, an overview of the six papers published in the Special Issue is presented. Findings The paper argues that simulation of complex adaptive social systems is a way to set the ground for updating the concept of bounded rationality and prepare for it to still play a significant role in the years to come. Originality/value After its introduction, bounded rationality remained mostly used but seldom discussed in both its assumptions and its meaning. The originality of this introduction is to unveil some of the points that keep rationality still at the core of organization and team research.

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 Computational Economics

2007 ◽  
pp. 183-197
Author(s):  
C. Bruun
Keyword(s):  
Economic System ◽  
Autonomous Agents ◽  
Complex Adaptive System ◽  
Methodological Approach ◽  
Computational Economics ◽  
Economic Methodology ◽  
Economic Systems ◽  
New Approach ◽  
Agent Based ◽  
Complex Adaptive

This chapter argues that the economic system is best perceived as a complex adaptive system, and as such, the traditional analytical methods of economics are not optimal for its study. Agent-based computational economics (ACE) studies the economic system from the bottom up and recognizes interaction between autonomous agents as the central mechanism in generating the self-organizing features of economic systems. Besides a discussion of this new economic methodology, a short how-to introduction is given, and the problem of constraining economics as a science within the ACE approach is raised. It is argued that ACE should be perceived as a new methodological approach to the study of economic systems rather than a new approach to economics, and that the use of ACE should be anchored in existing economic theory.

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.

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