Agent-Based Modeling

2010 ◽  
pp. 28-57 ◽  
Author(s):  
Brian L. Heath ◽  
Raymond R. Hill
Keyword(s):  
Real Life ◽  
Agent Based Modeling ◽  
Model Verification ◽  
Verification And Validation ◽  
Future Research ◽  
Agent Based Models ◽  
Agent Based ◽  
Philosophical Foundations ◽  
The Social ◽  
Model Verification And Validation

Models and simulations have been widely used as a means to predict the performance of systems. Agentbased modeling and agent distillations have recently found tremendous success particularly in analyzing ground force employment and doctrine. They have also seen wide use in the social sciences modeling a plethora of real-life scenarios. The use of these models always raises the question of whether the model is correctly encoded (verified) and accurately or faithfully represents the system of interest (validated). The topic of agent-based model verification and validation has received increased interest. This chapter traces the historical roots of agent-based modeling. This review examines the modern influences of systems thinking, cybernetics as well as chaos and complexity on the growth of agent-based modeling. The chapter then examines the philosophical foundations of simulation verification and validation. Simulation verification and validation can be viewed from two quite different perspectives: the simulation philosopher and the simulation practitioner. Personnel from either camp are typically unaware of the other camp’s view of simulation verification and validation. This chapter examines both camps while also providing a survey of the literature and efforts pertaining to the verification and validation of agent-based models. The chapter closes with insights pertaining to agent-based modeling, the verification and validation of agent-based models, and potential directions for future research.

How the Spread of User-Generated Contents (UGC) Shapes International Tourism Distribution: Using Agent-Based Modeling to Inform Strategic UGC Marketing

2020 ◽  
pp. 004728752095163
Author(s):  
Ye Zhang ◽  
Jie Gao ◽  
Shu Cole ◽  
Peter Ricci
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Real Life ◽  
Cost Effective ◽  
Agent Based Modeling ◽  
Research Approach ◽  
Agent Based ◽  
The Social ◽  
Travel Decision ◽  
Media Channels

While user-generated contents (UGC) are recognized as increasingly important to destination marketing, many DMOs are uncertain how to strategically manage them to their best advantage, largely due to their lack of understanding of mechanisms underlying the UGC effects. By integrating multiple theories of travel decision-making and UGC distribution, this study develops and validates an agent-based model to inform DMOs of potential causal mechanisms of how individual tourists’ UGC behavioral features shape international arrival distribution via the social media channels of review sites (RSs) and social networking sites (SNSs). Simulated experiments with the model decompose and assess the complex UGC behavioral effects, which further suggest context-based favorable UGC distribution statuses for DMOs’ strategic UGC marketing. The model developed following a rigid procedure offers a promising UGC research approach toward the combination of restrictive causal conceptualization and real-life replicability. It also provides an adaptive prototype for cost-effective UGC effect assessments by DMOs.

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.

Exposing System and Model Disparity and Agreement Using Wavelets

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Andrew D. Atkinson ◽  
Raymond R. Hill ◽  
Joseph J. Pignatiello ◽  
G. Geoffrey Vining ◽  
Edward D. White ◽  
...  
Keyword(s):  
Development Process ◽  
Model Development ◽  
Model Verification ◽  
Verification And Validation ◽  
High Dimensional ◽  
Model Data ◽  
Accurate Representation ◽  
Model Verification And Validation ◽  
Model Development Process ◽  
Functional Output

Model verification and validation (V&V) remain a critical step in the simulation model development process. A model requires verification to ensure that it has been correctly transitioned from a conceptual form to a computerized form. A model also requires validation to substantiate the accurate representation of the system it is meant to simulate. Validation assessments are complex when the system and model both generate high-dimensional functional output. To handle this complexity, this paper reviews several wavelet-based approaches for assessing models of this type and introduces a new concept for highlighting the areas of contrast and congruity between system and model data. This concept identifies individual wavelet coefficients that correspond to the areas of discrepancy between the system and model.

scholarly journals Discussing the Use of Complexity Theory in Engineering Management: Implications for Sustainability

2020 ◽  
Vol 12 (24) ◽  
pp. 10629
Author(s):  
Gianpaolo Abatecola ◽  
Alberto Surace
Keyword(s):  
New Product Development ◽  
Complexity Theory ◽  
Conceptual Modeling ◽  
New Product ◽  
Engineering Management ◽  
Agent Based Modeling ◽  
Current Status ◽  
Future Research ◽  
Agent Based ◽  
Wide Range

What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In this conceptual article, we critically discuss the current status of complexity research in EM. In this regard, we use IEEE Transactions on Engineering Management, because it is currently considered the leading journal in EM, and is as a reliable, heuristic proxy. From this journal, we analyze 38 representative publications on the topic published since 2000, and extrapolated through a rigorous keyword-based article search. In particular, we show that: (1) the adoption of CT has been associated with a wide range of key themes in EM, such as new product development, supply chain, and project management. (2) The adoption of CT has been witnessed in an increasing amount of publications, with a focus on conceptual modeling based on fuzzy logics, stochastic, or agent-based modeling prevailing. (3) Many key features of CT seem to be quite clearly observable in our dataset, with modeling and optimizing decision making, under uncertainty, as the dominant theme. However, only a limited number of studies appear to formally adhere to CT, to explain the different EM issues investigated. Thus, we derive various implications for EM research (concerning the research in and practice on sustainability issues).

scholarly journals Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Xiangqing Jiao ◽  
Yuan Liao ◽  
Thai Nguyen
Keyword(s):  
Statistical Analysis ◽  
System Analysis ◽  
Research Work ◽  
Field Measurements ◽  
Model Verification ◽  
Verification And Validation ◽  
Load Model ◽  
Load Models ◽  
Utility System ◽  
Model Verification And Validation

AbstractAccurate load models are critical for power system analysis and operation. A large amount of research work has been done on load modeling. Most of the existing research focuses on developing load models, while little has been done on developing formal load model verification and validation (V&V) methodologies or procedures. Most of the existing load model validation is based on qualitative rather than quantitative analysis. In addition, not all aspects of model V&V problem have been addressed by the existing approaches. To complement the existing methods, this paper proposes a novel load model verification and validation framework that can systematically and more comprehensively examine load model’s effectiveness and accuracy. Statistical analysis, instead of visual check, quantifies the load model’s accuracy, and provides a confidence level of the developed load model for model users. The analysis results can also be used to calibrate load models. The proposed framework can be used as a guidance to systematically examine load models for utility engineers and researchers. The proposed method is demonstrated through analysis of field measurements collected from a utility system.

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