An Agent-Based Model of Miscommunication in Complex System Engineering Organizations

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

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Remarks on the Behavior of an Agent-Based Model of Spatial Distribution of Species

Annals of Emerging Technologies in Computing ◽

10.33166/aetic.2021.02.004 ◽

2021 ◽

Vol 5 (2) ◽

pp. 37-49

Author(s):

João Bioco ◽

Paula Prata ◽

Fernando Cánovas ◽

Paulo Fazendeiro

Keyword(s):

Complex System ◽

Spatial Distribution ◽

Life Cycle ◽

Point Of View ◽

Emergent Behavior ◽

Model Parameters ◽

Simulation Approach ◽

Agent Based Model ◽

Agent Based ◽

Environment Variables

Agent-based models have gained considerable notoriety in ecological modeling as well as in several other fields yearning for the ability to capture the emergent behavior of a complex system in which individuals interact with each other and with their environment. These models are implemented by applying a bottom-up approach, where the entire behavior of the system emerges from the local interaction between their components (agents or individuals). Usually, these interactions between individuals and their enclosing environment are modeled by very simple local rules. From the conceptual point of view, another appealing characteristic of this simulation approach is that it is well aligned with the reality whenever the system is composed of a multitude of individuals (behavioral units) that can be flexibly combined and placed in the environment. Due to their inherent flexibility, and despite of their simplicity, it is necessary to pay attention to the adjustments in their parameters which may result in unforeseen changes on the overall behavior of these models. In this paper we study the behavior of an agent-based model of spatial distribution of species, by analyzing the effects of the model parameters and the implications of the environment variables (that compose the environment where the species lives) on the models’ output. The presented experiments show that the behavior of the model depends mainly on the conditions of the environment where the species live, and the main parameters presented in life cycle of the species.

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FraudSim

Artificial Crime Analysis Systems ◽

10.4018/978-1-59904-591-7.ch016 ◽

2011 ◽

pp. 319-337 ◽

Cited By ~ 5

Author(s):

Yushim Kim ◽

Ningchuan Xiao

Keyword(s):

Complex System ◽

Spatial Patterns ◽

White Collar Crime ◽

White Collar ◽

Agent Based Model ◽

Agent Based ◽

Public Managers ◽

Crime Literature ◽

Future Work

This chapter shows that white-collar crime can be approached using an agent-based model. Fraud in public delivery programs often involves several entities that are loosely interrelated and interact dynamically. However, the crime literature has not paid sufficient attention to these characteristics, thus providing limited utility for public managers. This chapter frames a public delivery program as a complex system. The patterns of fraud in such a system are simulated using an agent-based model called FraudSim. We demonstrate that FraudSim closely replicates the statistical and spatial patterns of fraud and provides a framework for future work in this area.

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