scholarly journals Agent-based Modeling in Tobacco Regulatory Science: Exploring 'What if' in Waterpipe Smoking

2020 ◽  
Vol 6 (3) ◽  
pp. 171-178
Author(s):  
Yong Yang ◽  
Kenneth D. Ward ◽  
Ramzi G. Salloum ◽  
Eric N. Lindblom
Keyword(s):  
Social Networks ◽  
Social Activity ◽  
Agent Based Modeling ◽  
Dynamic Interactions ◽  
Tobacco Products ◽  
Time Activity ◽  
Health Crisis ◽  
Agent Based ◽  
Related Research ◽  
Individual Level

Objectives: Waterpipe tobacco smoking (WTS) is an emerging public health crisis, particularly among youth and young adults. Different from the use of other tobacco products and e-cigarettes, WTS tends to be a social activity occurring among friends or persons associated with social networks. In this paper, we review a potential strategy for WTS-related research. Methods: As a bottom-up computational model, agent-based modeling (ABM) can simulate the actions and interactions of agents, as well as the dynamic interactions between agents and their environments, to gain an understanding of the functioning of a system. ABM is particularly useful for incorporating the influence of social networks in WTS, and capturing people's space-time activity and the spatial distribution of WTS venues. Results: Comprehensive knowledge of WTS-related behaviors at the individual level is needed to take advantage of ABM and use it to examine policies such as the interaction between WTS and cigarette smoking and the effect of flavors used in waterpipe tobacco. Longitudinal and WTS-specific surveys and laboratory experiments are particularly helpful to understand WTS basic mechanisms and elicit individual preferences, respectively. Conclusions: We argue that the uniqueness of WTS makes ABM a promising tool to be used in WTS-related research, as well as understanding use of other tobacco products.

scholarly journals Toward a better understanding of team decision processes: combining laboratory experiments with agent-based modeling

2021 ◽  
Author(s):  
Iris Lorscheid ◽  
Matthias Meyer
Keyword(s):  
Laboratory Experiment ◽  
Mixed Method ◽  
Agent Based Modeling ◽  
Decision Processes ◽  
Mixed Method Research ◽  
Team Cognition ◽  
Agent Based Model ◽  
Agent Based ◽  
Individual Level ◽  
Team Decision

AbstractDespite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.

Agent-Based Modeling for Simulation of Complex Business Systems

2011 ◽  
pp. 1852-1861
Author(s):  
Christopher Langdon
Keyword(s):  
Computer Science ◽  
Instrument Validation ◽  
Research Strategy ◽  
Agent Based Modeling ◽  
Scientific Study ◽  
Research Approach ◽  
Business Systems ◽  
Agent Based ◽  
Related Research ◽  
Advance Knowledge

This article aims to provide a synopsis of agent-based modeling and how to adapt an agent-based research strategy for the scientific study of complex business systems. Agent-based systems have been a popular field of study in computer science for some time. While computer science-related research has been focused on the artifact itself, such as computational languages and algorithms, research in the management sciences is explicitly focused on business problems. Research in Information Systems (IS) has begun to advance knowledge in the use of agent-based systems as a means to seek different, computational explanations for business phenomena that have eluded scientific inquiry reliant on traditional—specifically, law and axiomatic—explanation (Kimbrough, 2003). The focus on business problems requires a different research approach than what is successful in computer science. Key modifications include first, the explicit articulation of benefits specific to the management sciences, and second, instrument validation.

Identifying Influencers in Online Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Yifeng Zhang ◽  
Xiaoqing Li ◽  
Te-Wei Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Word Of Mouth ◽  
Agent Based Modeling ◽  
Viral Marketing ◽  
Tie Strength ◽  
Agent Based ◽  
Widespread Acceptance ◽  
Tremendous Amount ◽  
Marketing Messages

Online social networks (OSNs) are quickly becoming a key component of the Internet. With their widespread acceptance among the general public and the tremendous amount time that users spend on them, OSNs provide great potentials for marketing, especially viral marketing, in which marketing messages are spread among consumers via the word-of-mouth process. A critical task in viral marketing is influencer identification, i.e. finding a group of consumers as the initial receivers of a marketing message. Using agent-based modeling, this paper examines the effectiveness of tie strength as a criterion for influencer identification on OSNs. Results show that identifying influencers by the number of strong connections that a user has is superior to doing so by the total number of connections when the strength of strong connections is relatively high compared to that of weak connections or there is a relatively high percentage of strong connections between users. Implications of the results are discussed.

A framework of multilayer social networks for communication behavior with agent-based modeling

SIMULATION ◽  
2013 ◽  
Vol 89 (7) ◽  
pp. 810-828 ◽  
Author(s):  
Yuanzheng Ge ◽  
Liang Liu ◽  
Xiaogang Qiu ◽  
Hongbin Song ◽  
Yong Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Agent Based Modeling ◽  
Communication Behavior ◽  
Agent Based ◽  
Multilayer Social Networks

scholarly journals EQUATION-FREE MULTISCALE COMPUTATIONS IN SOCIAL NETWORKS: FROM AGENT-BASED MODELING TO COARSE-GRAINED STABILITY AND BIFURCATION ANALYSIS

2010 ◽  
Vol 20 (11) ◽  
pp. 3673-3688 ◽  
Author(s):  
A. C. TSOUMANIS ◽  
C. I. SIETTOS ◽  
G. V. BAFAS ◽  
I. G. KEVREKIDIS
Keyword(s):  
Social Networks ◽  
Public Information ◽  
Stationary Solutions ◽  
Agent Based Modeling ◽  
Coarse Grained ◽  
Computational Framework ◽  
Agent Based ◽  
Private And Public ◽  
Multiscale Computations ◽  
Dynamical Information

We focus on the "trijunction" between multiscale computations, bifurcation theory and social networks. In particular, we address how the Equation-Free approach, a recently developed computational framework, can be exploited to systematically extract coarse-grained, emergent dynamical information by bridging detailed, agent-based models of social interactions on networks, with macroscopic, systems-level, continuum numerical analysis tools. For our illustrations, we use a simple dynamic agent-based model describing the propagation of information between individuals interacting under mimesis in a social network with private and public information. We describe the rules governing the evolution of the agents' emotional state dynamics and discover, through simulation, multiple stable stationary states as a function of the network topology. Using the Equation-Free approach we track the dependence of these stationary solutions on network parameters and quantify their stability in the form of coarse-grained bifurcation diagrams.

Social capital in retirement villages: a literature review

2020 ◽  
pp. 1-29
Author(s):  
Nicole Schwitter
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Social Activity ◽  
Future Research ◽  
System Control ◽  
Individual Level ◽  
Care Services ◽  
Helping Behaviour ◽  
Different Dimensions ◽  
Extra Care Housing

Abstract Retirement villages are a model of extra-care housing, offering purpose-designed housing that incorporates both care services and a range of non-care-related facilities and activities. These generate opportunities for formal and informal social activity, and promote community engagement, solidarity between residents, and active and independent ageing. Providers suggest that retirement villages are able to foster an environment rich in social capital. This study's purpose is to review and summarise key findings on the topic of social capital in retirement villages in the gerontological literature. Social capital is defined as both an individual attribute of single actors and a feature of communities as a whole. A clear conceptualisation of social capital is used to organise the reviewed studies along different dimensions: on an individual level, social networks, trustworthiness and obligations are differentiated, while the collective level distinguishes between system control, system trust and system morality. Thirty-four studies are reviewed. While retirement villages are generally described as friendly places with widespread helping behaviour where new friends are made, research has also highlighted the difficulty of socially integrating the frail and very old. While, in particular, social networks and system morality have received much attention, there is a clear need for future research into the other domains of social capital.

scholarly journals Shared behavioral mechanisms underlie C. elegans aggregation and swarming

2018 ◽  
Author(s):  
S Serena Ding ◽  
Linus J. Schumacher ◽  
Avelino E. Javer ◽  
Robert G. Endres ◽  
André EX Brown
Keyword(s):  
A Priori ◽  
Population Level ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Individual Level ◽  
C Elegans ◽  
Behavioral Rules ◽  
Level Statistics ◽  
Approximate Bayesian ◽  
Behavioral Mechanisms

AbstractIn complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While such collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescent multi-worm tracking, we quantify aggregation behavior in terms of individual dynamics and population-level statistics. Based on our quantification, we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules that give rise to aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. Hence, mesoscopic C. elegans uses mechanisms familiar from microscopic systems for aggregation, but implemented via more complex behaviors characteristic of macroscopic organisms.

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