scholarly journals Robust coordination in adversarial social networks: From human behavior to agent-based modeling

2021 ◽  
pp. 1-36
Author(s):  
Chen Hajaj ◽  
Zlatko Joveski ◽  
Sixie Yu ◽  
Yevgeniy Vorobeychik
Keyword(s):  
Human Behavior ◽  
Computational Models ◽  
Human Subjects ◽  
Agent Based Modeling ◽  
Data Driven ◽  
Limited Attention ◽  
Decentralized Coordination ◽  
Agent Based ◽  
Network Topologies ◽  
Do So

Abstract Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.

Agent-based models and hypothesis testing: an example of innovation and organizational networks

2012 ◽  
Vol 27 (2) ◽  
pp. 221-238 ◽  
Author(s):  
Allen Wilhite ◽  
Eric A. Fong
Keyword(s):  
Hypothesis Testing ◽  
Corporate Culture ◽  
Computational Models ◽  
Organizational Structures ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Virtual Experiments ◽  
Network Topologies ◽  
And Performance ◽  
Using Data

AbstractHypothesis testing is uncommon in agent-based modeling and there are many reasons why (see Fagiolo et al. (2007) for a review). This is one of those uncommon studies: a combination of the new and old. First, a traditional neoclassical model of decision making is broadened by introducing agents who interact in an organization. The resulting computational model is analyzed using virtual experiments to consider how different organizational structures (different network topologies) affect the evolutionary path of an organization's corporate culture. These computational experiments establish testable hypotheses concerning structure, culture, and performance, and those hypotheses are tested empirically using data from an international sample of firms. In addition to learning something about organizational structure and innovation, the paper demonstrates how computational models can be used to frame empirical investigations and facilitate the interpretation of results in a traditional fashion.

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.

Projecting Health Care Factors into Future Outcomes with Agent-Based Modeling

2011 ◽  
pp. 1431-1453
Author(s):  
Georgiy Bobashev ◽  
Andrei Borshchev
Keyword(s):  
Health Care ◽  
Human Behavior ◽  
Hiv Transmission ◽  
Agent Based Modeling ◽  
Cross Sectional ◽  
Agent Based ◽  
Representative Study ◽  
Structure Changes ◽  
Social Network Structure ◽  
Future Outcomes

Human behavior is dynamic; it changes and adapts. In this chapter, we describe modeling approaches that consider human behavior as it relates to health care. We present examples the demonstrate how accounting for the social network structure changes the dynamics of infectious disease, how social hierarchy affects the chances of getting HIV, how the use of low dead-space syringe reduces the risk of HIV transmission, and how emergency departments could function more efficiently when real-time activities are simulated. The examples we use build from simple to more complex models and illustrate how agent-based modeling opens new horizons for providing descriptions of complex phenomena that were not possible with traditional statistical or even system dynamics methods. Agent-based modeling can use behavioral data from a cross-sectional representative study and project the behavior into the future so that the risks can be studied in a dynamical/temporal sense, thus combining the advantages of representative cross-sectional and longitudinal studies for the price of increased uncertainty. The authors also discuss data needs and potential future applications for this method.

Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach

2020 ◽  
Vol 512 ◽  
pp. 161-174 ◽  
Author(s):  
Guoyin Jiang ◽  
Xiaodong Feng ◽  
Wenping Liu ◽  
Xingjun Liu
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Online Review ◽  
Agent Based ◽  
Modeling Approach

Dynamic-data-driven agent-based modeling for the prediction of evacuation behavior during hurricanes

2021 ◽  
Vol 106 ◽  
pp. 102193
Author(s):  
Seunghan Lee ◽  
Saurabh Jain ◽  
Keeli Ginsbach ◽  
Young-Jun Son
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Dynamic Data ◽  
Agent Based ◽  
Evacuation Behavior

MODELING ENDOGENOUS RULE CHANGES IN AN INSTITUTIONAL CONTEXT: THE ADICO SEQUENCE

2008 ◽  
Vol 11 (02) ◽  
pp. 199-215 ◽  
Author(s):  
ALEX SMAJGL ◽  
LUIS R. IZQUIERDO ◽  
MARCO HUIGEN
Keyword(s):  
Social Group ◽  
Social Dynamics ◽  
Institutional Context ◽  
Agent Based Modeling ◽  
Institutional Framework ◽  
Agent Based ◽  
The Social ◽  
Rule Changes ◽  
Do So

Agent-based modeling is being increasingly used to simulate socio-techno-ecosystems that involve social dynamics. Humans face constraints that they sometimes wish to challenge, and when they do so, they often trigger changes at the scale of the social group too. Including such adaptation dynamics explicitly in our models would allow simulation of the endogenous emergence of rule changes. This paper discusses such an approach in an institutional framework and develops a sequence that allows modeling of endogenous rule changes. Parts of this sequence are implemented in a NetLogo KISS model to provide some illustrative results.

Projecting Health Care Factors into Future Outcomes with Agent-Based Modeling

2010 ◽  
pp. 234-256
Author(s):  
Georgiy Bobashev ◽  
Andrei Borshchev
Keyword(s):  
Health Care ◽  
Human Behavior ◽  
Hiv Transmission ◽  
Agent Based Modeling ◽  
Cross Sectional ◽  
Agent Based ◽  
Representative Study ◽  
Structure Changes ◽  
Social Network Structure ◽  
Future Outcomes

Human behavior is dynamic; it changes and adapts. In this chapter, we describe modeling approaches that consider human behavior as it relates to health care. We present examples the demonstrate how accounting for the social network structure changes the dynamics of infectious disease, how social hierarchy affects the chances of getting HIV, how the use of low dead-space syringe reduces the risk of HIV transmission, and how emergency departments could function more efficiently when real-time activities are simulated. The examples we use build from simple to more complex models and illustrate how agent-based modeling opens new horizons for providing descriptions of complex phenomena that were not possible with traditional statistical or even system dynamics methods. Agent-based modeling can use behavioral data from a cross-sectional representative study and project the behavior into the future so that the risks can be studied in a dynamical/temporal sense, thus combining the advantages of representative cross-sectional and longitudinal studies for the price of increased uncertainty. The authors also discuss data needs and potential future applications for this method.

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