A Conceptual Framework for Agent-Based Modeling of Human Behavior in Spatial Design

2020 ◽  
pp. 187-198
Author(s):  
Dario Esposito ◽  
Ilenia Abbattista ◽  
Domenico Camarda
Keyword(s):  
Conceptual Framework ◽  
Human Behavior ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Spatial Design

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.

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.

scholarly journals Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 519
Author(s):  
Nicholas R. Magliocca
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Significant Proportion ◽  
Agent Based Modeling ◽  
Future Research ◽  
Learning Technology ◽  
Coupled Modeling ◽  
Agent Based ◽  
Food Energy ◽  
Data Limitations

The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain.

A Unified Framework for Traditional and Agent-Based Social Network Modeling

2014 ◽  
pp. 184-199 ◽  
Author(s):  
Enrico Franchi ◽  
Michele Tomaiuolo
Keyword(s):  
Social Network ◽  
Conceptual Framework ◽  
Network Models ◽  
General Purpose ◽  
Agent Based Modeling ◽  
Unified Framework ◽  
Modeling Tool ◽  
Agent Based ◽  
Meta Model ◽  
Social Network Modeling

In the last sixty years of research, several models have been proposed to explain (i) the formation and (ii) the evolution of networks. However, because of the specialization required for the problems, most of the agent-based models are not general. On the other hand, many of the traditional network models focus on elementary interactions that are often part of several different processes. This phenomenon is especially evident in the field of models for social networks. Therefore, this chapter presents a unified conceptual framework to express both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model that acts as a template for other models. To support this meta-model, the chapter proposes a different kind of agent-based modeling tool that we specifically created for developing social network models. The tool the authors propose does not aim at being a general-purpose agent-based modeling tool, thus remaining a relatively simple software system, while it is extensible where it really matters. Eventually, the authors apply this toolkit to a novel problem coming from the domain of P2P social networking platforms.

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.

Sign in / Sign up

Export Citation Format

Share Document