scholarly journals Data-driven agent-based modeling, with application to rooftop solar adoption

2016 ◽  
Vol 30 (6) ◽  
pp. 1023-1049 ◽  
Author(s):  
Haifeng Zhang ◽  
Yevgeniy Vorobeychik ◽  
Joshua Letchford ◽  
Kiran Lakkaraju
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Agent Based ◽  
Rooftop Solar

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

scholarly journals Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation

2020 ◽  
Author(s):  
Zhaoyang Zhang ◽  
Christopher R. Cotter ◽  
Zhe Lyu ◽  
Lawrence J. Shimkets ◽  
Oleg A. Igoshin
Keyword(s):  
Cell Tracking ◽  
Mutant Cell ◽  
Agent Based Modeling ◽  
Behavioral Changes ◽  
Data Driven ◽  
Cell Behavior ◽  
Fruiting Bodies ◽  
Wild Type ◽  
Agent Based ◽  
Cell Behaviors

AbstractSingle mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC, that cannot aggregate unless mixed with wild type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.

scholarly journals Mapping the Landscape of Behavioral Theories: Systematic Literature Review

2019 ◽  
Vol 35 (2) ◽  
pp. 161-179 ◽  
Author(s):  
Heeseo Rain Kwon ◽  
Elisabete A. Silva
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Agent Based Modeling ◽  
Data Driven ◽  
Agent Based ◽  
Search Results ◽  
Behavioral Theories

The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.

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.

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