Community

Ian Dennis Miller Member since: Tuesday, February 16, 2016 Full Member

MA Social Psychology, BS Cognitive Science

PhD student at University of Toronto: memes, social networks, contagion, agent based modeling, synthetic populations

Gayanga Herath Member since: Wednesday, March 14, 2018 Full Member

Master's degree in Information Technology, Management & Organisational Change at Lancaster University, Bachelor of Engineering (BEng) (Hons) in Computer Networks And Security at Staffordshire University, PhD in Organizational Cognition at University of Southern Denmark (Present)

An ambitious and driven individual with knowledge and project experience in computer networks and security (BEng (Hons)), along with a masters degree at a top 10 UK university in the domain of IT, management and organizational change with a distinction, and is currently working as a Ph.D. Research fellow in Denmark.

Current Ph.D. Project - Work Improvisation, looking into more flexible and plastic management through cognition.

Organizational Cognition
Organizational behaviour
Organizational change
Gamification
Fit
Recruitment & Selection
Distribted Cognition

Dino Mujadzevic Member since: Wednesday, April 23, 2014

Ph.d., A. v. Humboldt postdoctoral researcher

Discourse and networks executing and supporting Turkish foreign policy under AK Party (since 2002) on example of Bosnia and Herzegovina; Spreading of ideas of contemporary “Turkish economic model” abroad

I Schubert Member since: Thursday, March 12, 2015

PhD student

My PHD project focuses on understanding factors influencing individual sustainable consumption behaviour and how these factors could promote a sustainability transition.

André Calero Valdez Member since: Wednesday, January 17, 2018

Dr. phil.

André Calero Valdez does research on Computational Communication Science investigating the influence of network structure and algorithms on communication flow using agent-based modeling.

Rory Sie Member since: Tuesday, February 11, 2014

dr., MSc.

Mainly interested in studying social networks of learners, teachers, and innovators. Uses Social Network Analysis, but also sentiment analysis, data mining, and recommender system techniques.

Zhanli Sun Member since: Thursday, January 27, 2011 Full Member Reviewer

PhD

Agent based modelling;
Land use/land cover change;
Payment for ecosystem services;
Bayesian Network;
System Dynamics

Shah Jamal Alam Member since: Wednesday, July 16, 2008 Full Member Reviewer

PhD in Social Simulation, Masters in Computer Science, BS in Computer Science

My current interests include: agent-based modeling, simulating social complexity, land use, dynamic networks, social and cultural anthropology, HIV transmission dynamics, socio-political conflicts and social movements

Jo Hill Member since: Friday, January 20, 2012 Full Member Reviewer

BSc First Class (hons) Animal Behaviour with Ecology and Conservation, Mres Biosystematics

I am a first year PhD student at the Jill Dando Institute for Security and Crime Science at University College London

Xiaotian Wang Member since: Friday, March 28, 2014

PHD of Engineering in Modeling and Simulation, Proficiency in Agent-based Modeling

Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.

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