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.
Klaus G. Troitzsch was a full professor of computer applications in the social sciences at the University of Koblenz-Landau since 1986 until he officially retired in 2012 (but continues his academic activities). He took his first degree as a political scientist. After eight years in active politics in Hamburg and after having taken his PhD, he returned to academia, first as a senior researcher in an election research project at the University of Koblenz-Landau, from 1986 as full professor of computer applications in the social sciences. His main interests in teaching and research are social science methodology and, especially, modelling and simulation in the social sciences.
Among his early research projects there is the MIMOSE project which developed a declarative functional simulation language and tool for micro and multilevel simulation between 1986 and 1992. Several EU funded projects were devoted to social simulation and policy modelling, the most recent from 2012 to 2015 combining data/text mining and agent-based simulation to analyse the global dynamics of extortion racket systems.
He authored, co-authored, and co-edited several books and many articles in social simulation, and he organised or co-organised a number of national and international conferences in this field. Over nearly three decades he advised and/or supervised more than 55 PhD theses, most of them in the field of social simulation. He offered annual summer and spring courses in social simulation between 1997 and 2009; more recent courses of this kind are now being organised by the European Social Simulation Assiciation and held at different places all over Europe (mostly with his contributions).
Computational social science, structuralist theory reconstruction
Sedar is a PhD student at the University of Leeds, department of Geography. He graduated in Computer Science at King’s College London 2018. From a very early stage of his degree, he focused on artificial intelligence planning implementations on drones in a search and rescue domain, and this was his first formal attempt to study artificial intelligence. He participated in summer school at Boğaziçi University in Istanbul working on programming techniques to reduce execution time. During his final year, he concentrated on how argumentation theory with natural language processing can be used to optimise political influence. In the midst of completing his degree, he applied to Professor Alison Heppenstall’s research proposal focusing on data analytics and society, a joint endeavour with the Alan Turing Institute and the Economic and Social Research Council. From 2018 - 2023 he will be working on his PhD at the Alan Turing Institute and Leeds Institute for Data Analytics.
Sedar will be focusing on data analytics and smart cities, developing a programming library to try simulate how policies can impact a small world of autonomous intelligent agents to try deduce positive or negative impact in the long run. If the impact is positive and this is conveyed collectively taking into consideration the agent’s health, happiness and other social characteristics then the policy can be considered. Furthermore, he will work on agent based modelling to solve and provide faster solutions to economic and social elements of society, establishing applied and theoretical answers. Some other interests are:
Eric Kameni holds a Ph.D. in Computer Science option modeling and application from the Radboud University of Nijmegen in the Netherlands, after a Bachelor’s Degree in Computer Science in Application Development and a Diploma in Master’s degree with Thesis in Computer Science on “modeling the diffusion of trust in social networks” at the University of Yaoundé I in Cameroon. My doctoral thesis focused on developing a model-based development approach for designing ICT-based solutions to solve environmental problems (Natural Model based Design in Context (NMDC)).
The particular focus of the research is the development of a spatial and Agent-Based Model to capture the motivations underlying the decision making of the various actors towards the investments in the quality of land and institutions, or other aspects of land use change. Inductive models (GIS and statistical based) can extrapolate existing land use patterns in time but cannot include actors decisions, learning and responses to new phenomena, e.g. new crops or soil conservation techniques. Therefore, more deductive (‘theory-driven’) approaches need to be used to complement the inductive (‘data-driven’) methods for a full grip on transition processes. Agent-Based Modeling is suitable for this work, in view of the number and types of actors (farmer, sedentary and transhumant herders, gender, ethnicity, wealth, local and supra-local) involved in land use and management. NetLogo framework could be use to facilitate modeling because it portray some desirable characteristics (agent based and spatially explicit). The model develop should provide social and anthropological insights in how farmers work and learn.