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.

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 study small- and large-scale sustainable resource management using a variety of techniques including mathematical modeling, agent-based simulation, and Statistical Inference

Quantitative research in economics.

Complex systems, agent-based modeling, social simulation, computational models, network models, network theory, methodology, philosophy of science, ontology, epistemology, ethics, artificial intelligence, big data analysis, geospatial data analysis,

My simulation research focuses on the applications of ABM to organizational behavior studies. I study socially-distributed decision making—i.e., the process of exploiting external resources in a social environment—and I work to develop its theoretical underpinnings in order to to test it. A second stream of research is on how group dynamics affect individual perceptions of social responsibility and on the definition and measurement of individual social responsibility (I-SR).

How to model it: Ecological models, in particular simulation models, often seem to be formulated ad hoc and only poorly analysed. I am therefore interested in strategies and methods for making ecological modelling more coherent and efficient. The ultimate aim is to develop preditive models that provide mechanstic understanding of ecological systems and that are transparent and structurally realistic enough to support environmental decision making.

Pattern-oriented modelling: This is a general strategy of using multiple patterns observed in real systems as multiple criteria for chosing model structure, selecting among alternative submodels, and inversely determining entire sets of unknown model parameters.

Individual-based and agent-based modelling: For many, if not most, ecological questions individual-level aspects can be decisive for explaining system-level behavior. IBM/ABMs allow to represent individual heterogeneity, local interactions, and/or adaptive behaviour

Ecological theory and concepts: I am particularly interested in exploring stability properties like resilience and persistence.

Modelling for ecological applications: Pattern-oriented modelling allows to develop structurally realistic models, which can be used to support decision making and the management of biodiversity and natural resources. Currently, I am involved in the EU project CREAM, where a suite of population models is developed for pesticide risk assessment.

Standards for model communication and formulation: In 2006, we published a general protocol for describing individual- and agent-based models, called the ODD protocol (Overview, Design concepts, details). ODD turned out to be more useful (and needed) than we expected.

My research aims to explore the potential of network science for the archaeological discipline. In my review work I confront the use of network-based methods in the archaeological discipline with their use in other disciplines, especially sociology and physics. In my archaeological work I aim to develop and apply network science techniques that show particular potential for archaeology. This is done through a number of archaeological case-studies: archaeological citation networks, visibility networks in Iron Age and Roman southern Spain, and tableware distribution in the Roman Eastern Mediterranean.

General Question:
Without Central Control is self organization possible?

Specific Case:

Considering the seemingly preplanned, densely aggregated communities of the prehistoric Puebloan Southwest, is it possible that without centralized authority (control), that patches of low-density communities dispersed in a bounded landscape could quickly self-organize and construct preplanned, highly organized, prehistoric villages/towns?