Bryann Avendaño Member since: Monday, June 29, 2015

B.Sc. Biologist, B.Sc. Ecologist, Applied Statistics and Systems Dynamic Modelling

Ecology - Natural Resources Management (Community-based management)

I worked on natural resources management modelling in STELLA. I developed a technical and scientific model to analyze soil, climate and biological conditions to explain how Bamboo ecosystem works and how people in Cundinamarca, Colombia could focus on a sustainable model for use and manage forestry resources.
Also, I worked on the seventh framework program named: Community-based management of Environmental Challenges in Latin America -COMET-LA-. The project built a learning arena with scientists, civil society and government to identify sustainable models for governance of natural resources in social-ecological systems located in a rural context from Colombia, México and Argentina.

I am interesting in research on Modelling of governance and Community-based management of natural resources.

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.

Gul Deniz Salali Member since: Sunday, November 15, 2015 Full Member

PhD in Biological Anthropology, UCL

I studied Molecular Biology and Genetics at Istanbul Technical University. During my undergraduate studies I became interested in the field of Ecology and Evolution and did internships on animal behaviour in Switzerland and Ireland. I then went on to pursue a 2-year research Master’s in Evolutionary Biology (MEME) funded by the European Union. I worked on projects using computer simulations to investigate evolution of social complexity and human cooperation. I also did behavioural economics experiments on how children learn social norms by copying others. After my Master’s, I pursued my dream of doing fieldwork and investigating human societies. I did my PhD at UCL, researching cultural evolution and behavioural adaptations in Pygmy hunter-gatherers in the Congo. During my PhD, I was part of an inter-disciplinary Hunter-Gatherer Resilience team funded by the Leverhulme Trust. I obtained a postdoctoral research fellowship from British Academy after my PhD. I am currently working as a British Academy research fellow and lecturer in Evolutionary Anthropology and Evolutionary Medicine at UCL.

  • Social learning and cultural evolution
  • Hunter-gatherers
  • Evolutionary medicine

Joseph A. E. Shaheen Member since: Wednesday, April 01, 2020 Full Member

Ph.D., Computational Social Science, George Mason University, MBA, Georgetown University, BSc, Engineering:Physics, Murray State University

Joseph is an Intelligence Community Postdoc Fellow (ODNI/NCTC) co-located with the faculty of the Department of Computational and Data Sciences at George Mason University. Since his first day of university training at age 15 and having earned his undergraduate degree in Engineering:Physics at age 19, his 15 years of industry experience has been diverse, ranging from industrial engineering to people analytics.

Dr. Shaheen earned his doctorate in Computational Social Science from GMU with a dissertation on economic policy and population-scale data analysis of Internal Revenue Service records. There, he studied all U.S. firms from a biologically-grounded perspective under the guidance of Professor Rob Axtell’s research group.

Following his U.S. State Department-funded assignment with the NATO STRATCOM Centre of Excellence where he conducted large scale analysis and provided policy recommendations in the fight against ISIS/ISIL/Daesh, he has been a guest speaker on issues of Information and \textit{Social Media Warfare}–a term closely associated with his 2015 NATO report–at the Pentagon (J-39 SMA), NATO Defense Against Terrorism COE, National Defense University, OMCC and others.

A life-long scholar, Joe has received training from academic leaders in Social Network Analysis and has been recognized as an honorary Links Center Fellow in 2015 and by GMU’s Teaching Excellence award in 5 consecutive iterations.

He has appeared on CNN HLN, FOX NEWS, NBC News, Entrepreneur Magazine and has been invited to participate in the 2020 (postponed to 2021) Heidelberg Laureate Forum (Heidelberg, Germany) where he will spend time with fellow scholars of the mathematical and computer sciences as well as Fields Medal, Abel Prize, Turing Award, and Nevanlinna prize winners.

In his free time, Joe enjoys a sense of humor and practices portrait, landscape and wildlife photography. Even so - he admits, he has never been able to successfully take one decent photo of himself

Agent-based Modeling
Social Network Analysis
Network Science
Public Policy
Security Policy
Taxation Policy

Volker Grimm Member since: Wednesday, July 18, 2007 Full Member Reviewer

Volker Grimm currently works at the Department of Ecological Modelling, Helmholtz-Zentrum für Umweltforschung. Volker does research in ecology and biodiversity research.

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.

Kristin Crouse Member since: Sunday, June 05, 2016 Full Member Reviewer

B.S. Astronomy/Astrophysics, B.A. Anthropology

I am a PhD Candidate in the Biological Anthropology program at the University of Minnesota. My research involves using agent-based models combined with field research to test a broad range of hypotheses in biology. I have created a model, B3GET, which simulates the evolution of virtual organisms to better understand the relationships between growth and development, life history and reproductive strategies, mating strategies, foraging strategies, and how ecological factors drive these relationships. I also conduct field research to better model the behavior of these virtual organisms. Here I am pictured with an adult male gelada in Ethiopia!

I specialize in writing agent-based models for both research in and the teaching of subjects including: biology, genetics, evolution, demography, and behavior.

For my dissertation research, I have produced “B3GET,” an agent-based model which simulates populations of virtual organisms evolving over generations, whose evolutionary outcomes reflect the selection pressures of their environment. The model simulates several factors considered important in biology, including life history trade-offs, investment in body size, variation in aggression, sperm competition, infanticide, and competition over access to food and mates. B3GET calculates each agent’s ‘decision-vectors’ from its diploid chromosomes and current environmental context. These decision-vectors dictate movement, body growth, desire to mate and eat, and other agent actions. Chromosomes are modified during recombination and mutation, resulting in behavioral strategies that evolve over generations. Rather than impose model parameters based on a priori assumptions, I have used an experimental evolution procedure to evolve traits that enabled populations to persist. Seeding a succession of populations with the longest surviving genotype from each run resulted in the evolution of populations that persisted indefinitely. I designed B3GET for my dissertation, but it has an indefinite number of applications for other projects in biology. B3GET helps answer fundamental questions in evolutionary biology by offering users a virtual field site to precisely track the evolution of organismal populations. Researchers can use B3GET to: (1) investigate how populations vary in response to ecological pressures; (2) trace evolutionary histories over indefinite time scales and generations; (3) track an individual for every moment of their life from conception to post-mortem decay; and (4) create virtual analogues of living species, including primates like baboons and chimpanzees, to answer species-specific questions. Users are able to save, edit, and import population and genotype files, offering an array of possibilities for creating controlled biological experiments.

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