Community

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

Arika Ligmann-Zielinska Member since: Tuesday, April 08, 2008 Full Member Reviewer

PhD

I am a spatial (GIS) agent-based modeler i.e. modeler that simulates the impact of various individual decisions on the environment. My work is mainly methodological i.e. I develop tools that make agent-based modeling (ABM) easier to do. I especially focus on developing tools that allow for evaluating various uncertainties in ABM. One of these uncertainties are the ways of quantifying agent decisions (i.e. the algorithmic representation of agent decision rules) for example to address the question of “How do the agents decide whether to grow crops or rather put land to fallow?”. One of the methods I developed focuses on representing residential developers’ risk perception for example to answer the question: “to what extent is the developer risk-taking and would be willing to build new houses targeted at high-income families (small market but big return on investment)?”. Other ABM uncertainties that I evaluate are various spatial inputs (e.g. different representations of soil erosion, different maps of environmental benefits from land conservation) and various demographics (i.e. are retired farmers more willing to put land to conservation?). The tools I develop are mostly used in (spatial) sensitivity analysis of ABM (quantitative, qualitative, and visual).

Sedar Olmez Member since: Wednesday, November 06, 2019 Full Member

MSci in Computer Science, MSc in Data Analytics and Society

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:

  • Multi-agent systems
  • Intelligent agents
  • Natural language processing
  • Artificial intelligence planning
  • Machine learning
  • Neural networks
  • Genetic programming
  • Geocomputation
  • Argumentation theory
  • Smart cities

Steve Peck Member since: Friday, April 24, 2020 Full Member

Biographical Sketch

(a) Professional Preparation

Brigham Young University Statistics & Computer Science B.S. 1986
University of North Carolina Chapel Hill Biostatistics M.S. 1988
North Carolina State University Biomathematics & Entomology Ph.D. 1997

(b) Appointments

Associate Professor 2006-current: Brigham Young University Department of Biology
Assistant Professor 2000-2006: Brigham Young University Department of Integrative Biology
Research Scientist 1997-1999: Agriculture Research Service-USDA Pacific Basin Agricultural Research Center.

(c) Publications

i. Five most relevant publications

Ahmadou H. Dicko, Renaud Lancelot, Momar Talla Seck, Laure Guerrini, Baba Sall, Mbargou Low, Marc J.B. Vreysen, Thierry Lefrançois, Fonta Williams, Steven L. Peck, and Jérémy Bouyer. 2014. Using species distribution models to optimize vector control: the tsetse eradication campaign in Senegal. Proceedings of the National Academy of Science. 11 (28) : 10149-10154
Peck, S. L. 2014. Perspectives on why digital ecologies matter: Combining population genetics and ecologically informed agent-based models with GIS for managing dipteran livestock pests. Acta Tropica. 138S (2014) S22–S25
Peck, S. L. and Jérémy Bouyer. 2012. Mathematical modeling, spatial complexity, and critical decisions in tsetse control. Journal of Economic Entomology 105(5): 1477—1486.
Peck, S. L. 2012. Networks of habitat patches in tsetse fly control: implications of metapopulation structure on assessing local extinction probabilities. Ecological Modelling 246: 99–102.
Peck, S. L. 2012. Agent-based models as fictive instantiations of ecological processes.” Philosophy & Theory in Biology. Vol. 4.e303 (2012): 12

ii. Five other publications of note

Peck, S. L. 2008. The Hermeneutics of Ecological Simulation. Biology and Philosophy 23:383-402.
K.M. Froerer, S.L. Peck, G.T. McQuate, R.I. Vargas, E.B. Jang, and D.O. McInnis. 2010. Long distance movement of Bactrocera dorsalis (Diptera: Tephritidae) in Puna, Hawaii: How far can they go? American Entomologist 56(2): 88-94
Peck, S. L. 2004. Simulation as experiment: a philosophical reassessment for biological modeling. Trends in Ecology and Evolution 19 (10): 530 534
Storer N.P., S. L. Peck, F. Gould, J. W. Van Duyn and G. G. Kennedy. 2003 Sensitivity analysis of a spatially-explicit stochastic simulation model of the evolution of resistance in Helicoverpa zea (Lepidoptera: Noctuidae) to Bt transgenic corn and cotton. Economic Entomology. 96(1): 173-187
Peck, S. L., F. Gould, and S. Ellner. 1999. The spread of resistance in spatially extended systems of transgenic cotton: Implications for the management of Heliothis virescens (Lepidoptera: Noctuidae). Economic Entomology 92:1-16.

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 developed “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.

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.