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 Guassa, 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.
B3GET 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 fighting ability and aggression, sperm competition, infanticide, and competition over access to food and mates. Downloaded materials include a starting genotype and population files. Edit the these files and see what changes occur in the behavior of virtual populations!
B3GET Classic includes previous versions used in the classroom and for publication. Please check out the latest version of B3GET here, which has several user-friendly features such as directly importing and exporting genotype and population files.
The classic versions of B3GET include: version one was and version three is currently used in undergraduate labs at the University of Minnesota to demonstrate principles in primate behavioral ecology; version two first demonstrated proof of concept for creating virtual biological organisms using decision-vector technology; version four was presented at the 2017 annual meeting at the American Association of Physical Anthropologists; version five was presented in a 2019 publication from the Journal of Human Evolution (Crouse, Miller, and Wilson, 2019).
This model replicates the Axelrod prisoner’s dilemma tournaments. The model takes as input a file of strategies and pits them against each other to see who achieves the best payoff in the end. Change the payoff structure to see how it changes the tournament outcome!
This model simulates the Hawk-Dove game as first described by John Maynard Smith, and further elaborated by Richard Dawkins in “The Selfish Gene”. In the game, two strategies, Hawks and Doves, compete against each other, and themselves, for reproductive benefits. A third strategy can be introduced, Retaliators, which act like either Hawks or Doves, depending on the context.
Fertility Tradeoffs is a NetLogo model that illustrates the emergencent tradeoffs between the quality and quantity of offspring. Often, we associate high fitness with maximizing the number of offspring. However, under certain circumstances, it pays instead to optimize the number of offspring, having fewer offspring than is possible. When the number of offspring is reduced, more energy can be invested in each offspring, which can be beneficial for their own fitness.
Dawkins’ Weasel is a NetLogo model that illustrates the principle of evolution by natural selection. It is inspired by a thought experiment presented by Richard Dawkins in his book The Blind Watchmaker (1996).
Evolution of Sex is a NetLogo model that illustrates the advantages and disadvantages of sexual and asexual reproductive strategies. It seeks to demonstrate the answer to the question “Why do we have sex?”