The following links are for students in MXB261 in case Canvas has issues again: Week 1: Maths refresher I Week 1 lecture recording Password: TEY*V$6q Week 1 lecture slides annotated Week 1 workshop questions and solutions Week 2: Maths refresher II Week 2 lecture recording Password: k!xCY5?& Week 2 lecture slides annotated Week 2 workshop questions and solutions
Students miss out on in-person workshops for all kinds of reasons, including work and caring responsibilities. It’s not enough to supply worked solutions because that doesn’t facilitate the productive struggle that deepens understanding and builds self-efficacy.
This semester, I had the opportunity to do 7 weeks of “interactive lectures” at QUT for MXB261 Modelling and Simulation Science, as well as one guest lecture on evolutionary game theory. In this blog post, I’ll talk about my experiences, share the first half of one of the lectures I gave, and provide the code, to generate the figures, in case any of that is useful to anyone.
For the past year, I’ve been working in my spare time through a very interesting paper by Kleshnina et al. (2023). The paper concerns an evolutionary game theory model where individuals play an iterated Prisoner’s Dilemma with an environmental feedback. In a previous post, I experimented with a method to automate the identification of the subgame-perfect Nash equilibria and their parameter-value…
Today, I gave a talk at Griffith University’s about my two most recent papers about the evolution of cooperation, published in Scientific Reports and Journal of Theoretical Biology. You can download the full slide deck here: evoln_coopn.pdf.
In an unpublished paper, Staab et al. (2022) studied the effects of heterogeneity in endowments and productivities on cooperation. They were particularly interested in the effect of allowing players to share the jointly produced goods unequally. Surprisingly, they found that unequal sharing can promote cooperation.
This morning, I attended an engaging virtual workshop on modelling and applications of evolutionary game theory.
This blog post summarises one of the results I obtained from some work I did back in 2015 with Eve McDonald-Madden at the University of Queensland. She didn’t end up using it for anything, but perhaps someone else might find it useful.
I recently read a paper by Kleshnina et al. (2023), The effect of environmental information on evolution of cooperation in stochastic games, which provided an opportunity to teach myself about how to analyse iterated games. In particular, the problem they investigated admits 64 possible scenarios with 256 possible strategies each, and I was interested in writing code that could automate the…
Metal.