Writing big projects is hard. Virtually every single graduate student I know struggles with writing to some degree. Some hide it better than others. Some power through it with pure, teeth-clenching grit. Many simply fail. Large, multi-year projects, such as books, dissertations, or theses, require extensive scheduling, planning, and project management. However, few advanced students are equipped with the tools to tackle such large projects.
Hopefully you’re convinced of why you might want to use Avida (if not, check out Why Use Artificial Life to Study Evolutionary Biology). However, Avida is a pretty large and intimidating software system even for computer programmers, so I can only imagine how it feels to be a biologist looking at all those configuration files. Therefore, I’ll give you a step-by-step guide for which pages you should go to to get up and running with Avida as soon as possible, no computer science bachelor’s required.
Previously, I talked about what fitness means. Now, it’s worth taking a moment to talk about how we calculate fitness in a few of our experimental systems, both biological and computational. Because what exactly we choose to measure can result in different outcomes, the way in which we measure something is important and therefore valuable to think critically about.
At ECAL 2015, Tim Taylor, Mark Bedau, and Alastair Channon organized a fascinating workshop on Open-Ended Evolution, which I presented at (you can watch the video here, but this post will basically cover the same points). Several of us in the Devolab have been thinking about this topic for a while; below is a collection of our thoughts for the sake of continuing this discussion. The question of open-ended evolution emerged from a practical place: organisms and ecosystems in computational evolutionary systems were far less diverse, complex, and interesting than those that seen in nature. The people studying these systems were concerned that this was the result of a fundamental limitation to the systems (although some have also argued that this is just an issue of scale). They began characterizing the dynamics of these systems in […]
It probably comes as no surprise that I’m a fan of science blogs! When I first got interested in science communication though, I found it a bit difficult to find many that I wanted to read. There seems to be an air of not publicizing our blogs much because we should be spending our time on science instead. Therefore, here at the Devolab, we’re starting a blogroll page where we’ll keep a list of all the other science blogs we follow and when we add a few, we’ll probably post about them so you don’t forget to check the blogroll.