I often argue that a big advantage of working with computational artificial life systems is that you can perform evolution experiments that would be difficult (or impossible!) with natural organisms. However, any time you want to use a digital system to understand how or why a particular trait or behavior evolves, your first challenge is to get that trait evolve at all! Warning: this can be a lot harder than it sounds. Of course, in this quest you will also learn a lot more about your study system, gain more insight into the dynamics you are investigating, and probably come up with at least half-a-dozen follow-up studies. Below are the steps that I suggest for new projects.
Recently, I was nominated for the Board of Directors for the International Society for Artificial Life (ISAL). After serious consideration about whether I could devote the necessary time and whether I felt I would be able to make a difference, I’ve decided to run. Normally, I try to say “no” to all such requests, but in this case I care quite a bit about the society and the field as a whole, and I want to play my part. Below is the 250-word statement I submitted with my acceptance of the nomination, followed by some more details that I couldn’t fit in the limited space.
Stommel plots are a popular tool in ecology for thinking about the spatial and temporal scales at which processes and patterns occur. Named for Henry Stommel, who created the first such plot (shown below), these plots generally have spatial scale on the x axis, temporal scale on the y axis, and some variable indicating strength of effect on the z axis. More recently, the z axis has often been replaced with colored circles, but the concept remains the same. For more information about the history of Stommel plots, see this post. These plots are often helpful in figuring out appropriate scales for data collection and analysis, as well as for conceptualizing how processes and patterns interact. But I have yet to see one that incorporates evolution.
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.