![]() While it is relatively easy to come up with hypotheses that could plausibly explain observed evolutionary outcomes, we often fail to take the next step of confirming that our proposed mechanism accurately describes the underlying evolutionary dynamics. Visualization is a powerful tool for exploring evolutionary history as it actually played out. We can create visualizations that summarize the evolutionary history of a population or group of populations by drawing representative lineages on top of the fitness landscape being traversed. This approach integrates information about the adaptations that took place with information about the evolutionary pressures they were being subjected to as they evolved. However, these visualizations can be challenging to depict on a two-dimensional surface, as they integrate multiple forms of three-dimensional (or more) data. Here, we propose an alternative: taking advantage of recent advances in virtual reality to view evolutionary history in three dimensions. This technique produces an intuitive and detailed illustration of evolutionary processes. A demo of our visualization is available here. Ībstract We discuss approaches to agent-based model visualization. Agent-based modeling has its own requirements for visualization, some shared with other forms of simulation software, and some unique to this approach. In particular, agent-based models are typified by complexity, dynamism, nonequilibrium and transient behavior, heterogeneity, and a researcher's interest in both individual- and aggregate-level behavior. These are all traits requiring careful consideration in the design, experimentation, and communication of results. In the case of all but final communication for dissemination, researchers may not make their visualizations public. Hence, the knowledge of how to visualize during these earlier stages is unavailable to the research community in a readily accessible form. Here we explore means by which all phases of agent-based modeling can benefit from visualization, and we provide examples from the available literature and online sources to illustrate key stages and techniques. Recently, Adami and coworkers have been able to measure the information content of digital organisms living in their Avida artificial life system. They show that over time, the organisms behave like Maxwell’s demon, accreting information (or complexity) as they evolve. In Avida the organisms don’t interact with each other, merely reproduce at a particular rate (their fitness), and attempt to evaluate an externally given arithmetic function in order win bonus fitness points.
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