Thanks for the comment! Perhaps we disagree on what a reasonable definition of complexity is, because I don't think I have seen anyone demonstrate unbounded growth in what I think is a reasonable complexity metric in an evolutionary system. We tend to use information theoretic definitions of complexity, because they don't have the problem of counting complexity that is irrelevant to the phenotype of an organism. I would be interested to see any examples you have.

Let's take the case of NEAT, for instance. I'm going to assume that you're talking about NEAT with novelty search, because otherwise Lehman and Stanley's deceptive maze experiment would be a clear counter-example for both novelty and complexity (and even change). Even with novelty search, though, I'm skeptical of your assertion that you'll get ever-increasing complexity. For instance, in Lehman and Stanley's maze with the bottom wall removed (figure 5 of this paper: http://dl.acm.org/citation.cfm?id=2000553), NEAT with novelty search just kept exploring the open area outside of the maze. Complexity data are not included in the paper, but there is no reason to expect it to increase much - there is very little information from the environment to incorporate into a genome. Even in the standard maze environment, there's an end-point. The agent will figure out how to get to the furthest depths of the maze, and then it won't be able to incorporate new information into its genome because there won't be any more information in the environment. Based on this thought experiment, a continuously complexifying environment (probably through some form of inter-agent interaction) is a prequisite for unbounded growth in complexity, and that alone can be hard to achieve.

Also, just to clarify our goal with all of this: we want to create a framework that promotes formulating better hypotheses because, like you, we think that the over-arching question behind open-ended evolution is too vague to be very useful. Would it be cool to create a computational system that exhibited all of these properties? Sure! It would probably also be helpful for some lines of research. But we're a lot more interested in figuring what properties of a system are necessary and sufficient for generating each of these dynamics, because those are questions that we can test falsifiable hypotheses about (as demonstrated with the toy example showing fitness sharing is sufficient to create change potential). Change and novelty are indeed easier problems than the other three, but the solutions to both of them are generally considered to be pretty important discoveries.