I found a neat article on an unlikely connection between physics and math--a similarity between energy levels of nuclei of heavy elements and Riemann's zeta function. I don't know the first thing about math or physics, but maybe someone with more background can say something interesting about this.

Digg, a social networking site (with emergent properties, maybe), has linked to a page boasting a set of sort algorithms faster than Quicksort. It's called Critticall, and it uses genetic algorithms to evolve C programs.

Here's my latest Netlogo model, a simulation of ants searching a field for food. Hopefully the model (with documentation) explains itself; if not, feel free to ask. Here it is

Here's a standard Langton's ant with a few plots that might be of use. Documentation on the page might be a little sparse--ask me or check out the source if anything's unclear. link

Here's a link to a paper written by a few researchers at the 'Logic Systems Laboratory'. The authors describe the behavior of several 'interesting' rulesets of >2-state ants, and then discuss systems with >1 ants. link [pdf]

I've been interested by computer modelling approaches to evolution for some time now, and I've come across a few papers on the subject that might offer something to a discussion of emergence. A sizeable part of evolutionary theory cannot practically be submitted to direct test. Evolution can only be observed in rare cases--among bacteria in petri dishes or finches on the Galapagos islands. This is where computer modelling comes in. It has been applied extensively to ideas about the evolution of cooperation in social species. Studies of this sort typically examine simulated interactions among members of a population, pitting them against each other in games like the prisoner's dilemma. Members of the population follow different strategies with different levels of cooperation. After some predetermined number of rounds of the game are played, a new "generation" is born, with its proportions of cooperators and defectors determined by the relative success of each strategy during the previous generation. This process is iterated over hundreds or thousands of generations. The entire simulation can be run with parameters--say, the payoffs and punishments of the game, or the initial proportions of each strategy--altered. Some configurations give rise to fixation of one strategy, others to a stable polymorphism, others to steady oscillations in strategy frequency, etc.