What a strange article. A professor makes a throwaway comment about wheels in the 1970s, and so Ann decides to show some pictures of circular proteins/enzymes (not wheels).
The implicit argument seems to be this: wheel-like structures are so intricate and finely engineered that they must be designed, not evolved. The professor’s comment is cited to show unlikely an evolutionary explanation is. So the various biochemical wheelies must be the result of ID.
FWIW, I happen to think this is a terribly weak scientific argument for the scientific form of ID.
I think if it were a strong scientific argument you wouldn’t see it first in ENV.
Her prof was right. No organisms move by means of wheels, like a car or bicycle does. I doubt he meant nothing in nature will be roughly circular. Sure he was already aware of things like jelly fish and mushroom caps.
Anyway, I also fail to see what design argument Gauger thinks she is making. Maybe the most revealing part of the article is the first paragraph, where she admits she often had no clue what her profs were talking about.
8 posts were split to a new topic: The DI and employment
This admission is part of the standard pulpit etiquette. The preacher confesses confusion or shortcoming so as to convey empathy with the congregation.
I fully believe that Ann eventually grasped the material via perseverance and intelligence.
Anybody remember the flash simulation boxcar2d?
I’ve occasionally heard creationists say that the simulation is sort of “front loaded” with information because the cars all start out with wheels.
So I did something a bit different. I set the simulation to disallow wheels (just set max wheel frequency to 0) and then pick the landscape that has a small hill just to the right of the starting position(called The Peak).
The simulation managed to evolve an approximate wheel in about 100 generations if I remember correctly. It’s pretty clear when you watch it evolve that it is only limited by the maximum number of vertices a “car” can have, which is 8.
What happens is random shapes will be dropped to the ground, and they will all fall apart, and while falling apart they will move a tiny bit, either to the left or to the right. The better they are at turning this initial drop into rightwards motion without falling apart, the higher a score they get. Higher scoring shapes are more likely to make it to the next generation. Rinse and repeat.
Here’s a small video where I copied the evolved wheel into another run of the simulation to see if it could be improved further, I had a pretty big improvement after about 30 generations(wheels at generation 59-60 are shown):
Yes, I experimented with it some time back. Fun stuff. IIRC I tried the same thing you did.
As with almost all such EAs, the creationist blatherskite about front-loading can be refuted without pause for thought simply by noting that different runs don’t produce the same results.
paper by Harvey A, Zukoff S (2011) “Wind-Powered Wheel Locomotion, Initiated by Leaping Somersaults, in Larvae of the Southeastern Beach Tiger Beetle (Cicindela dorsalis media),” PLoS ONE 6(3): e17746.
Abstract: “Rapid movement is challenging for elongate, soft-bodied animals with short or no legs. Leaping is known for only a few animals with this “worm-like” morphology. Wheel locomotion, in which the animal’s entire body rolls forward along a central axis, has been reported for only a handful of animals worldwide. Here we present the first documented case of wind-powered wheel locomotion, in larvae of the coastal tiger beetle Cicindela dorsalis media. When removed from their shallow burrows, larvae easily can be induced to enter a behavioral sequence that starts with leaping; while airborne, larvae loop their body into a rotating wheel and usually either “hit the ground rolling” or leap again. The direction larvae wheel is closely related to the direction in which winds are blowing; thus, all our larvae wheeled up-slope, as winds at our study site consistently blew from sea to land. Stronger winds increased both the proportion of larvae wheeling, and the distance traveled, exceeding 60 m in some cases. In addition, the proportion of larvae that wheel and the distance traveled by wheeling larvae are significantly greater on smooth sandy beaches than on beach surfaces made rough and irregular by pedestrian, equestrian, and vehicular traffic. Like other coastal species of tiger beetles, C. dorsalis media has suffered major declines in recent years that are clearly correlated with increased human impacts. The present study suggests that the negative effects of beach traffic may be indirect, preventing larvae from escaping from predators using wheel locomotion by disrupting the flat, hard surface necessary for efficient wheeling.”
I re-did the Boxcar2d wheel evolution, it’s been going all day and it’s at generation 294 as I write this. I’m intrigued to see huge fitness jumps from rare mutations. It seems to get stuck at some local optimum for a long time until a rare strongly beneficial mutation occurs. Will probably still be quite a while before it gets on-par with the octagons that evolved in earlier runs. I remember it was stuck at the same place for a really long time previously.
Can you compute the FI of the task at different thresholds using math that has been proposed by others?
I wouldn’t know how to estimate the number of possible configurations above the threshold, I mean except empirically of course, but it would take ages to sample that space significantly(the software does not simulate an entire population at once, it simulates one individual at a time, so population sizes are small to keep the generations short in real time). I also don’t know the actual total size of the search space.
I wanted to do something like that once but I needed to know the precision with which the software specifies the angles of the triangles that make up the “car”. I wrote the guy who made the simulation but he never answered.
That’s exactly right! Do it emperically! Is this the true FI? Probably not. It is the measured FI, which is all we can emperically access.