Thank you for your comments. This thread has now scrolled off the main page. As I post related threads, feel free to raise your points again. I think the readers will appreciate discussion of the issues you raise.

Salvador

Could you, in turn, demonstrate that CSI, the fundamental quantity of Intelligent Design arguments, is computable in such a simplified case as this? Can you calculate the CSI contained by, say, the formal solution and a MacGyver of your choice? Can you, then, show us exactly how and where the same amount of CSI is hidden in Dave’s code? This involves *more* than merely pointing at specific lines of code. It means actually calculating, with clear explanations, the amount of CSI hidden the “sneaky” code lines. It would also be important to explain exactly how these bits were transferred into the solution.

However, I’m not sure that it is justified to single out biology as *the* problematic science. As you more or less admitted, we can hardly show experimentally that the laws of classical physics follow from Quantum Mechanics. We can, with great effort, demonstrate that QM makes successful predictions on subatomic level. Similarly, biologists *have*, with great effort, made successful predictions on a wide range of issues from insect behavior to unicellular evolution. But it is next to impossible to explain the behahvior of a physical system of macroscopic scale in terms of individual “quantums”, just as it is next to impossible to explain the evolution of a population over evolutionary time in terms of individual selective pressures. (That is, if an *explanation* means something “fully detailed”.)

Also, I don’t think Dave Thomas ever claimed that his program was a simulation of biological evolution. He only claimed that it is a demonstration of a blind algorithm producing CSI by exploiting cumulative selection. And I certainly think Thomas was successful in what he set out to do. He uses a very simple fitness test (“consume less energy to be more fit”) which produces a diverse “family tree”, the surviving leaves of which tend to be MacGyvers. Does this fitness function “hide” intelligent design, and is it highly “unnatural”, whatever that is? Hardly. Although no natural population may have had to solve this specific Steiner problem (I wouldn’t bet on it, though!), all natural populations probably have addressed the “minimize energy consumption” problem in one way or the other!

Thank you for taking the time to read through the articles I linked to and responding. But I must caution, what scientific enterprise can hope to survive if it cannnot measure it’s most fundamental quantities??

In other scientific disciplines, we can measure mass, charge, energy, power, position, time, dimensions (well, at least at classical versus atomic scales). But the inability to measure fitness, a quantity so fundamental to a theory, seems extremely disconcerting. If one can’t measure fundamental quantities upon which a theory fundamentally relies, one then has to question the coherence of the theory in the first place as well as the ability to confirm it experimentally.

Granted, we do have Quantum Mechanics where the unmeasurability of observables is par for the course, but at least the theory is stated in terms of what can’t be measured! I find no comparable analogue for Natural Seleciton, and it is this fact that caused a few evolutioanry biologists to jump ship.

Because we have such a difficult time even measuring what’s in the real world, isn’t it a bit pre-mature to say our computer fitness correctly models something which we aren’t even sure exists?

I’m fine with operational science (like electrodynamics, celestial mechanics, chemistry, engineering, etc.), but forensic “science” (like evolutionary theory) ought perhaps be put in another category because we don’t have the same level of verifiability.

I’m not immediately saying here that ID is the answer (even though that is my personal view, and I certainly promote it), but ID issues aside, shouldn’t a greater degree of skepticism that has been practiced be welcome given the state of affairs as outlined by Lewontin and others?

Salvador

BTW. In the same paper Lewontin claims (“Getting there from here”) that complex evolutionary pathways have been experimentally verified to exist, with functional intermediates. How about that? (Yes, his main point is that such pathways are a subset in a “maze” with many dead ends, but I don’t think that is much of a problem for the theory of evolution. It is simply a partial answer to his own question: why only a tiny subset of all conceivable phenotypes have realized in the history of life.)

Caligula asked:

And listen, exactly what do you think natural selection is?

I think it is double speak, and does not reflect reality at all. Selection, as Allen Orr pointed out, does not trade in the language of design. As lewontin showed, its rife with mathematical self-contradiction. I fixed up some of the links to Lewontin above. I higly recommend his Santa Fe Winter 2003 essay. Quite eye-opening to the utter futility of describing evolution in terms of fitness.

Biology should be more appropriately described by function (an engineering perspective) versus fitness (a self-contradictory Darwinian paradigm).

I’m speechless.

…would have a CHANCE of being MARGINALLY successful? Is *that* what makes it cheating? If only the program was guaranteed to *fail*, it would immediately become a valid demonstration of random mutations with ID-free selection at work?

Listen, of course evolutionary algorithms and e.g. reinforcement learning are used exactly because we think they *might* succeed. But as you said yourself, all we know — or hope mostly, although hopes turned true are the ones that get published — is that these techniques have a *chance* of being successful. And indeed, they are favored in cases where we don’t need to get a 100% accurate answer to a problem. Instead, we oftentimes want to get sufficiently accurate answers to a whole bunch of problems — such as evaluating each of the possible states of the enviroment (e.g. valid game positions of a strategy game) w/o getting too many downright stupid evaluations.

And listen, exactly what do you think natural selection is? Is it a monkey or a randomizer? Sure, selection pressures can and do change both in quantity and quality, but they are definitely not wildly *random* (because they are part of Cosmos instead of Chaos, for starters). And yet, that’s exactly what you seem to be requiring from GAs in order for them to be “natural” in the quote above. If so, it is little wonder you think it can’t produce the illusion of design. You are saying that a selection pressure favoring blindness should grow ears!

[Dave Thomas wrote:]

My challenge to Salvador and the UD Software Engineering Team is simple and straightforward: if the Target’s “shirt” (a stated desire for the shortest connected straight-line networks) is indeed as “CLOSE” to the “Target” itself (the actual Steiner Solution for the given array of fixed points) as you say it is, then you and your Team should be easily able to deduce the proper answer, and send it along.

I’ll be waiting! See you next Monday, August 21st. – Dave

Thomas mis-describes the sense of my argument. The specification of a problem solving STRATEGY and successfully implementing that STRATEGY will yield solutions equivalent to some or all of the solutions in the solution space (or maybe good enough). Thomas mis-describes my position again. Aiming for the shirt versus the person is like aiming to find the right strategy. That’s what I meant. If he mis-understood for whatever reason be it me or him, I hope this helps clarify the issue.

With respect to my ga.c, of course I knew the program was rigged. I knew the seach strategy would work. Searching for a strategy is like finding a sufficient aimpoint. I provided 4 inexplicit strategies:

brute.c
gauss.c
recurs.c
ga.c

Each is a different strategy for hitting the same target. 4 different sets of driving directions leading to Rome from 4 different starting points, so to speak.

Of course the programs fitness function was rigged, and it’s no less rigged than the Steiner-solving GA’s. That’s the whole point. Did Dave Thomas not know in advance his fitness function would have a chance of being marginally successful (in a MacGeyver sense at least), or did he have some monkey code his fitness functions or describe the fitness function to him?

By the way, I’m honored to see Dave is effectively calling me liar.

cheerio guys,
Sal
PS
computing Fermat points is a bit tedious, if I have inclination I might provide them and finish of my speculation for a solution to his problem

“Avida promoters claim they refuted Behe’s notion of irreducible complexity (IC) with their Avida computer simulation.”

Actually, no. What GAs show is that it is possible to create systems via evolutionary mechanisms where the removal of any component makes the entire system fail. Really, there are two classes of IC then: “Class A” systems which can be constructed incrementally, but (at some later stage) fail if one piece is removed, and “Class B” systems which cannot be constructed incrementally and fail entirely if one piece is removed. Simply pointing out that a biological system fails if one piece is removed doesn’t tell you whether you’re dealing with a class A system (which evolution can create) or a class B system (which evolution cannot create). Careful research is needed to differeniate between then two, and IDists want to jump to the conclusion that any IC system is actually a class B system. GAs illuminated the fact that not all IC systems are class B systems (as IDists would like to argue).

By in large, Sal’s general argument seems to be that GAs, whether or not there is an explicit target, employ a very specific and limited strategy to find a specific solution. What Sal misses is the fact that GAs are much more capable he gives them credit for. First of all, Sal’s “GA” (if you can call it that) is specifically setup to hone-in on a solution to a problem that has one and only one solution. Actual GAs, on the other hand, are well known to be able to find completely different solutions (on different runs) to a single problem – they aren’t secretly preprogrammed with the solution, hard-coded to hone-in on that solution, or merely deriving the solution through a series of deterministic mathematical steps. GAs are moving through complex space and finding varieties of solutions that satisfy it’s goal.

“Is the selection process in Thomas’s code natural or intelligently designed?”

The selection process is intelligently designed. However, that isn’t particularly relevant when you understand how GAs work. The selection process (or fitness function) is used to determine which organisms (real or digital) go on to reproduce. The descendent organisms are similar to the parents that they descend from. Thus, a selection process gets evolution moving in a particular direction. Since we want a particular outcome (e.g. shortest route between six points), we want each subsequent generation to be closer to that goal and so we allow the best ones to have children. It’s not the goal that gets the organisms to evolve in a particular direction, but it’s actually the survival differential that makes them evolve. The GA goal is simply used to determine who reproduces and who doesn’t – in other words, it determines how the survival differential is applied across the population. In the absence of a goal, the organisms can evolve in a particular direction simply by having a survival differential – provided that the differential is non-random. In the real world, organisms are hunters, prey (by predators and micro-organisms), and competing for mates. This produces a somewhat stable (and non-random) survival differential that allows real-world organisms to evolve. Hence, it’s really not necessary to have an goal for evolution to work. (Another way to look at it is to say that nature does have a goal, and that goal is to produce organisms that reproduce – of which survival is an important part.)

“Thank you for responding. Can you, for the benefit of the reader explain what would happen to this algorithm in the absence of
1. intelligent design of the selection process
2. intelligent design of the “creatures” such that they are amenable to intelligently designed selection”

GAs generally aren’t used to create ecologies of organisms that need to eat, hunt, evade predators, or compete for mates. In the absence of a goal or any of those needs, GAs won’t create anything at all because there’s nothing to create a survival differential (no goal, no competition, no starvation) – everybody survives, everybody reproduces (on average) at similar levels, and that means nobody evolves. You don’t need a goal to have GAs evolve, but you do need a survival differential (or more accurately a reproductive differential) in order to have evolution.

“Rather, this circuitous route serves the anti-design case by sneaking away the fine-tuning into the things you just listed: CPU, OS, GA engine, etc.”

The CPU and OS aren’t “sneaking” anything into the simulation. They are analogous to having a universe that works by laws. Being allowed to alter them is a little bit like saying that you should be allowed to alter fundamental forces of the universe and still have biological evolution work while you (for example) alter the binding properties of carbon, make hydrogen an unstable element, turn nitrogen into a noble gas, or increase or decrease the electromagnetic or gravitational forces by several magnitudes. While it would theoretically be possible to sneak things in via the GA engine, the engine is right there for everyone to see and scrutinize. It still works when nobody is pulling any sneaky business. The purpose of GAs isn’t to answer the question of whether the universe is designed or not, but it illuminates the question of whether GAs can, in the presence of stable fundamental laws, create CSI. Under those conditions, the answer is “yes”. Further, you can verify in the code that we are maintaining a relatively hands-off approach to the system and allowing a few simple rules (random mutation, selection, reproduction) to create our complexity. If there is a breach of this (for example, if we alter the organisms’ genome by inserting pre-designed information), you are entitled to complain, but there isn’t one.