Uncommon Descent Serving The Intelligent Design Community

Tautologies and Theatrics (part 2): Dave Thomas’s Panda Food


(this also servers as a partial response to a formal request for a response fielded by the UDer’s mortal enemies, the Pandas, specifically Dave Thomas in Take the Design Challenge!)

This is part 2 a of discussion of evolutionary algorithms. In (part 1): adventures in Avida, I exposed the fallacious, misleading, and over-inflated claims of a Darwinist research program called Avida. Avida promoters claim they refuted Behe’s notion of irreducible complexity (IC) with their Avida computer simulation. I discussed why that wasn’t the case. In addition, I pointed out Avida had some quirks that allowed high doses of radiation to spontaneously generate and resurrect life. Avida promoters like Lenski, Pennock, Adami were too modest to report these fabulous qualities [note: sarcasm] about their make-believe Avidian creatures in the make-believe world of Avida. One could suppose they refrained from reporting these embarrassing facts about their work because it would have drawn the ridicule the project duly deserves from the scientific community.

In contrast to the spectacular computation theatrics of Avida, Dave Thomas of Pandas Thumb put together a far less entertaining but no less disingenuous “proof” of the effectiveness of Darwinian evolution. Every now and then, the Panda faithful need some food to help them sustain their delusions about naturalistic evolution. This food I call Panda food, and chef Dave Thomas cooked up a pretty nice recipe to feed delusions to the faithful Pandas at our rival weblog. Perhaps if Dave Thomas refines his Panda food recipes, he should consider opening a restaurant chain, and maybe he should call it Panda’s.

To introduce what is at stake, I first introduce the idea of known explicit targets and unknown but desired targets. An explicit known target is a target which we clearly can see, describe, and precisely locate. Examples of such a target would be the bulls-eye an archer aims for.

We can build machines to help us hit such explicit targets. A good example of such an intelligently designed explicit-target hunter is the Infra-red Maverick Missile.

IR Maverick

When on a mission to destroy something like a tank, the aircrew tasked to fly the mission, locates the explicit target (i.e. a tank), and then describes the target to the missile through the process of designation (the designation process is analogous to a point-an-click on the aircrews video screen). Upon launch, the missile employs a feedback and control strategy very akin to classical control theory to home its way to the target.

But those are examples hitting explicit targets. What about unknown, but desired targets? Let me call such targets “targets of opportunity”. A target of opportunity would be the kind of target we know inexplicitly, but still seek after. A good example of such a target of opportunity would be deer in forest during a deer hunting season. Hunters have a general strategy for tracking and hunting the deer, but they don’t know in advance what their target will be (be it Bambi or Bambi’s mother, for example). We don’t know what kind of game we may or may not bag, just that we have a general idea of what we’re striving after.

Does the military have human/machine systems with “target of opportunity” capability? Ahem. Even if I did know of such things, I’d have to deny the existence of such missiles like SLAM-ER Target-of-Opportunity Missile.


In the field of engineering and human endeavors, many of the solutions can be thought of like the process of hunting down targets of opportunity. Sometimes we are confronted with a problem, we have strategy we know in advance will yield a solution even before we explicitly know what the solution is.

A VERY simple case in point. Take the integers from 1 to 1000. The following question is posed to us, “what is the sum of these integers, 1 to 1000?” Do we have to know in advance what the answer is? Maybe, maybe not. I’ll cheat and give you the answer. It’s 500,500.

The important point is, that even if you did not know the answer (the target of opportunity) in advance, you have well-proven strategies to find and hit the target. One such strategy would be to sit down with a calculator or spread sheet and add the numbers form 1 to 1000. Another would be to write a computer program which added them together. Yet another would be to write a genetic algorithm to find the answer. I’ll provide several such examples a the end of this essay for you computer geeks out there! But the most important thing in hitting such a target of opportunity is that by intelligently designing the right strategy, one can hit a target of opportunity without the target being explicitly described. Get the picture?

Adding of numbers is a very primitive example of hunting down a target of opportunity. A far more sophisticated example, is finding the optimal design of a computer chip given certain constraints. The space of possibilities is extremely large, but engineers can program genetic algorithms (much like they build sophisticated calculators) to hunt down solutions on their behalf.

Back to the Pandas challenge of me. To build their case, anti-IDers will often need to equivocate and obfuscate the issues. Clarity is their enemy, confusion is their friend. Such was the recent offering by Dave Thomas of Pandas in a long, tedious essay, Target? TARGET? We don’t need no stinkin’ Target!.

He shows how a genetic algorithm can hunt down a target of opportunity. But as I hope I’ve shown, such a thing is unremarkable! However, he hints his program demonstrates mindless forces can find such targets without intelligent design.

Dave employs equivocation and Orwellian Double Speak to argue his case. He takes a designed selection strategy and tries to pass it off as an example of mindless undesigned forces which can magically converge on a target of opportunity. How does he promote his theatrical gimmick? Read what he says, and then read the challenge he poses to IDers:

Genetic Algorithms are simplified simulations of evolution that often produce surprising and useful answers in their own right. Creationists and Intelligent Design proponents often criticize such algorithms for not generating true novelty, and claim that these mathematical recipes always sneak the “answer” into the program via the algorithm’s fitness testing functions.

There’s a little problem with this claim, however. While some Genetic Algorithms, such as Richard Dawkin’s “Weasel” simulation, or the “Hello World” genetic algorithm discussed a few days ago on the Thumb, indeed include a precise description of the intended “Target” during “fitness testing” on of the numerical organisms being bred by the programmer, such precise specifications are normally only used for tutorial demonstrations rather than generation of true novelty

I have placed the complete listing of the Genetic Algorithm that generated the numerous MacGyvers and the Steiner solution, at the NMSR site.

If you contend that this algorithm works only by sneaking in the answer (the Steiner shape) into the fitness test, please identify the precise code snippet where this frontloading is being performed.

Thomas sneaks the answer in by intelligently designing a strategy which will find the target of opportunity. This sort of gimmickry is not much beyond the following illustration:

One kid goes up to another with a paint ball gun and shoots him, and says,

“Don’t get mad, I wasn’t aiming at you, I was aiming at the shirt you were wearing.”

bulls eye shirt

By giving the computer the correct strategy (like a method of adding numbers) one guarantees the answer (or target) will be hit, or at least a near miss. There are numerous strategies which will succeed, but they still must be intelligently designed. For the less technically minded readers, I hope what I’ve written so far gives a narrative explanation of what’s really going on.

To get an idea of how easy it would be to give the wrong search strategy, consider a long sequence of driving directions. If even one occurence of the word “left” is substitutted for “right” or vice versa, the directions will fail. Without intelligence programming the selection strategy, the target would have missed in Dave’s program. However, Dave Thomas used intelligence to ensure a miss wouldn’t happen, or at least, less likely. He thus snuck the answer in after all, contrary to his denials.

In the post script, for the benefit of the technically minded readers, I’ll address the more technical details to help put all of Dave’s nonsense to rest.

Salvador Cordova



Dave’s Challenge:

If you contend that this algorithm works only by sneaking in the answer (the Steiner shape) into the fitness test, please identify the precise code snippet where this frontloading is being performed.

I’ll identify it plain and simple, and call his bluff. The major front loading is in how selection is made. With the wrong selection description, the wrong target of opportunity, if any, will be hit. Simple!

Dave counts on a bit of obfuscation to make his work unreadable. He chooses an antiquated computer language known as FORTRAN to make his demands. “Lets invite UD software engineers to read my hieroglyphics and invite them to show where I sneaked the answer in!” Sheesh.

That said, I will identify an important part of his barely readable code, which, if removed will cause the genetic algorithm to miss the target. The fact that this section is essentially irreducibly complex is testament that intelligent design was needed to enable the genetic algorithm to do its thing.

If any section is even slightly re-written in a mindless way, the program likely misses the target at best and fails to even functionally compile at worst. I’m sorry the following link will look like hieroglyphics to some, but of necessity, I need to show it to call Dave’s bluff with it. Here is one of the many places where Dave sneaks the answer in:

Dave Thomas’s Code Bluff

Does Dave Thomas doubt me that I’ve identified where he snuck the answer in? How about we allow 5 random changes to the code segment I pointed to? Does he think such mindless modification can be introduced and the algorithm will still function? Do we think the GA will successfully hit the target (assuming the GA can even run) in the midst of 5 measly random changes? Will Dave run away from the fact that the above selection strategy needs intelligent design? Or will he represent that the above code segment came to be of its own accord, and that the selection strategy described by the above code is the product of blind mindless processes?? Will he continue to insist what he did is not sneaking the answer in?

The selection strategy in his program is anything but natural. Just because the terms Darwinian and selection are used in the argument does not mean intelligent agency is not permeating the entire project. Such labelings are doublespeak. If I went through and re-labeled everything intelligently designed selection vs. natural selection, you’d get the real gist of what’s happening!.

All right, as I promised, I’ll now present several ways to add the numbers 1 to 1000 and get the answer 500500. With the exception of the first program, in each case the target answer will not be an explicitly stated target, but rather a target of opportunity which is hit via an intelligently designed hunting strategy.

The sample programs are written in the C language.

This program will give the explicit answer to question, “What is the sum of the numbers from 1 to 1000?” :


This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?” through a brute force computation which involved adding all the numbers from 1 to 1000:


This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?” through Gauss’s method of mathematical induction:


This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?” through recursive addition of all the numbers from 1 to 1000 :


This program will give the answer to question, “What is the sum of the numbers from 1 to 1000?”
through a genetic algorithm. The algorithm pairs up numbers form 1 to 1000. Rather than compute the midpoint via a simple calculation it takes a random number as a starting point and then mutates the random number and uses a fitness function to select between the mutant and the original number to give the current best midpoint estimate. The process is repeated with increasing refinement. 2 times the sum of the midpoints then becomes the sum we are seeking. Snapshots of the algorithm’s progress are given along the way. The following computational theatrics are akin to what Dave Thomas performed:


I and my co-workers (while I was in school in the 90’s) worked on target recognition systems and simulations of missile guidance systems. Dave can feed the biologists at Pandas Thumb his Panda food, but half the UDers here have relevant engineering backgrounds to see through the charade. He could not have picked a worse thing to do than challenge the UDers to disprove the flimsy claims of his intelligently designed program.

[...] Thomas is in a bit of a tizzy over my humble offering: Tautologies and Theatrics (part 2): Dave Thomas’ Panda Food. He responds at Pandas Thumb with: Calling ID’s Bluff, Calling ID’s Bluff. I thought [...] Dave Thomas says, “Cordova’s algorithm is remarkable” | Uncommon Descent
Caligula, 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 scordova
Salvador: Just one additional comment, if you please. Although exact fitness calculations for wild populations over evolutionary time are complex beyond imagination, in Dave's simplified simulation (with only one selection factor) the fitness difference between any two organisms is quite trivial to calculate. I'm sure that Dave would be happy to comply if challenged. 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. caligula
Salvador: I think biologists readily admit that there is no consensus on mechanisms of evolution, or at least on their relative importance. This is largely due to problems with measuring selective pressures. It is of limited use to be able to measure the *total* fitness of an organism, if we can't *calculate* this fitness from theory, i.e. if we can't break the measured total fitness into more interesting subcomponents (pressures due to sexual selection etc.) Certainly, this *is* a major problem for biology. 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! caligula
Caligula, 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 scordova
Salvador: I read Lewontin's paper. I think he's saying that calculating the total fitness difference between organisms is a very *complex* affair. On the other hand, he is *not* claiming that (a) selection is random, (b) fitness differences did not exists, (c) fitness differences were not, in evolutionary timescale, responsible of complexity (i.e. CSI) in organisms. Yes, this paper can be seen as criticism on various hypotheses from the ultra-adaptationist camp. In Lewontin's opinion, many theorists simplify things way too much, (a) by isolating single selection factors from the whole picture and (b) by not taking into account that things like changes in the population size can affect selection factors. He's not necessaruily saying that ultra-adaptationist theories were *wrong*; he's claiming that their mathematical treatment is currently over-simplified. But this paper hardly helps *your* case here, because Lewontin is not giving any support to your position that natural selection is *chaotic*. Complex just isn't a synonym for chaotic. 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
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). scordova
scordova: "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?" 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! caligula
(here is a response I posted to at Pandas Thumb)
[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 scordova
Here's the problems I've seen with the arguments:
"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. BC
"ID advocates….are saying that the solution is already implicitly defined in the statement of the problem" "By the way steveh, do you think that Dave Thomas has misrepresented my position as I have pointed out above?" I don't know why you are asking me specifically, but I would answer "no". As far as I can tell, you have never used those words exactly; The way to the goal is not specified by the original statement - the standard claim is the solution is implicitly defined in the design of the fitness function. However, the fitness function usually mirrors the original statement in some way, so the claims are arguably equivalent - in cases where the fitness function isn't modelled on the original statement you would accuse us of sneaking in new information which leads to the solution by a back door. In other words, the original statement specifies a goal, the fitness function provides a metric of how close a potential solution is to that goal. The FF follows from the statement, not from advanced knowledge of what the solution is. For example, here the problem is to "find the (total length of the) shortest network which connects a given set of points" and the corresponding fitness function returns a score which is high if the total length is small and the points are connected. You could, maybe, restate the original problem as: "Find the network which has the minimum S, where S is the total length of the network + (L, a large amount if any point is not connected to it, or zero otherwise). Any design is in the mapping of a fake genome to a potential solution, but that design doesn't indicate how to get to a good solution. Yes, I used the word "design". One can design something to mimic an undesigned process, so don't get too excited about that. steveh
caligula wrote: I have not read Dembski’s books. Although The Design Inference has been even been translated to Finnish, I haven’t seen any of Dembski’s books at my local library
I appreciate your response. I was not trying to be provocative, but its been my experience that most of the criticisism agains CSI are misrepresentations of what CSI actually is. One of the most common come from Perahk, Elsberry, and Dembski's former teacher Shallit. Part 3 of 3 of this series will deal with Elsberry and Shallit's misrepresentations of CSI. For CSI to be defined one needs: 1. Space of possible outcomes 2. specification of a target within those possible outcomes 3. actual event that coincides with the target It is notable that Shallit's GA for travelling salesman didn't frame his refuation in terms of the CSI formailities, but equivocated the definition of "bit"! For example an Mp3 or JPEG, or better yet a ZIP file have a certain number of bits associated with them. They can decompress into a larger file. Is that a violation of conservation of CSI? When talking about CSI, the conception of "bit" in the compressed state is not appropriate to the conception of "bit" in the decompressed state, even though from a computer storage standpoint, the conception of bits is the same. However from the standpoint of CSI the conception of bit in each case is NOT the same. Thank you for visiting and responding to my questions. Given that you don't have access to Dembski's books, I will try to respond to you comments in light of those facts. I hope to have more comments later. In the mean time, you may want to familiarize yourself with this paper: Specification The Pattern that Signifies Intelligence Salvador scordova
(I notified Dave Thomas with the following at Pandas Thumb)
Dave Thomas wrote: ID advocates, since y’all are saying that the solution is already implicitly defined in the statement of the problem
By the way Dave, that does not represent mine or any IDers position that I know of. I can understand perhaps how you may have come to that conclusion. You might even be tempted to fault my infelicitous expression of ideas for your horrid misunderstandings and misrepresentations and mischaracterizations of what IDers believe. I presume the last thing you would blame this mischaracterization on would be the Panda's propensity to uncharitably characterize what IDers say....Fine! The point is, what you said in your opening post does not accurately represent what I or other IDers believe. I hope you'll post an addendum somewhere on this site conveying the fact to the readers that what you said does not represent my position. If you have to sugar coat it by arguing that you were misled because you couldn't decode what I was claiming, fine. But I request you withdraw your mischaracterization of what I believe. Salvador scordova
Salvador: A correction. You apparently are not denying Thomas has produced CSI. You are simply claiming that this CSI was brought in by ID which is somehow hidden in the fitness function. My point remains, though. How much intelligence it takes to know that conservation of energy is "beneficial"? That kind of simple rule -- as simple as the one governing the formation of atoms, snowflakes, etc. -- will produce CSI similar to what Thomas' GA produced in zillions of *different* applications. Just *how* generic does the fitness rule have to be in order not to sneak in ID? caligula
Good (late) morning Sal, I believe I am familiar with most of Dembski's central claims (complex specified information; CSI only produced by intelligence; sometimes: information *in general* only produced by intelligence; explanatory filter; No Free Lunch, essentially claiming that selection does not work). I have not read Dembski's books. Although The Design Inference has been even been translated to Finnish, I haven't seen any of Dembski's books at my local library. (I have borrowed and read most of the YEC books available, as well as one originally German ID/baraminology book called (in Finnish!) "Evolution - Critical Analysis" (Scherer, Junkers; Finn. trans. Matti Leisola)). I have read some of Dembski's free PDF publications. Would I like to read the books? Sure. However, I have a principle not to *buy* any creationist or ID publications; a principle I hope is acceptable as it does not mean unwillingness to become familiar with their arguments. How do I define CSI? I think Dawkins gave the definition in 1986 (Blind Watchmaker), except he called CSI just "complexity", and made it clear that "complexity" involdes information specified in advance. Practically anything in the physical world can be interpreted as "information". (However, we have to bear in mind that there is no *universal* interpretation which automatically maps any physical object or phenomenon to bits!) Complexity is something too improbable to originate by chance alone, provided that the complexity is specified in advance. As I understand it, you, Salvador, are now trying to claim about the CSI produced by Thomas: - information produced by a necessity such as a natural law is not CSI - Thomas' information was produced by a "hidden" necessity (the fitness function *statistically* limits freedom in such a fashion as to gradually evolve MacGyvers) Well, it sure does. But so does natural selection. So does *all* cumulative selection. It exponentially (albeit often only statistically) limits freedom in any search space, coming up with objects that look designed, and it does so amazingly fast compared to a mere chance hypothesis. If this appearance of design produced by cumulative selection does not count as CSI, then I wonder what does. You may insist than similar appearance of design in *nature* is, alone, CSI. Fine. But then please don't extend your claims to computer science. Anyway, you probably had more questions than the definition of CSI. Feel free to "interrogate". :-) caligula
Tom, you are a good man. Thanks to you and Salvador for a fantastic discussion. I, for one, appreciate it. Barrett1
Here is my correction A = (86.6025, 150) B = (313.3975, 150) C = Fermat Point joining vertex 5,6,3 The solution is therefore assymetric, contrary to my earlier speculation. There are other converse solutions. At least that is what I think. I have not looked too deeply into Steiner trees before last night. That is my best speculation so far. Assuming Dave Thomas used naming conventions in his Fotran program that accurately reflect what was there, I used that to identify the appropriate code snippet. Salvador scordova
Salvador, "I found a 6 vertex steiner solution with only 3 points." The MAX number of Steiner points is 4 in this case. Tom English
I may need to make a slight correction in light of this: Euclidean Steiner Tree I found a 6 vertex steiner solution with only 3 points. Salvador scordova
Tom, I will continue to review you points. Both Bill and others liked what your wrote at ARN and even here. The reason is, your technical criticisms are worlds above the misrepresentations I'm used to seeing. I mean, you actually give legitimate things to consider versus people like...well...I'll save the names for another time. :-) Let me think on what you said about the displacement theorem. regards, Salvador scordova
Scott @14: " Everyone, please see: https://uncommondescent.com/index.php/archives/802 " Everyone, also please see the follow-up, https://uncommondescent.com/index.php/archives/907, "C’est la Avida," which includes a discussion of Eric Anderson's peculiar use of term "cumulative complexity" -- the source of substantial confusion in the original thread's comments. —————————— If someone uses the Newton-Raphson method to solve a system of nonlinear equations, and the solution requires a precision of better than 1 part in 10^150 to be fully specified, does this demonstrate that intelligence isn't required to produce CSI? (Assume that the system models some physical problem.) j
Sorry Scordova, there’s a lot of “above” and I dangerously past my bedtime. Let me know specifically what’s being misrepresented and I’ll get back to you tomorrow night (Europe time).
Well, thank you for visiting. The issue in question was Dave Thomas's asseriton:
ID advocates....are saying that the solution is already implicitly defined in the statement of the problem
Sorry Scordova, there's a lot of "above" and I dangerously past my bedtime. Let me know specifically what's being misrepresented and I'll get back to you tomorrow night (Europe time). steveh
By the way steveh, do you think that Dave Thomas has misrepresented my position as I have pointed out above? scordova
steveh, I don't have a FORTRAN compiler, besides, that's only a code snippet, it wouldn't work anyway. :-) scordova
Hmmm, why not just use the code here to get an instant solution: http://smartaxes.com/docs/ud/tautologies/bluff.txt ? steveh
ofro wrote: What I don’t understand is the basic premise of your example, which apparently already has an explicit solution of the problem built into the program.
I'm afraid that isn't quite correct because if you go to ga.c, and do a text search for 500500 you won't find it. The solution was never explicitly stored anywhere. I appreciate your participation however. Salvador scordova
steveh wrote: If I understand your solution correctly, one of 5c or 2c is unecessary because 5 & 2 are already connected to the network by the other half of the solution. I imagine that would change the result
[update: see below for my revised guess] Salvador scordova
If I understand your solution correctly, one of 5c or 2c is unecessary because 5 & 2 are already connected to the network by the other half of the solution. I imagine that would change the result. steveh
Something I should point out which Dave Thomas said:
but am especially interested in solutions by ID advocates, since y’all are saying that the solution is already implicitly defined in the statement of the problem
That is not my position nor that of any IDer I know. That would be very bad misrepresentation on his part regarding what was the intent my description of his work. He may have over extrapolated my story about the kid with a paint ball gun, but my little parable does not imply as a general principle that the solution is already implicitly defined in the statement of the problem. The solution is implicitly implied if: 1. a solution strategy exists to solve the problem 2. the solution strategy is implemented I can understand perhaps Thomas erring once to say, "the solution is already implicitly defined in the statement of the problem", however if he maintains that, I will have to protest that he is making a flagrant misrepresentation of my position and that of other IDers. Salvador scordova
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