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Author
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Topic: Defining Evolution
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warren_bergerson
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Member # 262
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posted 16. November 2002 08:05
The definition of evolution provides a useful basis for differentiating design science and evolutionary biology/genetics. As will be discussed, the definition of evolution provided by evolutionary biology is clearly incomplete and inadequate for rigorous scientific analysis. Design science, by contrast, offers a logically, mathematically precise definition that can be used for analyzing the whole range of evolutionary change processes.
THE GENETIC DEFINITION
Genetics and evolutionary biology currently define evolution in terms of changes in genes. The genetic definition of evolutionary change as genetic change does provide a precise basis for defining and measuring changes in genes. It is incomplete and inadequate as a scientific definition because:
1. It has failed to produce a predictive model or theory of the processes responsible for producing genetic change.
2. It has failed to produce a viable explanation/model of the processes by which a static genetic code translates into a life form.
3. It has failed to produce a viable explanation/model of the origin of genetic change.
4. It has failed to produce explanations/models of evolutionary changes in behavior.
Evolutionary biology using the genetic definition of evolutionary change has failed to produce any of the above models/theories/explanations. [ To be more precise, evolutionary biology has failed to produce models/explanations/theories of any the above items that would meet engineering standards. ]
THE DESIGN SCIENCE DEFINITION OF EVOLUTION
Design science (the form of engineering standards design science I advocate) provides a general definition of evolution based on the following concepts:
1. Definition of life form: A life form is a system with the ability to create and maintain complex sets of causal relationships which are teleological or purposeful.
2. Definition of a causal relationship with the teleological property: A causal relationship has the teleological property if 1)the relationship can exhit multiple forms, 2)some subset of the set of possible forms are associated with an increased likelihood or expectation of resulting in some goal, purpose or function, and 3)the current form of the causal relationship is a member of the purposeful subset.
3. Definition of self organizing principles- Self organizing principles are the processes which maintain the teleological property of the set of causal relationships defining a life form.
Starting with the above definitions, we can now define evolution as :
4. Design science definition of evolution: Evolution or an evolutionary process is a process which 1)changes the operation of a self organizing principle, 2)creates new causal relationships which may have the teleological property, and/or 3)expands the set of possible forms associated with causal relationships with the potential to have teleological forms.
Unlike the ‘genetic’ definition of evolution, the design science definition appears to 1)cover all forms of evolutionary change including evolutionary change in artificial systems, 2)make it possible/practical to develop mathematically precise definitions of the concept of evolutionary change, and 3)make it possible/practical to develop engineering standards models, theories, and/or explanations which satisfy all four criteria listed above as failures of the genetic definition of evolution.
SUMMARY
Design science and evolutionary biology are very different types of science with very different approaches to analysis of biological systems. The differences between the two types of science can be very clearly identified by looking at how they define and approach the issue of evolution. The definitions offered here provide a useful starting point for anyone interesting in discussing the differences between ‘engineering standards design science’ and ‘peer review standards evolutionary biology’.
[ 20. November 2002, 09:17: Message edited by: warren_bergerson ]
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Daniel Edington
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posted 16. November 2002 20:12
quote:
... Design science, by contrast, offers a logically, mathematically precise definition that can be used for analyzing the whole range of evolutionary change processes.
Really? Yet the simplest of questions go un-answered? Why is that?
Perhaps you would like to explain the definition of complex specified information, in plain English, and then tell me how much information (specified or otherwise) is contained in a typical protein (let’s say cytochrome c if we want a specific example.) How about my personal favorite, the snowflake? How much information is contained in a typical snowflake? I should probably be more specific here, by snowflake I refer to the star shaped dendritic snow crystals that everyone thinks of when one mentions the word snowflake (to be sure this isn’t the only type of ice crystal possible.) One would think that determining the amount of information in something like a snowflake would relatively easy for a theory that is defined with such mathematical precision.
Another question is: How much information can be created in a natural process?
Dan
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warren_bergerson
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posted 17. November 2002 10:38
Daniel,
Quote: Perhaps you would like to explain the definition of complex specified information, in plain English, and then tell me how much information (specified or otherwise) is contained in a typical protein (let’s say cytochrome c if we want a specific example.) How about my personal favorite, the snowflake?
To begin, the claim is that there are logically, mathematically precise definitions of information and information generating processes. The definitions may not be entirely understandable without some knowledge of set theoretic mathematics.
Second, design science defines the ‘volume’ of information in terms of the complexity of dynamic causal relationships with the teleological property. By the definitions used in design science, the volume of information in a system, or the volume of information in some part of a system at a point in time is defined as the N/Nf where N is the total number of possible forms a dynamic causal relationship can take and Nf is the number of teleological or purposeful forms. Both N and Nf are defined in terms of dynamic causal relationships where the system being analyzed has the capacity to create and maintain a teleological state.
If you are interested in the mechanical details of measuring information, the neuron provides a useful example. At any point in time, the algorithm controlling input-output processing of a neuron can take something like 2^3000th different forms. The number of these forms that are teleological, (i.e. contribute to or increase the likelihood of survival will be very much smaller). In looking systems the concern is not only with complexity, but also with the speed at which the teleological or adaptive form changes. In neurons, the teleological or adaptive form of information processing algorithm changes in milliseconds. I will be glad to discuss both the mechanics and mathematics of design science measures of information in more detail.
Using design science definitions, information content is defined and measured in terms of the processes that create a pattern or design, not in terms of the design produced by the process. Using design science definitions, the process producing snowflakes has an information content of 1 because while N, the set of possible forms is very large, all possible forms have the same survival value and thus the set Nf is the same size as N.
Using design science definitions, the information content of a protein would be measured in terms of the information content of the assembly process. This in turn would be defined in terms of the number and complexity of assembly instructions involved. I am no expert on proteins, but from what I have read, it appears scientists have enough understanding of at least some protein assembly processes to measure/estimate the information content of the assembly process.
Quote: Another question is: How much information can be created in a natural process?
To understand the design science answer to this question, it may be useful to consider a simple example. Assume for the moment that in order to survive and reproduce an organism has to perform a sequence of k operations where the complexity or information content of each operation is N1, N2, …. Nk. The ‘information content’ of this survival essential process is the product N1*N2*….*NK= NT. Since NT is the volume of information required for survival, it is reasonable to assume the organism created or extracted from the environment information with a volume of NT. NT, as we know can be a very, very large number. Biological systems can and do routinely generate huge volumes of information.
However, the creation of NT bits of information does not necessarily occur 1 bit at a time. An information content of 2^X may be the result of X relatively simple binary choices. A portion of the NT volume of information needed for survival may be stored. A system may have stored a ‘short cut’ process for finding all or some of the NT volume of information.
In answer to your question, it is easily demonstrated that biological systems generate or extract from the environment vast quantities of information. The volume or complexity of information generated, is not however, necessarily the same magnitude as the complexity of the processes which produce the information.
Quote: Really? Yet the simplest of questions go un-answered? Why is that?
I must admit I don’t entirely understand why there is so much confusion surrounding the issues of complexity, information, and information generation. The mathematics involved, IMO, is neither terribly difficult nor terribly unconventional.
My guess is that there are two basic sources for the confusion. First, I am guessing that most mathematicians have addressed the question of information from the perspective of the transfer of coded information, and have not looked at the issue from the perspective of either complex causation or information generation.
Second, and I think this is the real source of the problem, I am guessing that the analysis of information generation has been handicapped by the presence of ‘experts’ from fields like evolutionary biology and physics who have dictated how the issues ‘must’ be addressed.
I don’t mean to imply that the design science definitions, concepts and techniques are simple, but I do continue to claim that my version of design science offers mathematically precise definitions of both design processes and evolutionary processes. I also continue to claim that these definitions and the associated techniques provide answers to the types of questions you raise. The answers, however, are in mathematical terminology, not plain English.
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Daniel Edington
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posted 17. November 2002 12:53
quote:
I also continue to claim that these definitions and the associated techniques provide answers to the types of questions you raise. The answers, however, are in mathematical terminology, not plain English.
I don't think any theory that can only be expressed as math can be worth much. Why is it not possible to express these ideas in some qualitative way? Anyway I asked for answers, so give them to me. I’ll make a deal with you, if you provide the answers the questions I asked you can let me worry about understanding those answers.
quote:
By the definitions used in design science, the volume of information in a system, or the volume of information in some part of a system at a point in time is defined as the N/Nf where N is the total number of possible forms a dynamic causal relationship can take and Nf is the number of teleological or purposeful forms.
It is this way as opposed to Nf/N for what reason? Perhaps a real world application would make all this more clear. Can you apply this to cytochrome c (a very well studied protein)? How would this definition relate to the theoretical sequence space for a protein like cytochrome c? Perhaps it doesn’t, perhaps were swimming in uncharted waters here.
quote:
I will be glad to discuss both the mechanics and mathematics of design science measures of information in more detail.
Please do. Even if you were not glad to do it, you would still wind up being forced to.
quote:
In answer to your question, it is easily demonstrated that biological systems generate or extract from the environment vast quantities of information. The volume or complexity of information generated, is not however, necessarily the same magnitude as the complexity of the processes which produce the information.
Actually what I wanted to know is how much CSI can be generated not extracted from a more complex system. When I say generate I mean the generation of complexity from less complex systems.
quote:
Dan: Really? Yet the simplest of questions go un-answered? Why is that?
Actually I was wondering why ID proponents don’t seem to like answering questions.
Dan
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Evan
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posted 17. November 2002 13:57
Although this may seem like a small point, it may also move us closer to specific answers, which is something Daniel would like to see.
Warren writes,
quote:
At any point in time, the algorithm controlling input-output processing of a neuron can take something like 2^3000th different forms.
I would be interested in seeing a source or rationale for this number. Exactly what component parts of the neuron and what possible states are being considered in coming up with this figure?
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warren_bergerson
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posted 18. November 2002 10:18
Daniel,
Quote: It is this way as opposed to Nf/N for what reason? Perhaps a real world application would make all this more clear. Can you apply this to cytochrome c (a very well studied protein)? How would this definition relate to the theoretical sequence space for a protein like cytochrome c? Perhaps it doesn’t, perhaps were swimming in uncharted waters here.
We are not in uncharted waters, but we are into the subject of different uses and abuses of the mathematical concept of information.
To begin, it useful to note that ‘information’ is not a stand alone concept. Information is not an inherent property of a physical phenomena such as a snowflake, a protein or a DNA strand. In analyzing biological systems, information is defined and quantified in terms of complexity (N/Nf) or its inverse improbability (Nf/N). Defining and measuring information requires the ability to define and measure N and Nf. To be of general use in analyzing biological systems, it is important to define N and Nf so that they relate to the processes which generate information.
The genetic approach on which Dembski’s analysis appears to be based attempts to define information in terms of physical structures (DNA code and protein codes). They then assume these codes are generated by 1)a materialistic RM&NS process and/or 2)a non-materialist intelligent design process.
The design science approach is quite different. Rather than basing information on physical structures, design science defines information based on the teleological complexity/improbability of dynamic causal relationships. This definition/concept of information in biological systems can then be tied directly to the processes which create and modify information.
Looking at proteins will contribute nothing to understanding the differences between these two very different uses of information in analyzing biological systems.
Quote: Actually what I wanted to know is how much CSI can be generated not extracted from a more complex system. When I say generate I mean the generation of complexity from less complex systems
CSI is a term from the genetic approach not the design science approach. In design science terminology, the term information would mean the same as the term ‘CSI’. As I explained yesterday, biological systems are capable of generating or extracting vast quantities of information.
"Generating or extracting information" is a complex mathematical concept/process involving the interaction between a system and its environment( both part of an abstract mathematical universe). Your question does not seem to be based on the precise mathematical concept/process of information generation.
Quote WB: I will be glad to discuss both the mechanics and mathematics of design science measures of information in more detail.
Quote DE: Please do. Even if you were not glad to do it, you would still wind up being forced to.
To clarify my comments, modeling information and information generating processes in biological systems is a very complex subject and the design science approach involves some ideas and techniques which may be either new or unconventional. If any one is interested in seriously discussing this rather technical/mathematical subject in more detail, I will be glad to provide additional information on the design science approach to make such a discussion productive.
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Evan
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posted 18. November 2002 12:58
Warren writes,
quote:
Defining and measuring information requires the ability to define and measure N and Nf. To be of general use in analyzing biological systems, it is important to define N and Nf so that they relate to the processes which generate information.
(my emphasis.)
One question is: how do you measure N? In a previous post, Warren says that “at any point in time, the algorithm controlling input-output processing of a neuron can take something like 2^3000th different forms,” and I asked at the time: where did that number come from? What measurements and calculations produce that number?
Warren then writes,
quote:
To clarify my comments, modeling information and information generating processes in biological systems is a very complex subject and the design science approach involves some ideas and techniques which may be either new or unconventional. If any one is interested in seriously discussing this rather technical/mathematical subject in more detail, I will be glad to provide additional information on the design science approach to make such a discussion productive.
My response is the same as Daniel’s: Yes, please do provide additional information. In particular, both Daniel and I would like to see your ideas applied to a real biological situation. What would need to be measured, and how would those measurements be taken, in order to provide the numbers N and Nf for a particular biological item, state, or process?
Please be more specific both about the complex mathematics and the data to which the mathematics would be applied.
Thanks.
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Daniel Edington
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posted 18. November 2002 21:33
Daniel,
quote:
…To be of general use in analyzing biological systems, it is important to define N and Nf so that they relate to the processes which generate information…
Looking at proteins will contribute nothing to understanding the differences between these two very different uses of information in analyzing biological systems.
Right, so what are we saying here that a protein contains no information unless couched in terms of the processes that generated it?
Does the dictionary contain no information in the same manner?
I could give you my real opinion, but it would probably offend you.
quote:
As I explained yesterday, biological systems are capable of generating or extracting vast quantities of information.
And as I clarified yesterday, that didn’t answer my question.
quote:
Your question does not seem to be based on the precise mathematical concept/process of information generation.
You mean the same precise mathematical concepts/processes that you are not explaining? My question is simple, perhaps I should rephrase it. I am not asking how much information can be extracted (going from a source of greater complexity to one of lesser complexity.) I am asking how much information can be generated (going from a state of less complexity to one of greater complexity.) The answer should be simple, either the answer is none or there is some value (known or unknown.)
quote:
To clarify my comments, modeling information and information generating processes in biological systems is a very complex subject and the design science approach involves some ideas and techniques which may be either new or unconventional.
Unconventional, indeed!
quote:
If any one is interested in seriously discussing this rather technical/mathematical subject in more detail, I will be glad to provide additional information on the design science approach to make such a discussion productive.
Which is what I asked for in the first place. However, I am not so sure we are on the same wavelength when it come to what it means to be “interested in seriously discussing” and the definition of what it takes to “make such a discussion productive.”
It occurs to me that when it comes to intelligent design “theory” there seems to be much confusion and I wonder how much of this confusion is intentional. I place theory in quotes for a very special reason, because it seems to me that every ID proponent has her/his own pet theory and there is in fact no official ID theory. In fact Warren’s ideas here seem to quite different from the ideas of Dembski. It is no wonder outsiders can’t figure out what is what, the people inside can’t even agree on what intelligent design is.
Dan
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Evan
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posted 18. November 2002 22:52
Daniel, I sent you a PM (personal message). Check your profile to read it if interested. Thanks.
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warren_bergerson
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posted 19. November 2002 10:29
COMPLEX CAUSATION
The design science definition of evolution presented above is based on three key mathematical/logical concepts- 1)complex causation, 2)information generation, and 3)engineering solution sets. I will present for discussion the design science(my version) approach to each of these mathematical/logical issues starting with the issue of complex causation.
COMPLEX CAUSATION
It is widely, if not universally, accepted that life forms involve complex causation. From a mathematical perspective, the issue of complex causation comes down to questions such as 1)How are different types of complex causation defined in mathematics?, 2) What are the relationships among different types of complex causation? and 3)How can the complex causal relationships or processes associated with life forms be expressed?
It should be noted here that mathematics does not provide a single correct answer to the above questions. The issue here is not whether the design science approaches are the only correct answers, but whether the answers proposed are logically sound. [Eventually we need to address the issue of whether the proposed answers are useful, but the discussion here is limited to the issue of mathematical soundness.] As should be obvious, personal opinions of the form ‘I wouldn’t do it that way’ or ‘that is not the way X defines it’ are only indirectly relevant to the discussion.
HOW TO EXPRESS COMPLEX CAUSAL RELATIONSHIPS
Design science uses a complex type of algorithm called a ‘programmable logic machine’ to represent the complex causal relationships associated with biological entities. In other words, design science asserts that a biological entities can be viewed and modeled as programmable logic machines or computers.
The idea or concept that a life forms is, or can be viewed as, a computer or logic machine is not, of course, unique to design science. The following are the some of the key features the design science approach:
1. LOGIC MACHINE AS COMPLEX CAUSAL PROCESS- Causation is a relationship of the type ‘cause produces effect’. A logic machine has the same logical structure ‘input produce output’ or ‘stimuli produce responses’. The logic machine makes it possible to express or represent very complex causal relationships.
2. THE COMPONENTS OF A LOGIC MACHINE- Defining a logic machine involves defining three types of components- 1)the programs controlling the logic machine, 2)the input-out processes defining the interactions between the logic machine (biological entity) and its external environment and 3)external environment.
Two features of the logic machines used in design science are important to note. First, the external environment with which these logic machines interact is an ‘abstract mathematical external environment’ not the ‘real world’. The logic machines being considered here are ‘open or interactive sub-systems’ in a larger abstract mathematical space. Second, defining the logic machines does not require precisely defining the external environment. The logic machines used are defined as having limited knowledge of the external environment. Specifically, the logic machines are defined to operate without knowledge of future events, and without the ability to know if future events are predictable/deterministic.
3. THE STRUCTURE OF THE LOGIC MACHINE PROGRAM- The logic machines used in design science involve at least two ‘levels’ of processing. On the one level, the programs receive input st at time t, and based on the processing function or algorithm ft, the program generates output rt as ft(st)=rt.
On a second level, processing in a logic machine produces changes in the algorithm ft. Specifically a programmable, or self programmable, logic machine involves some process or algorithm G such that G(ft, input) =ft+1. It is sometimes useful to visualize a logic machine with addition levels such as a level where H(Gt, input)=Gt+1. In other words, a programmable logic machine can have the ability to reprogram the programs that produce reprogramming. [For most forms of analysis, it is only necessary to consider two levels of complexity. ]
Although some of the terminology used here may be new, but the basic concept of using programmable logic machines to model complex causation is not new and therefore the legitimacy of the technique is well established.
DIFFERENT TYPES OF CAUSAL RELATIONSHIPS
It is possible to create, model or simulate many different types of complex causal relationships using programmable logic machines. The following three types of causal relationships or processes are of particular interest in modeling and analyzing life forms:
1. DYNAMIC OR PROGRAMMABLE CAUSAL RELATIONSHIP- This is the type of input/output or causal relationship where ‘A causes B’ can be changed or reprogrammed to "A causes C’. The mechanics of modeling this type of causal relationship should be obvious.
2. TELEOLOGICAL CAUSAL RELATIONSHIP- In common usage, teleological causation is a relationship of the form "A causes B results in increased expectation of goal or purpose P’ or "ft(A)=B results in increased expectation of P’. As defined here, ‘ft(A)=B’ is defined as purposeful or teleological if a) ‘ft is a member of some set F of possible functions with N members, and b)ft is a member of a subset Fpt of F which involves increased expectation of achieving P and contain Nf members. Note that simulating a teleological causal relationship is the same as simulating any dynamic causal relationship. The ‘difficult’ issue is finding and maintaining ft as a member of Fpt.
3. SELF ORGANIZING PRINCIPLES- As defined here, these are causal processes which change dynamic causal relationships into purposeful or teleological causal relationships. These are the causal relationships or processes which generate information or extract information from the external environment. I will defer discussion of modeling or simulating this type of causal relationship until I discuss the topic of generating information. As should be apparent, a self organizing principle is a type of reprogramming process.
It is again worth noting that discussion here relates to mathematical models of causation.
DERIVATION OF DIFFERENT TYPES OF CAUSATION
All the types of complex causation defined and modeled here are defined in terms of, derived from, or constructed from the basic permanent and universal causal relationship. I believe that all the causal relationships considered here are modeled or represented in a form that is consistent with standard materialistic/deterministic causation. I will be glad to discuss these derivations off line with anyone with a working knowledge of set theoretic math.
The fact that complex causal relationships such as teleological causation and self organizing principles can, apparently, be defined, modeled, and simulated as standard materialistic/deterministic processes is an important finding for ID. Many proponents of ID believe either that modeling design processes requires new types of causal relationships (Chris Langan) or non-materialistic/deterministic processes. The analysis here clearly suggests that scientific analysis of design and design processes does not require anything beyond the standard scientific materialistic determinism.
SUMMARY
Enough for one day. I will address the issue of generating information next. I repeat again that the above discussion relates entirely to mathematics, not to how mathematics is used in scientific analysis. The issue for discussion here is whether the mathematical concepts defined above are logically sound.
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warren_bergerson
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posted 19. November 2002 10:45
Dan,
Quote: Right, so what are we saying here that a protein contains no information unless couched in terms of the processes that generated it? Does the dictionary contain no information in the same manner?
Do you think the symbols in a dictionary contain information except in the context of human usage? Sequences of symbols or DNA strings only contain ‘information’ in context.
Information in biological systems is defined in the context of teleological or functional improbability or complexity. That is the way information is defined in design science. I believe it is also the same concept Dembski uses although he would have to speak for himself. Mathematicians define information in terms of symbol improbability or complexity.
If you have an alternative concept/definition of information you are welcome to present it.
Quote: It occurs to me that when it comes to intelligent design "theory" there seems to be much confusion and I wonder how much of this confusion is intentional. I place theory in quotes for a very special reason, because it seems to me that every ID proponent has her/his own pet theory and there is in fact no official ID theory.
The issues of information and information generation are mathematical issues not scientific. Different people may pursue different aspects of mathematics and may apply mathematics differently, but the validity of all mathematical claims are evaluated on the same objective, logical standards. Mathematical validity is not determined by the personal subjective opinions of a individuals who claim to be experts on a subject. The subjective peer review standard is not applicable in mathematics.
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Evan
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posted 19. November 2002 11:08
Hi Warren.
Three questions.
1) How is N measured? What measurable facts about an entity such as a protein would we need to know to compute N?
2) Where does the number 2 ^ 3000 come from as an estimate of the number of possible states of a neuron?
3) Can you provide a separate, short answer to these questions that actually discusses the biology involved but that doesn't involve writing a complete essay.
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Daniel Edington
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posted 19. November 2002 20:33
Warren,
quote:
Information in biological systems is defined in the context of teleological or functional improbability or complexity. That is the way information is defined in design science. I believe it is also the same concept Dembski uses although he would have to speak for himself. Mathematicians define information in terms of symbol improbability or complexity.
You have still managed to evade my question: Does a protein, such as cytochrome c, contain any information and if so how much? You have stated that to answer such a question won’t get us anywhere useful, I disagree.
We could ask a simpler question: for a protein sequence N amino acids long, the theoretical sequence space is 20^N, of those what fractions would fold to a compact globular shape and posses an active site for carrying out some sort of chemical reaction? Would the answer to such a question relate in any way to the “functional improbability” of a globular protein?
If we found an alien dictionary, written in a language humans couldn’t decipher, would it contain information in the context of human usage? Would it contain any information at all?
quote:
The subjective peer review standard is not applicable in mathematics.
The peer review standard is subjective? I’m sure mathematicians would be surprised to hear that peer review is not applicable in their field.
quote:
The issue for discussion here is whether the mathematical concepts defined above are logically sound.
I however am not really interested “whether the mathematical concepts defined above are logically sound.” It is possible for the math to be both correct and logical and still not apply to reality. In order to ascertain whether or not this design theory is of any merit it must make specific claims that can be tested.
Dan
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