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Author
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Topic: The Characterization of Intelligent Causation
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IF
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Member # 1904
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posted 19. July 2007 10:02
LE,
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Conflicts of this general type are quite common, but they are most commonly framed in terms of an ‘applied science’ versus ‘theoretical science’ conflict. In general, most people involved in these conflicts recognize that if they directly challenge academic science they will loose and they will not be able to find resources or support to continue the challenge. One approach that sometimes allows the hard science position to succeed is developing a marketable product and taking the product directly to the marketplace.
'applied science' = Engineering? Engineering (http://dictionary.reference.com/browse/Engineering) = the art or science of making practical application of the knowledge of pure sciences, as physics or chemistry, as in the construction of engines, bridges, buildings, mines, ships, and chemical plants? Does pure science = hard science in your view?
Conflicts in how best to resolve problems is the most wonderful activity of humans. Foraging for good ideas is the first step in their attack. Determining the best idea of those collected is time consuming and, depending on the time available to sift through them all, difficult. The point is that this process, i.e. foraging, gathering, sifting, determining the best of the bunch, should never stop.
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There are lots of examples from the field of applied systems design versus theoretical AI. Theoretical AI, it will be noted, continually argues that intelligence is indefinable and beyond our ability to simulate on computers. Systems designers, in contradiction to the academic science position, routinely develop systems to successfully simulate all sorts of intelligent behavior.
That, as I mentioned, is the foraging process in action! Good foraging, (like searching for gold) is more of an art than a science. However, the more foragers the better.
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One of the more interesting current conflicts involves the issue of global warming. While academic science and hard science are currently in agreement on the existence of a potential climate change problem, the two conflicting approaches produce very different types of solutions and the two approaches suggest very different uses of available resources.
That is the key issue, i.e. available resources, i.e. financial, data, time, personnel, etc. Being careful to not be wasteful and who determines where the resources go, how they are used, etc. is the "political" aspect and the inevitable basis of these kinds of conflicts.
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Again, you ask a very interesting question. Conflicts between hard science (or hard science wit teleological theories) and academic science are quite common but are usually not described or framed in terms of a direct conflict between two competing forms of scientific analysis. In general, hard science wins a fair number of these contests when we are dealing with engineering and physical science issues, but hard science wins far fewer contests when dealing with life sciences and particularly when dealing with behaviors viewed as involving human intelligence.
That whole nasty process may it never stop! We all win in the long run but only in the long run!
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IF
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posted 19. July 2007 15:11
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WINNING AND SCIENTIFIC ANALYSIS
It is probably easier to understand, and possibly even resolve, the issues of teleological causation and intelligent causation if we recognize the difference.
From what I understand, the word teleological invokes the concepts associated with supernatural goals in nature. That being said, I don't think a group will have any problems if they all agree to use a more purely natural definition. As long as they all realize the distinction there are no problems in this particular regard.
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... But the available evidence suggests that highly efficient and highly effective human social structures are relatively rare and highly unstable. Productive social structures are subject to relatively rapid corruption and loss of productivity.
Agree, especially when groups get larger or too many disagree on the definitions and concepts involved with the jargon/terminology being used.
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Human social structures deteriorate or become corrupted when individuals who lack the required skills get into key positions. For example, human social structures are known to deteriorate when people with scientific skills get into positions requiring political skills and people with political skills get into positions requiring scientific skills. The points that are relevant to the discussion here are that 1) there is ample available evidence for the existence of differences between scientific and political skills and 2) there is ample available evidence for the negative impact of the wrong skills in the wrong position.
All of that is purely and wonderfully human. As long as no one is prevented from pursuing what they think is a better path to the solution. However, draining the resources from the larger group to what appears to them as wasteful or unnecessary is where the 'political' decisions must be made. Many times when this happens group split off from one another to pursue the matter "their way". Sometimes at a later date they rejoin to the betterment of all.
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The question that is controversial and that has been controversial for a very long time is the question of what methodology or skills SHOULD be used in making the decisions. The problem, or perceived problem, is that if you if recognize and permit intelligence based teleological theories in scientific analysis, then every human decision whether political or religious or scientific, becomes subject to analysis by the scientific method. If purposeful intelligent causation, which includes human behavior, is recognized as subject to formal scientific analysis, then every decision made by a human being becomes subject to formal scientific analysis.
I agree completely and that is exactly what is being done today! The only dispute is the definition/understanding of the phrase "purposeful intelligent causation"! In science today it is a natural process not supernatural.
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This presents a direct challenge to the ultimate authority and ultimate decision making currently recognized as the belonging to those using political skills.
With my comments above in mind, I agree!
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LifeEngineer
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posted 20. July 2007 07:58
IF,
You wanted to see examples of the conflict between hard science and academic science and now you are attempting to misrepresent the conflict.
Recall that the hard science process or paradigm involves formulating, testing, and refining predictive theories. You can look forever at the life science side of academic science and you are unlikely to ever find a non-trivial predictive theory that satisfies hard science requirements. If you don’t have real theories it is obviously not possible to have real testing and real improvements in theories.
But look closely at any successful example of applied science and you will find numerous valid examples of open and objective testing and you will find the results of this testing being used to refine predictive theories. Look closely at the ‘theories’ being tested in successful applied science of any form and you will find goal variables and teleological theories.
As far as can be determined, all successful applied science analysis, this includes engineering and systems design and a variety of other applied science fields, is based on the use of hard science methodologies and on the use of teleological theories.
This is in direct contrast to essentially all academic science that rejects teleological theories and generally rejects evaluations based on open and objective testing.
The differences between ‘successful applied science based on teleological theories and hard science standards’ and ‘academic science that rejects teleological theories and is not based on hard science methods and standards’ are both dramatic and readily demonstrable. These differences lead to numerous conflicts including those I described yesterday.
Relate this back to the issue of intelligent causation being discussed here. The academic science position as expounded by aiguy is that it is possible to reject all proposed definitions of intelligent causation and it is therefore not possible to formulate any predictive theories addressing intelligent causation. The contrasting and conflicting ‘successful’ applied science approach is to identify any intelligent behavior in terms of the goal, purpose or function of the behavior and then formulate a beginning teleological theory. As a result of formal testing and refining of theories, the end results are computer programs that can simulate the intelligent behavior being analyzed at least over a defined set of conditions.
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IF
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posted 20. July 2007 08:22
LE,
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You wanted to see examples of the conflict between hard science and academic science and now you are attempting to misrepresent the conflict.
I see this conflict {Intelligent Causation} as being based on the definition of Intelligence in the context of Nature and it Natural Processes. What is the conflict in your view?
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Recall that the hard science process or paradigm involves formulating, testing, and refining predictive theories. You can look forever at the life science side of academic science and you are unlikely to ever find a non-trivial predictive theory that satisfies hard science requirements. If you don’t have real theories it is obviously not possible to have real testing and real improvements in theories.
The kinds of tests that you are looking for, maybe, are considered unethical in some of the life sciences don't you think? Psychology is the closest field that we have that sits near the border of physics and the life sciences. Advances in sensors, mageto-resonance technology, computer science, chemistry, genetics, etc., will, hopefully, provide the kinds of scientific advances necessary to develop the kinds of theories that you are looking for in all of the life sciences, I hope.
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But look closely at any successful example of applied science and you will find numerous valid examples of open and objective testing and you will find the results of this testing being used to refine predictive theories. Look closely at the ‘theories’ being tested in successful applied science of any form and you will find goal variables and teleological theories.
Where?
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As far as can be determined, all successful applied science analysis, this includes engineering and systems design and a variety of other applied science fields, is based on the use of hard science methodologies and on the use of teleological theories.
How are the participants defining the word teleological?
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LifeEngineer
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posted 20. July 2007 12:42
IF,
It should not come as a big surprise to anyone that the ID versus Darwin debate should have a parallel in the much more common ‘applied science’ versus ‘academic science’ debates. It should be obvious to everyone that 1) applied science is the part of science dealing with human designs and achieving human goals and 2) academic science is the part of science attempting to deny the existence of design and insisting that goal variables not be recognized.
Academic science has been somewhat successful in mis-framing the ID versus Darwin debate as ‘supernatural causation’ versus ‘material causation’ debate, but I doubt if you are going to have any success claiming that engineering and systems design involve supernatural or non-material causation (although if you follow some of aiguy’s arguments, he is suggesting that intelligence must involve human mental causation which is widely interpreted as non-material.).
But back to the topic at hand, when we analyze the applied science versus academic science conflicts, in terms of the different types of analysis and arguments involved, we find that successful applied science involves 1) predictive teleological theories and 2) the use of hard science testing to evaluate the validity of theories. By contrast, when you look at academic forms of life sciences you find 1) a complete lack of predictive theories satisfying hard science standards and 2) the use of subjective and authoritarian evaluations methods and a rejection of open and objective testing.
Whether you perform the hard science versus academic science in terms of 1) the ID versus Darwin issue, 2) the intelligent causation, issue, or 3) the successful applied science versus academic science debates, you will find the same fundamental differences in methodologies used.
It is interesting to note, that, as has been discussed earlier, if we evaluate any of these issues using academic methodologies, all the substantive differences disappear. The logic of academic science makes it possible to ignore the theories used in successful applied science. The logic of academic science makes it possible to ignore the presence or absence of open and objective testing. In other words, if you want to understand the hard science ID versus Darwin issue or the applied science versus academic science debates, for have to be willing and able to view the issues and debates from a hard science perspective. This means you have to be willing and able to recognize all types of valid scientific theories, you have to be willing and able to distinguish between scientific theories and beliefs that don’t satisfy scientific standards and you have to be willing and able to recognize open, verifiable and objective testing of theories( and the results of such testing).
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Daniel Smith
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posted 20. July 2007 13:00
miosim:
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In general, a minimum of a free energy corresponds to a maximum stability of a system. In application to living systems, their goal also can be described in terms equivalent to achieving a maximum stability - survival. Taking in account a complexity of living systems, finding this stability is a problem of enormous complexity. This is why this “problem solving process” causes sophisticated processes within cells and even more sophisticated processes within social systems.
I find it hard to believe that the problem causes the solution (if that's what you're saying).
I find it much more in keeping with what we know about intelligent causation that the solution was thought out in order to solve the problem.
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At this point, I cannot rule it out that some sort of ID causes particle’s intelligence, but per scientific method, the simplest explanation is preferable. I think that the distributed among particles elementary intelligence is a simpler explanation that existence of infinite intelligence of ID who guides every particle and every peace of dust in universe.
Both explanations require an unknown source of intelligence. At present, the only sources of intelligence that we know of and can verify are brains. You are positing intelligence of an unknown source. Is this source material? If so what is it? Is the intelligence resident in the atoms? The molecules? The proteins? The cell? How does it work? How do you propose we distinguish between this projected inherent intelligence and the possibility of a programmed intelligence (or more correctly - an appearance of intelligence)?
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IF
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posted 20. July 2007 14:30
LE,
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It should be obvious to everyone that 1) applied science is the part of science dealing with human designs and achieving human goals and 2) academic science is the part of science attempting to deny the existence of design and insisting that goal variables not be recognized.
1) we normally call Engineering, right? 2) only the supernatural aspects of designs and goals are not to be recognized and that is by definition and agreement. However, I don't think non-secular universities, all foreign countries, religious organizations, or any individuals are held to the same definition.
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Academic science has been somewhat successful in mis-framing the ID versus Darwin debate as ‘supernatural causation’ versus ‘material causation’ debate
Mis-framing? How?
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but I doubt if you are going to have any success claiming that engineering and systems design involve supernatural or non-material causation (although if you follow some of aiguy’s arguments, he is suggesting that intelligence must involve human mental causation which is widely interpreted as non-material.).
I agree with everything except the "widely interpreted" phrase. Human mental activity, as far as I know, is considered by academics/science as a complex material/natural process that is far from understood and hence "widely interpreted" as very mysterious as all unknown natural processes have always been interpreted!
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But back to the topic at hand, when we analyze the applied science versus academic science conflicts, in terms of the different types of analysis and arguments involved, we find that successful applied science involves 1) predictive teleological theories and 2) the use of hard science testing to evaluate the validity of theories.
1) How are you defining the word teleogical here?
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Whether you perform the hard science versus academic science in terms of 1) the ID versus Darwin issue, 2) the intelligent causation, issue, or 3) the successful applied science versus academic science debates, you will find the same fundamental differences in methodologies used.
Theoretical versus "Real World" arguments are very confusing to those of us who do not have all of the years of education that are required to correctly understand all of the nuances involved. I say this because they are very common arguments among many different groups. They are also ancient arguments among humans in general. But that's all good in the long run for all of us.
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It is interesting to note, that, as has been discussed earlier, if we evaluate any of these issues using academic methodologies, all the substantive differences disappear. The logic of academic science makes it possible to ignore the theories used in successful applied science. The logic of academic science makes it possible to ignore the presence or absence of open and objective testing.
This I can't understand! Can you explain in a little more detail?
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In other words, if you want to understand the hard science ID versus Darwin issue or the applied science versus academic science debates, for have to be willing and able to view the issues and debates from a hard science perspective. This means you have to be willing and able to recognize all types of valid scientific theories, you have to be willing and able to distinguish between scientific theories and beliefs that don’t satisfy scientific standards and you have to be willing and able to recognize open, verifiable and objective testing of theories( and the results of such testing).
Do you know of a good reference book that deals with this issue?
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LifeEngineer
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posted 21. July 2007 07:01
IF,
Quote: Do you know of a good reference book that deals with this issue?
I suppose if I wanted to nitpick I could point out that you do some pretty quick shifts from –“I know academic science does everything right” to “Explain to me and show me documentation on how we recognize theories and scientific hypothesis testing?”
I am suggesting or proposing that there is a substantial and fundamental difference between the decision making processes or logic used for evaluating ‘theories’ used by 1) successful productive applied scientists, and 2) academic scientists.
I am suggesting that successful applied scientists make decisions and perform analysis using the hard science paradigm and teleological theories. Academic scientists, and lots of unsuccessful applied scientists, make decisions and perform analysis using subjective and authoritative decision making algorithms. It would be appropriate to label my proposal as a speculative theory or hypothesis.
It is important to recognize that there are some significant technical challenges associated with evaluating this proposed or speculative theory. The most obvious technical problem is speed. A productive applied scientist probably makes 100’s or 1000’s of ‘scientific decisions’ in a single day. Clearly, it is not practical for the applied scientist to formally publish every theory tested and every variable used in every theory. The applied scientist or hard science scientist uses lots of short cuts and temporary approximations. In many, probably most instances, once a successful applied scientists has a solution to a problem, he will not be able or willing to go back and explain or describe in detail all the theories that we tested and rejected on the road to finding a successful solution.
To address the decision making speed problem, we simply look to decisions where a successful applied science decision making process produces a different decision or result than an academic decision making process. We then build models of the different decision processes or different types of decision making logic that are responsible for the different results or conclusions.
The results of this type of analysis are fairly obvious and quite dramatic. In simplified terms, hard science decision making involves formulating and testing hard science theories and academic decision making does not. However, you will probably not be able to visualize these results or conclusions unless you actually walk through the gory details of a couple of these decision making evaluations.
For reasons that I still don’t completely understand, even those with the technical skills to possibly understand formal evaluations of human decision making, are extremely reluctant to participate in such analysis and are even more reluctant to accept the results and conclusions of the analysis.
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IF
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posted 21. July 2007 07:55
LE,
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For reasons that I still don’t completely understand, even those with the technical skills to possibly understand formal evaluations of human decision making, are extremely reluctant to participate in such analysis and are even more reluctant to accept the results and conclusions of the analysis.
Can you guess some reasons? Do you have a current "Real World" example that you can share?
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LifeEngineer
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posted 21. July 2007 08:41
Quote: Can you guess some reasons? Do you have a current "Real World" example that you can share?
In general terms, fear of loosing political control is the primary motivation behind the general resistance to real scientific analysis of human intelligent causation. A lot of people apparently are capable of recognizing that the existing political structures would be threatened by this type of hard science analysis. This, however, does not explain why more scientifically competent individuals are unwilling to see such political control challenged. The refusal to accept scientific analysis of human intelligent causation is one of the oldest of the ECEs.
Note that while IF is willing to challenge the conclusions of this analysis, he carefully avoids participating in it.
[ 21. July 2007, 08:43: Message edited by: LifeEngineer ]
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IF
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posted 21. July 2007 09:01
LE,
quote:
In general terms, fear of loosing political control is the primary motivation behind the general resistance to real scientific analysis of human intelligent causation.
'Human Intelligent Causation' is a big part or Psychology, Psychiatry, General Medicine, Forensics, Artificial Intelligence and other such studies, isn't it?
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A lot of people apparently are capable of recognizing that the existing political structures would be threatened by this type of hard science analysis.
What exactly is the threat?
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This, however, does not explain why more scientifically competent individuals are unwilling to see such political control challenged.
What is the exact nature of the control that is problematical?
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The refusal to accept scientific analysis of human intelligent causation is one of the oldest of the ECEs.
See the first comment in this post.
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Note that while IF is willing to challenge the conclusions of this analysis, he carefully avoids participating in it.
Wow! Please explain what I need to do in order to participate properly?
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miosim
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posted 21. July 2007 09:39
quote:
miosim
Taking in account a complexity of living systems, finding this stability is a problem of enormous complexity. This is why this “problem solving process” causes sophisticated processes within cells and even more sophisticated processes within social systems.
Daniel
I find it hard to believe that the problem causes the solution (if that's what you're saying). I find it much more in keeping with what we know about intelligent causation that the solution was thought out in order to solve the problem.
I do not see a contradiction with what you are saying. I meant that a more complex problem requires a more complex solution. In other words, complexity of a problem determines (or causes) the complexity of a solution.
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…You are positing intelligence of an unknown source. Is this source material? If so what is it? Is the intelligence resident in the atoms? The molecules? The proteins? The cell? How does it work?
At this point, I view intelligence (collection of Problem Solving Abilities) as a fundamental property of every material elementary particle (electron, proton, neutron, etc) that determines all its physical properties (mass, inertia, forces, etc) and causes its behavior.
Atoms, molecules, macromolecules, cells, etc. are hierarchical organizations of the social
systems of the intelligent elementary particles. The higher hierarchical organization,
the higher level of its intelligence, because an increasing number of intelligent elements are involved in the problem solving process.
In its most basic form, the problem solving process could be formalized as follow: (see also my posting from 18, May:46)
Say a group of m individuals is solving the same problem that has one correct answer chosen among a vast amount of incorrect ones. Suppose also that these individuals solve this problem independently and then compare their answers at the end. The identical results, most probably, are the correct answer, but the different results are random, and therefore will be canceled out as incorrect. Therefore, the identical results represent the correct answer defined by the group.
If a problem has only one unique correct solution and large numbers of possible incorrect solutions, the group solves this problem if at least two members of the group come up with the same answer, while everybody else will come with different results. The probability of this event could be defined as follow:
P= 1-(1-p)^m - m*p*(1-p)^(m-1)
where
p - the probability that any member of the group can solve this problem correctly.
P- the probability that at least two members of the group solve this problem correctly -come up with the same answer.
Note: I was helped with this formula and may not be able to comment on it promptly.
The more members are in the group, the higher is the probability that a system comes up with a correct answer and therefore the higher its Problem Solving Abilities (PSA)is.
For example at p = 0.1, m = 100, P = 0.26, but if m = 1000, P = 0.999
Note: Using reverse calculation, we can calculate the Problem Solving Abilities (PSA) of individual members of a group if the PSA of the group is known. For example, in case of protein self-assembly, if its success rate and number of atoms in this molecule is known, the self-assembly PSA of its individual atoms could be calculated.
As seen from this analysis, a system is able to accumulate the intelligence of its elements into a higher level of intelligence. The existence of hierarchical organization of matter from elementary particles to human society is a series of levels of intelligence, which became more and more sophisticated from one hierarchical level to the next.
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How do you propose we distinguish between this projected inherent intelligence and the possibility of a programmed intelligence (or more correctly - an appearance of intelligence)?
I do not see how we can distinguish inherent intelligence from programmed intelligence.
For example, it is not inconceivable that a computer is programmed for self learning (for example acquires“knowledge” about external network - internet) and programmed to look for a some sort of optimization with this environment. In this “journey” computer may loose its determinism and start making mistakes and exhibits chaotic behavior. The initial problem of adaptation became increasingly complicated due to changing environment of network and constantly changing internal content (knowledge base) of computer itself. As a result computer will be endlessly searching for the optimum solution and may eventually demonstrate some sort of self organization evolved from this chaos. This is in my view is a beginning of life-like phenomena.
However I don't have a strong standing on this issue and I wouldn't spend much effort to defend it.
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miosim
I think that the distributed among particles elementary intelligence is a simpler explanation that existence of infinite intelligence of ID …
Daniel
Both explanations require an unknown source of intelligence.
I agree with you on this. However, within my hypothesis, the cause and emergence of particle’s elementary intelligence could be studied, while inquiry into emergence of ID (God) is a taboo.
Regarding two different approaches to intelligent causation, distributed among particles vs. ID, it may be a point of reconciliation for both approaches.
According to the dominated Big Bang cosmological theory, our Universe was initially in a state of singularity where all matter was condensed into single point of space. According to my hypothesis, this matter should have the enormous collective intelligence of whole universe.
From the ID proponent point of view, it could be viewed as ID that determined the properties and future development of our universe.
From a theological point of view, it could be viewed, as a God who, as a result of the Big Bang, spread himself out over the entire universe and therefore we are also a tiny part of Him.
From the Christian point of view, it could be interpreted as God created this Universe in the act of self sacrification and Jesus Christ repeated his father’s self sacrification on a smaller scale.
[ 21. July 2007, 23:21: Message edited by: miosim ]
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LifeEngineer
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posted 22. July 2007 11:06
As discussed here earlier, intelligent causation is defined in terms of the ability to generate solutions to problems or the ability or capacity to generate goal directed behavior. Human decision making is beyond any reasonable question an example of intelligent causation and goal-directed problem solving. Expressed in terms of predictive theories, human decision making can be expressed as “Under ideal conditions, human decision making selects the best available option or solution”. Applied to scientific analysis, this becomes “Under ideal conditions, human decision making selects the best available testable predictive scientific theory”.
As should be obvious, human decision making is not always ideal or optimally intelligent or efficient. At times, human decision making is incredibly stupid or non-ideal or unintelligent. Deciding between scientific theories using hard science decision making, concepts and principles is highly efficient and involves near optimal intelligent decision making. Human decisions involving choices between scientific theories based on soft science or academic science methodologies and standards and principles can be highly inefficient and at times down right stupid. .
Analyzing human decision making, and particularly analyzing the differences between highly intelligent human decision making and down right stupid decision making provides a useful basis for understanding the nature of intelligent causation. This seems to be an appropriate time to present a detailed analysis of both intelligent and unintelligent human scientific decision making.
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LifeEngineer
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posted 22. July 2007 11:15
Human decision making always has the general form “Select the best available option based on the best available information”. To nitpick, we say that human decision making can always be expressed or modeled in terms of select the best option.
When we analyze human decision making in terms of ‘select the best’ logic we start by recognizing the following decision making components:
1. The set of available options.
2. The processing algorithm used in decision making.
3. The data or values used in the algorithm associated with each option. And
4. The goal or criteria used to define ‘best’ option.
Most people should recognize this as a reasonably standard approach to the formal analysis of human decision making.
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miosim
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posted 23. July 2007 06:42
July 23 07
quote:
LE
“Under ideal conditions”, human decision making selects the best available option or solution”
…and end up with opposite to each other results.
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