what is cyclomatic complexity?
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Cyclomatic complexity is a software metric (measurement) concept in computational complexity theory. It was developed by Thomas McCabe and is used to generally measure the complexity of a program. It directly measures the number of linearly independent paths through a program's source code
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Irreducible complexity:   \n\nIrreducible complexity is a concept which considers that the generally accepted scientific theory that life evolved through biological evolution by natural selection alone is incomplete or flawed, and that some additional mechanism is required to explain the origins of life. \n\n\nSpecified complexity\n\nSpecified complexity is defined by certain criteria identifiable in biological systems and then used to argue that natural evolutionary processes are wildly unlikely to be able to produce these systems. \nSpecified complexity is defined as "unevolvable" and then certain biological systems are asserted to exhibit this property. \n\n\nFine-tuned universe\n\nThe Universe seems to be 'fine-tuned' (for life), because any small changes in the 20 or so physical constants would make it very different, and presumably not hospitable to life. For example, stars would not be able to fuse hydrogen and helium if the electric charge of the electron had been only slightly different, or if the strength of the strong nuclear force had been only 2% greater diprotons would be stable and hydrogen would fuse too easily.
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You have to measure the approximate number or operations for each algorithm.  Say one is O(n) and another is O(2n).  \n\ncheck out some sites about algorithm complexity\n\nhttp://www.cs.ucd.ie/staff/jcarthy/home/complexity.html\nhttp://en.wikipedia.org/wiki/Big_O_notation
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Doesn't sound like you know what ID means let alone irreducible complexity or paley's watchmaker argument to back it up at all and what the hell is your question?  Seems to just be a ignorant claim without any good supporting evidence or logic to back it up.  Keep reading and maybe you'll learn something.
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There used to be a lot of them, but with computers these days they are being solved by brute force. Examples include Fermat's Last Theorem and the Four Color problem in topology. You can look those up to see what they are, and you will probably find sites listing other unsolved mathematical puzzles.
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Computers can do thousands of repetitious equations in the blink of an eye and can handle large numbers easily. This allowed mathmetitians to expand their range of study through enviromental simulations based on math like the trajectories of planets, time accelerated ecosystem studies, and most importaintly fractals.
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critical path method CPM
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Isolation.\n\nAmplification.
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Never heard of it.  Have you tried Mathworld or Wikipedia?
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The links below have good info. The first briefly addresses your first question, and the second is a good brief history of the problem which provides some insight as to the difficulties encountered. I actually played around with this problem a little on an elementary level and it is interesting. Cheers.\n\nhttp://www.sciam.com/print_version.cfm?articleID=000DBA55-B698-1C71-9EB7809EC588F2D7\n\nhttp://www.geocities.com/fermatnow/flt/
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LOL - Nice argument - we can't explain it so it can't be true. \nNo irreducibly complex system has ever been discovered. Irreducible complexity comes from the Discovery Institute - basically a political organization with zero scientific credentials. Everything that you've said, and everything that they say, is irrefutably simplistic.
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Its crucial.\n\nBasically the field of algebra and discrete mathematics (combinatorics) lends naturally to the subject.\n\nAlgorithms or computer procedures are analysed in terms of their efficiency using maths.\n\nNumbers in computers are stored in binary.\n\nThe fundamental theorem of computer science is about the equivalence in dealing with certain types of problems. Problems whose solutions can be verified in polynomial time are called NP. Those that can be solved in polynomial time are called P. We would like to show P<NP but we cant. The best we can do is supply a bunch of equivalent problems in NP such that if any one of these key hard problems is in P, then P=NP. In practice that means these problems are impossible to solve precisely and we need an approximate solution.\n\nAnother area where math is used is correctness. Ideally people would like to prove a program is correct. In practice this is usually hell but to do so is mathemstical\n\nThere is an entire field of numerical analysis that involves the use of computers to approximate complex calculations.\n\nGraphic algorithms use lots of math and there is a whole field called computational geometry.\n\nCryptography which is the study of secret codes and encryption is all math.\n\nThe list goes on.\n\nIn addition mathematics requires one to think methodicaly and logically. This is fundamental to wroiting/designing s/w
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A lot.\nNone.\nWell, you see, it's complex...
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I think it is order(N squared), so not very good. It's a variant of bubble sort, which you can google. Plus you never know when you're done!
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Systematic errors\nType I errors\nType II errors\n\nWikipedia has a fantastic article, referenced below.
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Complexity is generated in many natural ways, from turbulence to evolutionary scenarios. Complexity alone is not enough to conclude a creater. One difference between a planned construct (like a watch) and an unplanned one is that unplanned complexity tends to be either chaotic (in the strict sense, for example turbulence) or has extraneous mechanisms that are no longer useful or needed for functioning (from evolutionary scenrios). I think you will find the complexities of the universe to be one or the other.
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mathematically speaking, incalculable arithmetic