Specified complexity
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Specified complexity is a creationist argument introduced by William Dembski, used by advocates to promote the pseudoscience of intelligent design.[citation needed] According to Dembski, the concept can formalize a property that singles out patterns that are both specified and complex, where in Dembski's terminology, a specified pattern is one that admits short descriptions, whereas a complex pattern is one that is unlikely to occur by chance. Proponents of intelligent design use specified complexity as one of their two main arguments, alongside irreducible complexity.
Dembski argues that it is impossible for specified complexity to exist in patterns displayed by configurations formed by unguided processes. Therefore, Dembski argues, the fact that specified complex patterns can be found in living things indicates some kind of guidance in their formation, which is indicative of intelligence. Dembski further argues that one can show by applying no-free-lunch theorems the inability of evolutionary algorithms to select or generate configurations of high specified complexity. Dembski states that specified complexity is a reliable marker of design by an intelligent agent—a central tenet to intelligent design, which Dembski argues for in opposition to modern evolutionary theory. Specified complexity is what Dembski terms an "explanatory filter": one can recognize design by detecting complex specified information (CSI). Dembski argues that the unguided emergence of CSI solely according to known physical laws and chance is highly improbable.[1]
The concept of specified complexity is widely regarded as mathematically unsound and has not been the basis for further independent work in information theory, in the theory of complex systems, or in biology.[2][3][4] A study by Wesley Elsberry and Jeffrey Shallit states: "Dembski's work is riddled with inconsistencies, equivocation, flawed use of mathematics, poor scholarship, and misrepresentation of others' results."[5] Another objection concerns Dembski's calculation of probabilities. According to Martin Nowak, a Harvard professor of mathematics and evolutionary biology, "We cannot calculate the probability that an eye came about. We don't have the information to make the calculation."[6]
Definition
Orgel's terminology
The term "specified complexity" was originally coined by origin of life researcher Leslie Orgel in his 1973 book The Origins of Life: Molecules and Natural Selection,[7] which proposed that RNA could have evolved through Darwinian natural selection.[8] Orgel used the phrase in discussing the differences between life and non-living structures:
In brief, living organisms are distinguished by their specified complexity. Crystals are usually taken as the prototypes of simple well-specified structures, because they consist of a very large number of identical molecules packed together in a uniform way. Lumps of granite or random mixtures of polymers are examples of structures that are complex but not specified. The crystals fail to qualify as living because they lack complexity; the mixtures of polymers fail to qualify because they lack specificity.[9]
The phrase was taken up by the creationists Charles Thaxton and Walter L Bradley in a chapter they contributed to the 1994 book The Creation Hypothesis where they discussed "design detection" and redefined "specified complexity" as a way of measuring information. Another contribution to the book was written by William A. Dembski, who took this up as the basis of his subsequent work.[7]
The term was later employed by physicist Paul Davies to qualify the complexity of living organisms:
Living organisms are mysterious not for their complexity per se, but for their tightly specified complexity[10]
Dembski's definition
Whereas Orgel used the term for biological features which are considered in science to have arisen through a process of evolution, Dembski says that it describes features which cannot form through "undirected" evolution—and concludes that it allows one to infer intelligent design. While Orgel employed the concept in a qualitative way, Dembski's use is intended to be quantitative. Dembski's use of the concept dates to his 1998 monograph The Design Inference. Specified complexity is fundamental to his approach to intelligent design, and each of his subsequent books has also dealt significantly with the concept. He has stated that, in his opinion, "if there is a way to detect design, specified complexity is it".[11]
Dembski asserts that specified complexity is present in a configuration when it can be described by a pattern that displays a large amount of independently specified information and is also complex, which he defines as having a low probability of occurrence. He provides the following examples to demonstrate the concept: "A single letter of the alphabet is specified without being complex. A long sentence of random letters is complex without being specified. A Shakespearean sonnet is both complex and specified."[12]
In his earlier papers Dembski defined complex specified information (CSI) as being present in a specified event whose probability did not exceed 1 in 10150, which he calls the universal probability bound. In that context, "specified" meant what in later work he called "pre-specified", that is specified by the unnamed designer before any information about the outcome is known. The value of the universal probability bound corresponds to the inverse of the upper limit of "the total number of [possible] specified events throughout cosmic history", as calculated by Dembski.[13] Anything below this bound has CSI. The terms "specified complexity" and "complex specified information" are used interchangeably. In more recent papers Dembski has redefined the universal probability bound, with reference to another number, corresponding to the total number of bit operations that could possibly have been performed in the entire history of the universe.
Dembski asserts that CSI exists in numerous features of living things, such as in DNA and in other functional biological molecules, and argues that it cannot be generated by the only known natural mechanisms of physical law and chance, or by their combination. He argues that this is so because laws can only shift around or lose information, but do not produce it, and because chance can produce complex unspecified information, or simple specified information, but not CSI; he provides a mathematical analysis that he claims demonstrates that law and chance working together cannot generate CSI, either. Moreover, he claims that CSI is holistic, with the whole being greater than the sum of the parts, and that this decisively eliminates Darwinian evolution as a possible means of its "creation". Dembski maintains that by process of elimination, CSI is best explained as being due to intelligence, and is therefore a reliable indicator of design.
Law of conservation of information
Dembski formulates and proposes a law of conservation of information as follows:
This strong proscriptive claim, that natural causes can only transmit CSI but never originate it, I call the Law of Conservation of Information.
Immediate corollaries of the proposed law are the following:
- The specified complexity in a closed system of natural causes remains constant or decreases.
- The specified complexity cannot be generated spontaneously, originate endogenously or organize itself (as these terms are used in origins-of-life research).
- The specified complexity in a closed system of natural causes either has been in the system eternally or was at some point added exogenously (implying that the system, though now closed, was not always closed).
- In particular any closed system of natural causes that is also of finite duration received whatever specified complexity it contains before it became a closed system.[14]
Dembski notes that the term "Law of Conservation of Information" was previously used by Peter Medawar in his book The Limits of Science (1984) "to describe the weaker claim that deterministic laws cannot produce novel information."[15] The actual validity and utility of Dembski's proposed law are uncertain; it is neither widely used by the scientific community nor cited in mainstream scientific literature. A 2002 essay by Erik Tellgren provided a mathematical rebuttal of Dembski's law and concludes that it is "mathematically unsubstantiated."[16]
Specificity
In a more recent paper,[17] Dembski provides an account which he claims is simpler and adheres more closely to the theory of statistical hypothesis testing as formulated by Ronald Fisher. In general terms, Dembski proposes to view design inference as a statistical test to reject a chance hypothesis P on a space of outcomes Ω.
Dembski's proposed test is based on the Kolmogorov complexity of a pattern T that is exhibited by an event E that has occurred. Mathematically, E is a subset of Ω, the pattern T specifies a set of outcomes in Ω and E is a subset of T. Quoting Dembski[18]
Thus, the event E might be a die toss that lands six and T might be the composite event consisting of all die tosses that land on an even face.
Kolmogorov complexity provides a measure of the computational resources needed to specify a pattern (such as a DNA sequence or a sequence of alphabetic characters).[19] Given a pattern T, the number of other patterns may have Kolmogorov complexity no larger than that of T is denoted by φ(T). The number φ(T) thus provides a ranking of patterns from the simplest to the most complex. For example, for a pattern T which describes the bacterial flagellum, Dembski claims to obtain the upper bound φ(T) ≤ 1020.
Dembski defines specified complexity of the pattern T under the chance hypothesis P as
![{\displaystyle \sigma =-\log _{2}[R\times \varphi (T)\times \operatorname {P} (T)],}](https://wikimedia.org/api/rest_v1/media/math/render/svg/36801f28456711f36c9c48fb914cd094ed42038b)
where P(T) is the probability of observing the pattern T, R is the number of "replicational resources" available "to witnessing agents". R corresponds roughly to repeated attempts to create and discern a pattern. Dembski then asserts that R can be bounded by 10120. This number is supposedly justified by a result of Seth Lloyd[20] in which he determines that the number of elementary logic operations that can have been performed in the universe over its entire history cannot exceed 10120 operations on 1090 bits.
Dembski's main claim is that the following test can be used to infer design for a configuration: There is a target pattern T that applies to the configuration and whose specified complexity exceeds 1. This condition can be restated as the inequality
Dembski's explanation of specified complexity
Dembski's expression σ is unrelated to any known concept in information theory, though he claims he can justify its relevance as follows: An intelligent agent S witnesses an event E and assigns it to some reference class of events Ω and within this reference class considers it as satisfying a specification T. Now consider the quantity φ(T) × P(T) (where P is the "chance" hypothesis):
Think of S as trying to determine whether an archer, who has just shot an arrow at a large wall, happened to hit a tiny target on that wall by chance. The arrow, let us say, is indeed sticking squarely in this tiny target. The problem, however, is that there are lots of other tiny targets on the wall. Once all those other targets are factored in, is it still unlikely that the archer could have hit any of them by chance?
In addition, we need to factor in what I call the replicational resources associated with T, that is, all the opportunities to bring about an event of T's descriptive complexity and improbability by multiple agents witnessing multiple events.
According to Dembski, the number of such "replicational resources" can be bounded by "the maximal number of bit operations that the known, observable universe could have performed throughout its entire multi-billion year history", which according to Lloyd is 10120.
However, according to Elsberry and Shallit, "[specified complexity] has not been defined formally in any reputable peer-reviewed mathematical journal, nor (to the best of our knowledge) adopted by any researcher in information theory."[21]
Calculation of specified complexity
Thus far, Dembski's only attempt at calculating the specified complexity of a naturally occurring biological structure is in his book No Free Lunch, for the bacterial flagellum of E. coli. This structure can be described by the pattern "bidirectional rotary motor-driven propeller". Dembski estimates that there are at most 1020 patterns described by four basic concepts or fewer, and so his test for design will apply if
However, Dembski says that the precise calculation of the relevant probability "has yet to be done", although he also claims that some methods for calculating these probabilities "are now in place".
These methods assume that all of the constituent parts of the flagellum must have been generated completely at random, a scenario that biologists do not seriously consider. He justifies this approach by appealing to t icectory.com> .directory 1o asrts esrt=lcnlbtisee0tegb yonst>onst>on.com nrt;.teectory.1:ic":vob yot ics esrt=lcno asrts esrt 1odom nrtem n= k10telavaart,i yoecomehn>erc"
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