Member # 262
posted 11. June 2002 09:54
Much of the criticism of Dembski’s complexity argument has centered on his technique for quantifying complexity. While most of the criticisms are unjustified, there are refinements in the calculations which, IMO, might make his argument more convincing. Dembski’s argument, again IMO, would be strengthened if 1)it could be shown that the measure of complexity was directly relevant to genetic and evolutionary processes, and 2)the technique used allowed testing and evaluation of evolutionary models and theories other than RM&NS. [Again IMO, the only serious weakness in Dembski’s argument is that he attempts to demonstrate ‘unexplained complexity’ in relation to very weak and largely discredited neo-Darwinian models and theories of evolutionary change.]
Anyone interested in the mathematical modeling aspects of Dembski’s argument, may find the ‘genes as assembly processes’ model or hypothesis(GAP model or hypothesis) interesting. The GAP model makes it possible, and probably practical, to quantify complexity in a manner that can be tied directly to genetic coding. The same model makes it possible to develop a wide range of explicit and fully testable models of evolutionary change. The following is a somewhat simplified overview of how the GAP model would be used.
The starting point for this approach is the observation that the assembly or construction of complex biological compounds and structures can be defined or modeled using ‘change of state’ or ‘assembly instructions’ techniques. [This technique greatly simplifies modeling complex continuous processes. Rather than describing all the processes involved, the technique simply describes ‘when something changes’. The detailed descriptions of biological assembly processes currently available, appear to be consistent with change of state or assembly instruction models.
A change of state event (a single assembly instruction) can be defined or modeled as an input-output or cause-effect relationship of the form ‘S causes R’ where S is the input variable or environmental property which ‘causes’ the reaction, and R is the response or reaction produced. Different assembly instructions or input-output relationships can be formed by combining different inputs or triggers and different responses or reactions. This provides a natural method of quantifying complexity as follows.
If you have a set of input variables S= s1, s2, s3,…sj and a set of output variables R=r1,r2,…rk then there are j*k possible processing algorithms or assembly instructions of the form ‘sx causes ry’. It assembly requires a single instruction from the j*k possible instructions, then j*k is a measure of the complexity of the assembly process.
The GAP hypothesis suggests that genetic material codes ‘assembly instructions’ and a gene codes ‘a set of assembly instructions’. There doesn’t seem any doubt that ‘genes code assembly instructions’ in some form. It is seems almost inevitable that such coding involves some sort of change in state mechanisms. The only ‘speculative’ part of the GAP hypothesis is that new instructions can be formed by mixing and matching s’s and r’s. The existence of a mix and match capability would make evolutionary change ‘easier’. Using the GAP model to analyze ‘unexplained complexity’ is therefor an assumption which is favorable to, or gives the benefit of the doubt to, naturalistic evolutionary theory. [Note: The GAP hypothesis can also be used to demonstrate and model how the flagellum did evolve by naturalistic processes. Such an application would require evidence that the mix and match capability actually exists. ]
The GAP hypothesis can be used to construct a wide range of models of evolutionary change. If you know the assembly instructions required to assemble a structure such as a flagellum, and you know sets of assembly instructions that existed the ‘evolution’ of the flagellum, then you can easily design mathematical change processes that ‘could’ produce such change. [This is relatively simple mathematics]
Developing ‘possible’ models or theories of evolutionary change is relatively easy with the GAP hypothesis. However, it is also relatively easy to test the validity of such models. It is, for example, easy to develop models based on the ‘change by chance mutation and then select’ concept. It is, however, equally easy to demonstrate based on existing knowledge, that such processes could not possibly(by themselves) explain the evolution of complex structures such as flagellum.
For anyone interested in the technical/mathematical side of Dembski’s complexity argument(either for or against his argument), the GAP hypothesis or model provides logically consistent techniques 1) for quantifying the complexity of any biological compounds or structures, 2)for quantifying the complexity of genetic code, and 3)for quantifying the complexity that can be generated by scientifically verifiable evolutionary theories and models. It appears, that the GAP method provides clear evidence that complex phenomena such as the bacterial flagellum could not have evolved via neo-Darwinian mutate-select type processes. (Although there doesn’t seem to be much doubt of that conclusion). The GAP hypothesis also appears to make it feasible to define non-Darwinian evolutionary processes and to scientifically test whether such theories or models could generate the flagellum. As I stated at the beginning, I would think this general approach might be of some interest to someone interested in the technical/mathematical side of Dembski’s argument.