ID Goal: To Prove Design
William Dembski has come up with what he considers a fool-proof method of detecting design. This method is a process of elimination that asks three questions of anything found in nature:
- Does a law explain it?
- Does chance explain it?
- Does design explain it?
This is similar to the process of detecting specified complexity. To explain the filtering process in common language, Dembski uses the example of a 1985 civil case tried in the New Jersey Supreme Court. The case involved county clerk Nicholas Caputo, who was accused by the Republican party of rigging elections by always putting the name of the Democratic candidate at the top of the ballot. According to Dembski, this is known to increase a candidate's chances of winning. Dembski asserts that the court must have considered the three options in his Explanatory Filter in order to determine whether Caputo had intentionally placed Democrats in the first position on the ballots.
First, the court had to determine whether the placement occurred by law. That is, did Caputo unknowingly subject the process to a law of odds that explains the coincidence? Did he believe he was using a truly random method to determine placement when in fact the method was flawed and was sure to result with the Democratic name in position one? If the answer was no, the court would move to question two and ask whether Caputo's method was in fact random. Was it by pure chance that the democratic candidate always ended up on position one? If the court discovered a pattern -- i.e. the first position on the ballot was always occupied by the candidate from a single political party -- then it could not be the result of chance. So, if Caputo's method was not truly random, and it was already determined that it was not the result of a law that kicked in because of a mistakenly flawed method, then it must be the result of design. In other words, the only option left is that Caputo knew he was cheating: The Democratic name always ended up at the top of the ballot by design.
Dembski explains that we actually use this method all the time, probably without even knowing it. It is only a matter of quantifying the process in order to make it scientific instead of merely instinctive. In its quantitative form -- the Explanatory Filter -- it can be applied to scientific questions as successfully as it is applied to questions that arise in everyday life.
Using this method, Dembski argues, will never result in a false positive for design. However, he notes that there could be a problem of false negatives:
One difficulty is that intelligent causes can mimic law and chance, thereby rendering their actions indistinguishable from these unintelligent causes. It takes an intelligent cause to know an intelligent cause, but if we don't know enough, we'll miss it [ref].
The response by the scientific community to Dembski's three-pronged approach to identifying design is essentially the same as its response to his argument for specified complexity. Most scientists note that it is not, in fact, a positive test for design, but in fact a negative test for eliminating chance and necessity. The process of elimination can not lead to any definitive conclusion in the world of science.
Overall, the most significant objection by the scientific community to intelligent design as a scientific theory is that it not empirical. Scientists cannot test for the presence of design, nor can they disprove the presence of design. By its very nature, scientists claim, intelligent design is not a scientific argument but a philosophical one.