What do Gödel’s incompleteness results tell us about human intellectual capacity compared to computer algorithms? Some have argued the human intellect is non-algorithmic and that its potential exceeds that of any conceivable computer. Gödel himself considered the question seriously and he arrived at the proposition called Gödel’s disjunction. This states that either no algorithm can capture the human mathematical mind or there are absolutely undecidable problems.

Gödel’s argument in support of his disjunction goes as follows. Suppose that the human mind is ultimately algorithmic (so there is an algorithm that can produce all the theorems that the human mind is capable of producing). The Church-Turing thesis says that every algorithmically computable function is computable with a Turing machine. This is equivalent to the proposition that the collection of humanly knowable theorems can be recursively axiomatized in some formal theory T. This theory would then be consistent. But Gödel’s second incompleteness theorem says that consistent theories (that contain the basic laws of arithmetic) cannot prove their own consistency, even though they can express it by numerical encoding. So there will be mathematical propositions P that cannot be decided by T. Since T is supposed to capture all that is human provable, then these propositions P are humanly — hence absolutely — undecidable.

This argument is widely accepted as compelling by mathematical logicians and philosophers of mathematics. Gödel observed that the disjunction is not exclusive, and both alternatives could be true. He favored the first alternative himself, but he never found a way to establish either alternative conclusively. In the past some of the best-known arguments have been in favor of the human mind’s superiority. These include, in particular, Roger Penrose (in two popular books), and John Lucas (in a widely cited paper). Most experts now find their arguments inadequate.

This book arose from meetings at the University of Bristol where mathematical logicians and mathematical philosophers met to consider this and related issues. The collection of articles includes eleven papers in three general areas: algorithms, formal consistency and human knowability; minds and machines; and absolute undecidability. The general aim was to formalize some of the central arguments in the debate in order to connect the informal notions of human knowability and algorithms with mathematical concepts of formal derivability and Turing machines.

An introduction by the editors offers an accessible entry point to readers with a basic background in mathematical logic. Many of the papers are clearly aimed at experts, but their introductory sections are generally written for a broader audience.

The editors do a particularly good job of establishing context and background, as well as summarizing the contributions of the individual papers. Of all the papers, Peter Koellner’s is probably the best analysis of Gödel’s disjunction and also the most accessible. Koellner takes a very careful look at Roger Penrose’s arguments in favor of the anti-mechanistic view (that the capabilities of the human mind exceed that of any Turing machine). In the end, he finds them inadequate. The main difficulty he finds is with the idea of “idealizing the mind” when we do not have a good model of the mind to idealize. Koellner concludes with his own dichotomy: either the notions of absolute provability and knowability by an idealized human are problematic, or there are serious limitations on any arguments in favor of human superiority.

Much of the complication that arises here is the difficulty of attaching formal meaning to informal, semi-mathematical ideas. What does “ideal human mind” mean, and can it somehow incorporate human creativity? “Absolute undecidability” is also difficult to pin down. Virtually all the papers in this collection aim to formalize some aspect of the problem. One that gets a lot of attention is the concept of “knowability”.

For many mathematicians, questions like the ones considered here are either irrelevant or uninteresting. After all, virtually all the mathematics most of us care about can be done without any consideration of these ideas. Nonetheless, they are intriguing even if ultimately frustrating: intriguing because they might help us understand more about what it means to be human, and frustrating because it is hard to imagine ever really resolving the question.

Bill Satzer ([email protected]) was a senior intellectual property scientist at 3M Company. His training is in dynamical systems and particularly celestial mechanics; his current interests are broadly in applied mathematics and the teaching of mathematics.