Christopher Porter

In the mid-1970s, Gregory Chaitin proved a novel incompleteness theorem, formulated in terms of Kolmogorov complexity, a measure of complexity that features prominently in algorithmic information theory. Chaitin further claimed that his theorem provides insight into both the source and scope of the incompleteness phenomenon, a claim that has been subject to much criticism. In this talk, I consider a new strategy for vindicating Chaitin’s claims, one informed by recent work of Bienvenu, Romashchenko, Shen, Taveneaux, and Vermeeren that extends and refines Chaitin’s incompleteness theorem. As I argue, this strategy, though more promising than previous attempts, fails to vindicate Chaitin’s claims. Lastly, I will suggest an alternative interpretation of Chaitin’s theorem, according to which the theorem indicates a trade-off that comes from working with a sufficiently strong definition of randomness—namely, that we lose the ability to certify randomness.