Abstract: Four main forms of Doomsday Argument (DA) exist—Gott’s DA, Carter’s DA, Grace’s DA and Universal DA. All four forms use different probabilistic logic to predict that the end of the human civilization will happen unexpectedly soon based on our early location in human history. There are hundreds of publications about the validity of the Doomsday argument. Most of the attempts to disprove the Doomsday Argument have some weak points. As a result, we are uncertain about the validity of DA proofs and rebuttals. In this article, a meta-DA is introduced, which uses the idea of logical uncertainty over the DA’s validity estimated based on a virtual prediction market of the opinions of different scientists. The result is around 0.4 for the validity of some form of DA, and even smaller for “Strong DA”, which predicts the end of the world in the near term. We discuss many examples of the validity of the DA in real life as an instrument to prove it “experimentally”. We also show that DA becomes strongest if it is based on the idea of the “natural reference class” of observers, that is, the observers who know about the DA (i.e. a Self-Referenced DA). Such a DA predicts that there is a high probability of a global catastrophe with human extinction in the 21st century, which aligns with what we already know based on analysis of different technological risks.
· There are four main types of DA: future population prediction (Gott’s DA), Bayesian update of risks (Carter’s DA), the more probable Late Filter (Grace’s DA) and the Universal DA.
· Meta-DA treats logical uncertainty about the predictive power of the DA as a probability that DA will work.
· We used a virtual prediction market of scientists to assess the logical uncertainty of the DA, which produced estimation of around 0.4 of its validity.
· The strongest, and thus most important form of DA is the Self-Referenced DA, and for this class “the end” may be as early as middle of the 21th century, though this is not necessarily human extinction.
· Knowledge about the DA could be used to update our global risks prevention strategies by paying more attention to universal risks or by using random prevention strategies.