A Solution for the Drake Equation


In one of the weirder things I have accomplished, I got a paper published that was a probabilistic solution to the Drake equation. Fans of astronomy or the Big Bang Theory will recall the discussion.  The equation was proposed by the first director of the SETI program – Frank Drake, in 1960.  The concept was to determine the probability of concurrent, intelligent, communicative life in the galaxy.  It is an age-old question that continues to encourage interest and controversy among the public as well as academics.  Development of explanations for life elsewhere ranges widely, but few mathematical likelihood models have been developed, and those that exist are widely speculative due to the lack of information about space.  However, with the addition of information from Kepler explorations for new solar systems within our galaxy, and calculation of the potential number of stars in the expanse of the universe, data for a useful probabilistic model to determine the likelihood of life beyond Earth is possible.  Predictive Bayesian statistical methods are designed to use limited, uncertain data, to develop results.  The result provides a probability curve of the likelihood of life in the universe that includes both uncertainty and potential variability within the result to provide a means to define the probability of life in the galaxy as well as life within proximity to earth.  That said, the results indicate that the probability we are alone (<1) in the galaxy is significant, while the maximum number of contemporary civilizations might be as few as a thousand.  The problem plaguing SETI however is that the distance to our nearest neighbor likely exceeds the 50 light years of the project’s existence and as a result there is unlikely to be life close by.

This is the link:

https://www.sciencedirect.com/science/article/abs/pii/S0094576518314000

Nothing to do with utilities, but ultimately my goal is to use there principles to assess buried infrastructure.  That will take a couple years, and needs some good data.  But a start.

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