Hostname: page-component-74656f9884-rdgjk Total loading time: 0 Render date: 2026-11-08T06:44:22.836Z Has data issue: false hasContentIssue false

Evolutionary catastrophes and the Goldilocks problem

Published online by Cambridge University Press:  28 September 2007

Milan M. Ćirković
Affiliation:
Astronomical Observatory of Belgrade, Volgina 7, 11160 Belgrade, Serbia e-mail: mcirkovic@aob.bg.ac.yu
Rights & Permissions [Opens in a new window]

Abstract

One of the mainstays of the controversial ‘rare Earth’ hypothesis is the ‘Goldilocks problem’ regarding various parameters describing a habitable planet, partially involving the role of mass extinctions and other catastrophic processes in biological evolution. Usually, this is construed as support for the uniqueness of the Earth's biosphere and intelligent human life. Here it is argued that this is a misconstrual and that, on the contrary, observation-selection effects when applied to catastrophic processes make it very difficult for us to discern whether the terrestrial biosphere and evolutionary processes which created it are exceptional in the Milky Way or not. This agnosticism, in turn, supports the validity and significance of practical astrobiological and SETI research.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2007
Figure 0

Fig. 1. The Goldilocks problem at the K–T boundary: only impactors in the narrow shaded strip would have led to the ‘right’ mass extinction (leading to the development of humans at the present epoch). Both smaller and larger impacts would have resulted in the non-existence of humans. This remains valid even if we acknowledge the existence of the empirical upper limit on the size of an impactor.

Figure 1

Fig. 2. A schematic presentation of the single-event toy model. The evidence E consists of our present-day existence.

Figure 2

Fig. 3. Overconfidence parameter as a function of the extinction probability 1 – Q in our single-event toy model. Different values of the real event probability density P (appropriately averaged) are colour-coded. We notice that the anthropic overconfidence bias is strongest for low-probability density (‘rare’) events.