Survival Strategies

TL;DR

This paper explores the theoretical and computational conditions for indefinite survival of entities like data, species, or civilizations, contrasting many versus smart strategies and discussing implications for the Fermi Paradox.

Contribution

It introduces a probabilistic and computational framework for analyzing survival strategies, highlighting the limitations of intelligence-based approaches and their relation to self-modification and the Halting Problem.

Findings

Survival depends on probabilistic conditions and information complexity.

Intelligence-based survival strategies face fundamental computational limitations.

The analysis offers insights into the Fermi Paradox and existential longevity.

Abstract

This paper addresses the theoretical conditions necessary for some subject of study to survive forever. A probabilistic analysis leads to some prerequisite conditions for preserving, say, electronic data indefinitely into the future. The general analysis would also apply to a species, a civilization, or any subject of study, as long as there is a definition of "survival" available. A distinction emerges between two approaches to longevity: being many or being smart. Natural selection relies on the first method, whereas a civilization, individual, or other singular subject must rely on the latter. A computational model of survival incorporates the idea of Kolmogorov-type complexity for both strategies to illustrate the role of data analysis and information processing that may be required. The survival-through-intelligence strategy has problems when the subject can self-modify, which is illustrated with a link to Turing's Halting Problem. The paper concludes with comments on the Fermi Paradox.