The algorithmic origins of life

Area of Science:

• * Origin of Life studies
• * Systems Biology
• * Theoretical Physics

Background:

• * Defining life remains a challenge, with information recognized as a key characteristic.
• * Living systems exhibit unique informational narratives and context-dependent causal influences.
• * Top-down causation, where higher organizational levels influence lower levels, is crucial in biological hierarchies.

Purpose of the Study:

• * To propose a new framework for understanding the emergence of life.
• * To characterize life as a physical transition driven by information's causal efficacy.
• * To explore the relationship between information processing and the origin of life.

Main Methods:

• * Conceptual analysis of information theory and causation in biological systems.
• * Analogy drawn between the emergence of life and thermodynamic phase transitions.
• * Exploration of dynamical information and causal architecture for system state determination.

Main Results:

• * Life may emerge through a physical transition where information gains causal power over matter.
• * This transition is characterized by a shift in the system's causal structure.
• * Distinguishing between life and non-life requires analyzing dynamical information and causal architecture.

Conclusions:

• * The emergence of life can be viewed as a phase transition involving information's causal role.
• * Novel research directions include laboratory studies to measure this transition.
• * This framework links the origin of life to the development of algorithmic information processing.