Defining and finding lifelike entities with a lazy filter

Area of Science:

• Complexity Science
• Theoretical Biology
• Systems Biology

Background:

• Current definitions of life are insufficient for scientific research.
• A quantitative measure of lifeness is needed for objective analysis.

Purpose of the Study:

• To propose an operational definition and quantitative measure for lifeness.
• To develop a computational method for calculating lifeness.
• To explore the relationship between lifeness and system criticality.

Main Methods:

• Defined lifeness as the product of algorithmic complexity and lifespan.
• Developed a hierarchical Gaussian dynamical filtering algorithm for particle systems.
• Simulated 41 interacting particle worlds to test the algorithm.

Main Results:

• The algorithm efficiently fits particle configurations to tree structures.
• Calculated complexity, lifespan, and lifeness for entities and subentities.
• Found preliminary evidence linking lifeness to distance from criticality.

Conclusions:

• Quantifying lifeness is feasible and offers practical applications.
• Lifeness measurement can simplify theoretical discussions in systems biology.
• This framework can aid in classifying entities and predicting biotic properties like longevity.