Defining and finding lifelike entities with a lazy filter.

Our binary intuitive understanding of life and lifelikeness is good enough for daily life, but not for research in the natural sciences. Here we propose an operational definition of the lifeness of an entity, or object defined by its structure, as a scalar, namely the product of its defining information (algorithmic complexity) integrated over its lifetime. We provide a hierarchical Gaussian, yet parameter-free, dynamical filtering algorithm that can efficiently and on-the-fly fit configurations of entities constituted by moving particles to a tree structure that can model a wide range of hierarchical system entities, while extracting and calculating the complexity, lifespan, and lifeness of the entity and all of its constituting subtrees or subentities. We simulated 41 interacting particle worlds and found preliminary evidence suggesting that the lifeness of entities is associated with the distance to criticality, as roughly measured by the range of the pairwise interaction forces of the elemental particles of the worlds they inhabit. This study is a proof of concept that defining, measuring, and quantifying lifeness is (1) feasible, useful for (2) simplifying theoretical discussions, for (3) hierarchically assessing biotic properties such as number of hierarchical levels and predicted longevity and for (4) classifying both artificial and biological entities, respectively via computer simulations and a combination of evolutionary and molecular biology approaches.