An Open-Ended Approach to Understanding Local, Emergent Conservation Laws in Biological Evolution

TL;DR

This paper explores how internal system constraints can lead to emergent conservation laws and rules in biological evolution, offering insights into open-endedness and the continual discovery of new states and rules.

Contribution

It introduces a framework for understanding how internal constraints generate emergent rules, contrasting with external constraint models, with implications for AI and biological systems.

Findings

Constraints can be classified as conserved, quasi-conserved, or conditionally conserved.

Emergent conservation laws can lead to new system rules and dynamics.

Internal constraints are key to understanding open-ended evolution.

Abstract

While fields like Artificial Life have made huge strides in quantifying the mechanisms that distinguish living systems from non-living ones, particular mechanisms remain difficult to reproduce in silico. Known as open-endedness, we've been successful in finding mechanisms that generate new states, but have been less successful in finding mechanisms that generate new rules. Here, we weigh whether or not analyzing the effects of internal and external system constraints on a system's dynamics would be a fruitful avenue to understanding open-endedness. We discuss the connection between physical constraints and the ways that the system can physically reach possible states while those constraints are present. It seems that the physical constraints that define biological objects (and dynamics) are maintained by dynamics that occur from within the system. This is in opposition to current modeling approaches where system constraints are maintained externally. We suggest that constraints can be characterized as variables whose values are either completely conserved, quasi-conserved, or conditionally conserved. Regardless of whether or not a constrained variable is a part of the biological object or present in the object's environment, we discuss how the accessible system states under that constraint can lead to local, emergent conservation laws (rules), with examples. Finally, we discuss the possible benefits of formally understanding how system constraints that emerge from within a system lead to system dynamics that can be characterized as new, emergent rules -- particularly for artificial intelligence, hybrid life, embodiment, astrobiology, and more. Understanding how new, local rules might emerge from within the system is crucial for understanding how open-ended systems continually discover new update rules, in addition to continually discovering new states.