  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.
