2 research outputs found
Emergence and algorithmic information dynamics of systems and observers
Previous work has shown that perturbation analysis in software space can
produce candidate computable generative models and uncover possible causal
properties from the finite description of an object or system quantifying the
algorithmic contribution of each of its elements relative to the whole. One of
the challenges for defining emergence is that one observer's prior knowledge
may cause a phenomenon to present itself to such observer as emergent while for
another as reducible. When attempting to quantify emergence, we demonstrate
that the methods of Algorithmic Information Dynamics can deal with the richness
of such observer-object dependencies both in theory and practice. By
formalising the act of observing as mutual algorithmic perturbation, the
emergence of algorithmic information is rendered invariant, minimal, and robust
in the face of information cost and distortion, while still observer-dependent.
We demonstrate that the unbounded increase of emergent algorithmic information
implies asymptotically observer-independent emergence, which eventually
overcomes any formal theory that an observer might devise to finitely
characterise a phenomenon. We discuss observer-dependent emergence and
asymptotically observer-independent emergence solving some previous suggestions
indicating a hard distinction between strong and weak emergence