Emergence and algorithmic information dynamics of systems and observers
Felipe S. Abrah\~ao, Hector Zenil

TL;DR
This paper formalizes how emergence in systems depends on the observer's knowledge and introduces observer-dependent and asymptotic observer-independent emergence based on algorithmic information theory.
Contribution
It develops a formal framework linking observer knowledge to emergence and introduces the concepts of ODE and AOIE within algorithmic information dynamics.
Findings
Emergence depends on observer knowledge but is robust to language and measurement choices.
AOIE represents emergence invariant across all formal theories.
Models demonstrate both diachronic and holistic AOIE phenomena.
Abstract
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. By formalising the act of observing as mutual perturbations between dynamical systems, we demonstrate that emergence of algorithmic information do depend on the observer's formal knowledge, while robust to other subjective factors, particularly: the choice of the programming language and the measurement method; errors or distortions during the information acquisition; and the informational cost of processing.…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Cellular Automata and Applications · Distributed systems and fault tolerance
