The paper proposes the "law of increasing functional information," a new law of nature that could help explain the evolution of complex systems across multiple scales in the universe, from atoms and molecules to stars and brains.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
The relationship between predictability and reconstructability, and how it can vary in opposite directions in complex systems. The work is based on information theory and was performed on various dynamics on random graphs, including continuous deterministic systems, and provides analytical calculations of the uncertainty coefficients for many different systems.
