Imperfect Automata:
Effects of mutation on the evolution of automaton 01101110 (i.e., Rule 110)
Python, Tkinter
In biological systems, novelty is made possible by spontaneous variability – i.e., mutation (Thomas, 1974), a concept central to my computational work on learning and emergence (Popa, 2013, 2019; Popa & McDowell, 2016).
Unlike biological systems, CAs are deterministic systems in which the state of each cell is fully determined by the precise rules that define the automaton, which means that structures cannot, in principle, emerge from non-structures.
This got me thinking: what if we allow automata to make mistakes? If, every time a cell is created, there’s a small probability to write 0 instead of 1 and vice-versa, will structures spawn out of noise, like the one originating in the red circle?
The Emergence of You
Python, Matplolib
When did you become you?
How does society influence biology?
Where do genes interact with families?
How do nature and nurture mediate each other?
Physical forces acting on particles explain how physical systems change over time. Evolutionary forces acting on populations of genomes explain change in the genetic structure of populations across generations. The dynamics of human development - i.e., learning, or change in psychological systems, are not yet understood. This is a step in that direction.