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Evolutionary dynamics of group interactions on structured populations: A review

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and nonliving matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proven valuable for studying pattern formation, equilibrium selection, and self-organisation in evolutionary games. Here we review recent advances in the study of evolutionary dynamics of group interactions on structured populations, including lattices, complex networks and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical and social sciences, benefits from the synergy between statistical physics, network science and evolutionary game theory.

 

Evolutionary dynamics of group interactions on structured populations: A review

Matjaz Perc, Jesús Gómez-Gardeñes, Attila Szolnoki, Luis M. Floría, Yamir Moreno

http://arxiv.org/abs/1301.2247

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Complex dynamics of elementary cellular automata emerging in chaotic rules

Complex dynamics of elementary cellular automata emerging in chaotic rules | Papers | Scoop.it

We show novel techniques of analysing complex dynamics of cellular automata (CA) with chaotic behaviour. CA are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and disorder. A number of attempts have been made to classify CA functions on their spatio-temporal dynamics and to predict behavior of any given function. Examples include mechanical computation, lambda and Z-parameters, mean field theory, differential equations and number conserving features. We propose to classify CA based on their behaviour when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behaviour from almost any initial condition. Thus in just a few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory.

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