Emergence of complex data from simple local rules in a network game

As one of the main subjects of investigation in data science, network science has been demonstrated a wide range of applications to real-world networks analysis and modeling. For example, the pervasive presence of structural or topological characteristics, such as the small-world phenomenon, small-diameter, scale-free properties, or fat-tailed degree distribution were one of the underlying pillars fostering the study of complex networks. Relating these phenomena with other emergent properties in complex systems became a subject of central importance. By introducing new implications on the interface between data science and complex systems science with the purpose of tackling some of these issues, in this article we present a model for a network game played by complex networks in which nodes are computable systems. In particular, we present and discuss how some network topological properties and simple local communication rules are able to generate a phase transition with respect to the emergence of incompressible data.

[1]  Artur Ziviani,et al.  Emergent Open-Endedness from Contagion of the Fittest , 2018, Complex Syst..

[2]  Hector Zenil,et al.  A Review of Graph and Network Complexity from an Algorithmic Information Perspective , 2018, Entropy.

[3]  Joseph T. Lizier,et al.  Information Decomposition of Target Effects from Multi-Source Interactions: Perspectives on Previous, Current and Future Work , 2018, Entropy.

[4]  Artur Ziviani,et al.  Avoiding Spurious Paths in Centralities Based on Shortest Paths in High Order Networks , 2018, 2018 Eighth Latin-American Symposium on Dependable Computing (LADC).

[5]  Albert-Lszl Barabsi,et al.  Network Science , 2016, Encyclopedia of Big Data.

[6]  Hector Zenil,et al.  Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems , 2016, Scientific Reports.

[7]  Artur Ziviani,et al.  Algorithmic Networks: central time to trigger expected emergent open-endedness , 2017, Theor. Comput. Sci..

[8]  Melanie Mitchell,et al.  Complexity - A Guided Tour , 2009 .

[9]  Gregory J. Chaitin,et al.  A Philosophical Perspective on a Metatheory of Biological Evolution , 2018 .

[10]  Felipe S. Abrahão,et al.  Metabiología: los orígenes de la creatividad biológica , 2014 .

[11]  Paul G. Spirakis,et al.  Elements of the theory of dynamic networks , 2018, Commun. ACM.

[12]  Itala M. Loffredo D'Ottaviano,et al.  Learning the undecidable from networked systems , 2019, Unravelling Complexity.

[13]  Hector Zenil,et al.  Algorithmic information and incompressibility of families of multidimensional networks. , 2018 .

[14]  Marcus Hutter,et al.  Algorithmic Information Theory , 1977, IBM J. Res. Dev..

[15]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[16]  Hector Zenil,et al.  Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification. , 2018, 1802.05843.

[17]  Hector Zenil,et al.  Causal deconvolution by algorithmic generative models , 2019, Nature Machine Intelligence.

[18]  Gregory Chaitin,et al.  Life as Evolving Software , 2012 .

[19]  Eric Fleury,et al.  On MultiAspect graphs , 2014, Theor. Comput. Sci..

[20]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[21]  Hector Zenil,et al.  Undecidability and Irreducibility Conditions for Open-Ended Evolution and Emergence , 2016, Artificial Life.

[22]  Mario Villalobos,et al.  Enactive autonomy in computational systems , 2017, Synthese.

[23]  Mikhail Prokopenko,et al.  An information-theoretic primer on complexity, self-organization, and emergence , 2009, Complex..

[24]  Alex Borges Vieira,et al.  Time Centrality in Dynamic Complex Networks , 2015, Adv. Complex Syst..

[25]  Felipe S. Abrahão The "paradox" of computability and a recursive relative version of the Busy Beaver function , 2016, ArXiv.

[26]  Hector Zenil,et al.  An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems , 2017, bioRxiv.

[27]  Hector Zenil,et al.  Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity , 2017, Royal Society Open Science.