Expected Emergent Algorithmic Creativity and Integration in Dynamic Complex Networks

• Felipe S. Abrahão LNCC
• Klaus Wehmuth LNCC
• Artur Ziviani LNCC

DOI: https://doi.org/10.5753/etc.2018.3152

Resumo

We present a theoretical investigation of the emergence of complexity or irreducible information in networked computable systems when the network topology may change over time. For this purpose, we build a network model in which nodes are randomly generated Turing machines that obey a communication protocol of imitation of the fittest neighbor. Then, we show that there are topological conditions that trigger a phase transition in which eventually these networked computable systems begin to produce an unlimited amount of bits of expected emergent algorithmic complexity, creativity and integration as the network size goes to infinity.

Referências

Abrahão, F. S. (2016). Emergent algorithmic creativity on networked Turing machines. In The 8th International Workshop on Guided Self-Organization at the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE), Cancún.

Abrahão, F. S., Wehmuth, K., and Ziviani, A. (2017). Algorithmic Networks: central time to trigger expected emergent open-endedness.

Barabasi, A.-L. (2009). Scale-Free Networks: A Decade and Beyond. Science, 325(5939):412–413.

Barabási, A.-L., Albert, R., and Jeong, H. (1999). Mean-field theory for scale-free random networks. Physica A: Statistical Mechanics and its Applications, 272(1-2):173–187.

Costa, E. C., Vieira, A. B., Wehmuth, K., Ziviani, A., and da Silva, A. P. C. (2015). Time Centrality in Dynamic Complex Networks. Advances in Complex Systems, 18(07n08).

Wehmuth, K., Fleury, É., and Ziviani, A. (2016). On MultiAspect graphs. Theoretical Computer Science, 651:50–61.