Name
Abstract | ||
|---|---|---|
This paper presents a theoretical investigation of the general problem of emergent irreducible information in networked populations of computable systems. In particular, we narrow our scope to study this problem in algorithmic networks composed of randomly generated Turing machines that follow a susceptible-infected-susceptible contagion model of imitation of the fittest neighbor. We show that there is a lower bound for the stationary prevalence (i.e., the average density of infected nodes by the fittest nodes) that triggers expected (local) emergent open-endedness, that is, that triggers an unlimited increase of the expected local emergent algorithmic complexity (or information) of a node as the population size grows. In addition, we show that static networks with a power-law degree distribution following the Barabasi-Albert model satisfy this lower bound and thus display expected (local) emergent open-endedness. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.25088/ComplexSystems.27.4.369 | COMPLEX SYSTEMS |
Keywords | Field | DocType |
emergence, algorithmic information, spreading, complex networks, distributed systems | Cognitive science,Survival of the fittest,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 4 | 0891-2513 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felipe S. Abrahão | 1 | 0 | 0.68 |
| Klaus Wehmuth | 2 | 70 | 10.17 |
| Artur Ziviani | 3 | 646 | 56.62 |