Local distributional chaos
Francisco Balibrea, Lenka Ruck\'a

TL;DR
This paper explores local distributional chaos in dynamical systems, showing that in symbolic spaces all points are DC1-points and that positive entropy implies many DC1-points, while in higher dimensions chaos can be less concentrated.
Contribution
It introduces the concept of DCi-points, proves that all points in symbolic spaces are DC1-points, and demonstrates that positive entropy leads to uncountably many DC1-points, with examples in higher dimensions.
Findings
All points in symbolic space are DC1-points.
Positive topological entropy implies uncountably many DC1-points.
Higher-dimensional systems can have positive entropy without DC2-points.
Abstract
In discrete dynamical system where is a topological space and , three notions of distributional chaos were defined. They were denoted by and . For interval systems such three notions coincide and they will be denoted by DC-chaos. Generally speaking we have -chaos. We wonder if it is possible that chaos can concentrate in some points and develop a local idea of the distributional chaos. Answering to this question, in [12] is introduced the new notion of -points for . Such special points are those in which DC-chaos of different types concentrate. Also in [12] it is proved that if is continuous interval map with positive topological entropy, then there is at least one DC1-point in the system. In this paper it is proved that in the symbolic space where is the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMathematical Dynamics and Fractals · Computability, Logic, AI Algorithms
