A METHOD FOR INFERRING HIERARCHICAL DYNAMICS IN STOCHASTIC PROCESSES

Olof Görnerup () and Martin Nilsson Jacobi ()
Additional contact information
Olof Görnerup: Complex Systems Group, Department of Energy and Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden
Martin Nilsson Jacobi: Complex Systems Group, Department of Energy and Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden

Advances in Complex Systems (ACS), 2008, vol. 11, issue 01, 1-16

Abstract: Complex systems may often be characterized by their hierarchical dynamics. In this paper we present a method and an operational algorithm that automatically infer this property in a broad range of systems — discrete stochastic processes. The main idea is to systematically explore the set of projections from the state space of a process to smaller state spaces, and to determine which of the projections impose Markovian dynamics on the coarser level. These projections, which we callMarkov projections, then constitute the hierarchical dynamics of the system. The algorithm operates on time series or other statistics, soa prioriknowledge of the intrinsic workings of a system is not required in order to determine its hierarchical dynamics. We illustrate the method by applying it to two simple processes — a finite state automaton and an iterated map.

Keywords: Hierarchical dynamics; model reduction; coarse-graining (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525908001507
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:11:y:2008:i:01:n:s0219525908001507

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525908001507

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().