Complexity Measurements and Causation for Dynamic Complex Systems
This book examines the problems of causal determinism and limited completeness in systems theory. Furthermore, the author analyzes options for complexity measurements that include systems’ autonomy and variability for causal inference—i.e., the ability to derive causal relationships from data record...
Պահպանված է:
| Հիմնական հեղինակ: | |
|---|---|
| Համատեղ հեղինակ: | |
| Ձևաչափ: | Էլեկտրոնային էլ․ գիրք |
| Լեզու: | անգլերեն |
| Հրապարակվել է: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Հրատարակություն: | 1st ed. 2025. |
| Շարք: | Understanding Complex Systems,
|
| Խորագրեր: | |
| Առցանց հասանելիություն: | https://doi.org/10.1007/978-3-031-84709-7 |
| Ցուցիչներ: |
Ավելացրեք ցուցիչ
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-84709-7 | ||
| 003 | DE-He213 | ||
| 005 | 20260906075235.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 250313s2025 sz | o |||| 0|eng d | ||
| 020 | |a 9783031847097 |9 978-3-031-84709-7 | ||
| 024 | 7 | |a 10.1007/978-3-031-84709-7 |2 doi | |
| 050 | 4 | |a Q295 | |
| 072 | 7 | |a GPFC |2 bicssc | |
| 072 | 7 | |a SCI064000 |2 bisacsh | |
| 072 | 7 | |a GPFC |2 thema | |
| 082 | 0 | 4 | |a 530.1 |2 23 |
| 100 | 1 | |a Diaz Ochoa, Juan Guillermo. |e author. |0 (orcid)0000-0002-9893-4068 |1 https://orcid.org/0000-0002-9893-4068 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Complexity Measurements and Causation for Dynamic Complex Systems |h [electronic resource] / |c by Juan Guillermo Diaz Ochoa. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XIV, 159 p. 46 illus.). |b 43 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of colour is Not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background colour |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All Non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Understanding Complex Systems, |x 1860-0840 | |
| 505 | 0 | |a Concepts of Causality and Systems theory -- A brief overview on Dynamic Complex Systems And Causal Inference -- Elastic States and Complex Dynamics in Mechanistic Models -- A cartography of complexity -- The implications of relative causal inference for the understanding of complex systems. | |
| 520 | |a This book examines the problems of causal determinism and limited completeness in systems theory. Furthermore, the author analyzes options for complexity measurements that include systems’ autonomy and variability for causal inference—i.e., the ability to derive causal relationships from data recorded as a function of time. Such complexity measures present limitations in the derivation of absolute causality in complex systems and the recognition of relative and contextual causality, with practical consequences for causal inference and modeling. Finally, the author provides concepts for relative causal determinism. As a result, new ideas are presented to explore the frontiers of systems theory, specifically in relation to biological systems and teleonomy, i.e., evolved biological purposiveness. This book is written for graduate students in physics, biology, medicine, social sciences, economics, and engineering who are seeking new concepts of causal inference applied in systems theory. It is also intended for scientists with an interest in philosophy and philosophers interested in the foundations of systems theory. Additionally, data scientists seeking new methods for the analysis of time series to extract features useful for machine learning will find this book of interest. | ||
| 532 | 8 | |a Accessibility summary: This PDF does Not fully comply with PDF/UA standards but does include limited screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a System theory. | |
| 650 | 0 | |a Dynamics. | |
| 650 | 0 | |a Nonlinear theories. | |
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 1 | 4 | |a Complex Systems. |
| 650 | 2 | 4 | |a Applied Dynamical Systems. |
| 650 | 2 | 4 | |a Data Science. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031847080 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031847103 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031863769 |
| 830 | 0 | |a Understanding Complex Systems, |x 1860-0840 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-84709-7 |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 912 | |a ZDB-2-250 | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||
| 950 | |a Focus Bundle Physics and Astronomy (SpringerNature-85420) | ||