LEADER 04037nam 22007695 450 001 9910987690103321 005 20251106120247.0 010 $a9783031847097 010 $a3031847091 024 7 $a10.1007/978-3-031-84709-7 035 $a(CKB)37876816200041 035 $a(DE-He213)978-3-031-84709-7 035 $a(MiAaPQ)EBC31958877 035 $a(Au-PeEL)EBL31958877 035 $a(EXLCZ)9937876816200041 100 $a20250313d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComplexity Measurements and Causation for Dynamic Complex Systems /$fby Juan Guillermo Diaz Ochoa 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XIV, 159 p. 46 illus., 43 illus. in color.) 225 1 $aUnderstanding Complex Systems,$x1860-0840 311 08$a9783031847080 311 08$a3031847083 327 $aConcepts 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. 330 $aThis book examines the problems of causal determinism and limited completeness in systems theory. Furthermore, the author analyzes options for complexity measurements that include systems? autonomy and variability for causal inference?i.e., the ability to derive causal relationships from data recorded as a function of time. Such complexity measures present limitations in the derivation of absolute causality in complex systems and the recognition of relative and contextual causality, with practical consequences for causal inference and modeling. Finally, the author provides concepts for relative causal determinism. As a result, new ideas are presented to explore the frontiers of systems theory, specifically in relation to biological systems and teleonomy, i.e., evolved biological purposiveness. This book is written for graduate students in physics, biology, medicine, social sciences, economics, and engineering who are seeking new concepts of causal inference applied in systems theory. It is also intended for scientists with an interest in philosophy and philosophers interested in the foundations of systems theory. Additionally, data scientists seeking new methods for the analysis of time series to extract features useful for machine learning will find this book of interest. 410 0$aUnderstanding Complex Systems,$x1860-0840 606 $aSystem theory 606 $aDynamics 606 $aNonlinear theories 606 $aArtificial intelligence$xData processing 606 $aComplex Systems 606 $aApplied Dynamical Systems 606 $aData Science 606 $aTeoria de sistemes$2thub 606 $aDinàmica$2thub 606 $aTeories no lineals$2thub 606 $aIntel·ligència artificial$2thub 606 $aProcessament de dades$2thub 606 $aSistemes complexos$2thub 608 $aLlibres electṛnics$2thub 615 0$aSystem theory. 615 0$aDynamics. 615 0$aNonlinear theories. 615 0$aArtificial intelligence$xData processing. 615 14$aComplex Systems. 615 24$aApplied Dynamical Systems. 615 24$aData Science. 615 7$aTeoria de sistemes 615 7$aDinàmica 615 7$aTeories no lineals 615 7$aIntel·ligència artificial 615 7$aProcessament de dades 615 7$aSistemes complexos 676 $a530.1 700 $aDiaz Ochoa$b Juan Guillermo$4aut$4http://id.loc.gov/vocabulary/relators/aut$01803293 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910987690103321 996 $aComplexity Measurements and Causation for Dynamic Complex Systems$94350082 997 $aUNINA