scholarly journals Bundled Causal History Interaction

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 360
Author(s):  
Peishi Jiang ◽  
Praveen Kumar
Keyword(s):  
Complex Systems ◽  
Evolutionary Dynamics ◽  
Graphical Model ◽  
Partial Information ◽  
Synergistic Effects ◽  
Nonlinear Interactions ◽  
Stream Chemistry ◽  
Multivariate System ◽  
Complex Dependence ◽  
Temporal Interactions

Complex systems arise as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: How do two non-overlapping multivariate subsets of variables interact to causally determine the outcome of a specific variable? Here, we provide an information-based approach to address this problem. We delineate the temporal interactions between the bundles in a probabilistic graphical model. The strength of the interactions, captured by partial information decomposition, then exposes complex behavior of dependencies and memory within the system. The proposed approach successfully illustrated complex dependence between cations and anions as determinants of pH in an observed stream chemistry system. In the studied catchment, the dynamics of pH is a result of both cations and anions through mainly synergistic effects of the two and their individual influences as well. This example demonstrates the potentially broad applicability of the approach, establishing the foundation to study the interaction between groups of variables in a range of complex systems.

scholarly journals NATIONAL FINANCIAL SYSTEM ANALYSIS IN TERMS OF THE COMPLEX SYSTEMS THEORY

2020 ◽  
Author(s):  
Viorica Lopotenco
Keyword(s):  
Complex Systems ◽  
System Analysis ◽  
Synergistic Effects ◽  
International Monetary System ◽  
Quality Level ◽  
Financial Systems ◽  
Complex Systems Theory ◽  
Monetary System ◽  
Stability Of Development ◽  
Development Trajectories

The recession caused by the pandemic and the vulnerabilities faced by the entire international monetary system and the national financial systems requires a particular approach to analyzing the current situation and the design of new developments. Based on these arguments, we set out to investigate national financial systems from the perspective of complex systems theory.Following the research, we concluded that understanding the nature and characteristics of the manifestation of synergistic effects allows organizing the financial system's management at a new quality level, based on the concepts of discretion and stability of development trajectories of the world economy.

Chaos Theory and Complexity Theory

2013 ◽  
Author(s):  
Keith Warren
Keyword(s):  
Nonlinear Dynamics ◽  
Dynamical Systems ◽  
Complex Systems ◽  
Systems Theory ◽  
Complexity Theory ◽  
Chaos Theory ◽  
Mathematical Framework ◽  
Nonlinear Interactions ◽  
Simple Systems ◽  
Over Time

Chaos theory and complexity theory, collectively known as nonlinear dynamics or dynamical systems theory, provide a mathematical framework for thinking about change over time. Chaos theory seeks an understanding of simple systems that may change in a sudden, unexpected, or irregular way. Complexity theory focuses on complex systems involving numerous interacting parts, which often give rise to unexpected order. The framework that encompasses both theories is one of nonlinear interactions between variables that give rise to outcomes that are not easily predictable. This entry provides a nonmathematical introduction, discussion of current research, and references for further reading.

Evolution Towards Criticality in Ising Neural Agents

2020 ◽  
Vol 26 (1) ◽  
pp. 112-129 ◽  
Author(s):  
Sina Khajehabdollahi ◽  
Olaf Witkowski
Keyword(s):  
Complex Systems ◽  
Critical State ◽  
Regulatory Networks ◽  
Evolutionary Dynamics ◽  
Subcritical State ◽  
Experimental Conditions ◽  
Supercritical Regime ◽  
Gene Regulatory ◽  
Human Brains ◽  
Clear Description

Criticality is thought to be crucial for complex systems to adapt at the boundary between regimes with different dynamics, where the system may transition from one phase to another. Numerous systems, from sandpiles to gene regulatory networks to swarms to human brains, seem to work towards preserving a precarious balance right at their critical point. Understanding criticality therefore seems strongly related to a broad, fundamental theory for the physics of life as it could be, which still lacks a clear description of how life can arise and maintain itself in complex systems. In order to investigate this crucial question, we model populations of Ising agents competing for resources in a simple 2D environment subject to an evolutionary algorithm. We then compare its evolutionary dynamics under different experimental conditions. We demonstrate the utility that arises at a critical state and contrast it with the behaviors and dynamics that arise far from criticality. The results show compelling evidence that not only is a critical state remarkable in its ability to adapt and find solutions to the environment, but the evolving parameters in the agents tend to flow towards criticality if starting from a supercritical regime. We present simulations showing that a system in a supercritical state will tend to self-organize towards criticality, in contrast to a subcritical state, which remains subcritical though it is still capable of adapting and increasing its fitness.

scholarly journals On a class of integro-differential equations modeling complex systems with nonlinear interactions

2012 ◽  
Vol 25 (3) ◽  
pp. 490-495 ◽  
Author(s):  
L. Arlotti ◽  
E. De Angelis ◽  
L. Fermo ◽  
M. Lachowicz ◽  
N. Bellomo
Keyword(s):  
Differential Equations ◽  
Complex Systems ◽  
Nonlinear Interactions

scholarly journals On the modeling of nonlinear interactions in large complex systems

2010 ◽  
Vol 23 (11) ◽  
pp. 1372-1377 ◽  
Author(s):  
N. Bellomo ◽  
C. Bianca ◽  
M.S. Mongiovì
Keyword(s):  
Complex Systems ◽  
Nonlinear Interactions ◽  
Large Complex

The Future of the Science of Complex Systems?

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Scaling Laws ◽  
Evolutionary Dynamics ◽  
First Century ◽  
Mathematical Framework ◽  
New Directions ◽  
The Future ◽  
Universality Classes ◽  
Century Science

The chapter is a mini outlook on the field. The classic achievenments in complexity science are mentioned, and we summarize how the new directions contained in this book might open new doors into a truly twenty-first-century science of complex systems.We do that by clarifying the origin of scaling laws, in particular for driven non-equilibrium systems, deriving the statistics of driven systems on the basis of driving and relaxing processes, categorizing probabilistic complex systems into universality classes, by developing ways for meaningful generalizations of statistical mechanics, and information theory so that they become useful for complex systems, and finally, by unifying the different approaches to evolution and co-evolution into a single mathematical framework that can serve as the basis for understanding co-evolutionary dynamics of states and interactions. We comment on our view of the role of artificial intelligence and our opinion on the future of science of complex systems.

scholarly journals A Path-Based Partial Information Decomposition

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 952
Author(s):  
David Sigtermans
Keyword(s):  
Information Theory ◽  
Mutual Information ◽  
Graphical Model ◽  
Partial Information ◽  
Communication Channels ◽  
Negative Information ◽  
Information Measure ◽  
Probability Mass ◽  
Information Components ◽  
Mass Functions

Based on the conceptual basis of information theory, we propose a novel mutual information measure—‘path-based mutual information’. This information measure results from the representation of a set of random variables as a probabilistic graphical model. The edges in this graph are modeled as discrete memoryless communication channels, that is, the underlying data is ergodic, stationary, and the Markov condition is assumed to be applicable. The associated multilinear stochastic maps, tensors, transform source probability mass functions into destination probability mass functions. This allows for an exact expression of the resulting tensor of a cascade of discrete memoryless communication channels in terms of the tensors of the constituting communication channels in the paths. The resulting path-based information measure gives rise to intuitive, non-negative, and additive path-based information components—redundant, unique, and synergistic information—as proposed by Williams and Beer. The path-based redundancy satisfies the axioms postulated by Williams and Beer, the identity axiom postulated by Harder, and the left monotonicity axiom postulated Bertschinger. The ordering relations between redundancies of different joint collections of sources, as captured in the redundancy lattices of Williams and Beer, follow from the data processing inequality. Although negative information components can arise, we speculate that these either result from unobserved variables, or from adding additional sources that are statistically independent from all other sources to a system containing only non-negative information components. This path-based approach illustrates that information theory provides the concepts and measures for a partial information decomposition.

scholarly journals Automatic Emergence Detection in Complex Systems

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-24 ◽  
Author(s):  
Eugene Santos ◽  
Yan Zhao
Keyword(s):  
Complex Systems ◽  
Graphical Model ◽  
Central Element ◽  
Knowledge Bases ◽  
Emergent Properties ◽  
Single Complex ◽  
Definition Of ◽  
Quantitative Definition ◽  
Common Variable ◽  
Baseline Approach

Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent) system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs), learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble) on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.

Evolutionary Dynamics of Inter-firm Networks: A Complex Systems Perspective

2008 ◽  
pp. 67-129 ◽  
Author(s):  
Giovanni Battista Dagnino ◽  
Gabriella Levanti ◽  
Arabella Mocciaro Li Destri
Keyword(s):  
Complex Systems ◽  
Evolutionary Dynamics ◽  
Systems Perspective ◽  
Firm Networks

System analysis in management

2017 ◽  
Author(s):  
Булыгина ◽  
Ol'ga Bulygina ◽  
Емельянов ◽  
Aleksandr Emel'yanov ◽  
Емельянова ◽  
...  
Keyword(s):  
Complex Systems ◽  
Information Management ◽  
Management System ◽  
System Analysis ◽  
Evolutionary Dynamics ◽  
Operational Calculus ◽  
Information Management System ◽  
Life Cycles ◽  
Information Control ◽  
Order Intervals

The book gives current scientific positions of the system analysis methodology on the basis of various models and scales, both in deterministic conditions and under conditions of uncertainty and risks. Models of complex systems are considered. Classification of types of systems modeling is presented. The principles of constructing scales are considered: nominal type, order, intervals models of complex systems are formulated. Stages of construction of mathematical model of system are made. The main types of measurement, ratios, differences and absolute scale. Methods for processing characteristics measured in different scales. Life cycles and empirical laws of evolution, engineering and reengineering of developing management information systems, patterns of evolution of such systems, bifurcation phenomena and the importance of attractor development constraints are analyzed. The problems of management of innovative projects using fuzzy logic methods, fuzzy algorithms (Mamdani, Larsen, Sugeno and Tsukamoto), fuzzy growing pyramidal networks and elements of artificial intelligence are considered. Some methods for analyzing and assessing the sustainability of managing risky investment projects on the basis of the theory of the function of a complex variable, operational calculus, actor-network theory, imitation modeling, and the construction of a stability curve are presented. Empirical laws of evolutionary dynamics of information-control systems are considered. Some methods for modeling defect management processes in a developing information management system are presented, including mathematical modeling using the Kendall method. The stochastic network model of the evolution of the information-control system is analyzed. Interpretation and evaluation of the penetration of defects into the system is given. Management decisions and budgeting of the information management system are analyzed.

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