scholarly journals An Information-Theoretic Framework for Complex Systems

2018 ◽  
Vol 140 (12) ◽  
pp. S16-S23
Author(s):  
Hanieh Agharazi ◽  
Wanchat Theeranaew ◽  
Kolacinski Richard M. ◽  
Kenneth A. Lopaor
Keyword(s):  
Communication Network ◽  
Complex Systems ◽  
Information Channel ◽  
Information Theoretic

We propose an information-theoretic framework for modeling complex systems as a communication network where physical devices can be organized into subsystems and subsystems are communicating through an information channel governed by the dynamics of the system.

scholarly journals On the complexity of assimilation in urban communities

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Renita Murimi
Keyword(s):  
Complex Systems ◽  
Urban Communities ◽  
Human Experience ◽  
Urban Environments ◽  
Urban Systems ◽  
Sociological Perspective ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Urban Complex ◽  
The Relationship

AbstractCities are microcosms representing a diversity of human experience. The complexity of urban systems arises from this diversity, where the services that cities offer to their inhabitants have to be tailored for their unique requirements. This paper studies the complexity of urban environments in terms of the assimilation of its communities. We examine the urban assimilation complexity with respect to the foreignness between communities and formalize the level of complexity using information-theoretic measures. Our findings contribute to a sociological perspective of the relationship between urban complex systems and the diversity of communities that make up urban systems.

scholarly journals Ergodicity reveals assistance and learning from physical human-robot interaction

2019 ◽  
Vol 4 (29) ◽  
pp. eaav6079
Author(s):  
Kathleen Fitzsimons ◽  
Ana Maria Acosta ◽  
Julius P. A. Dewald ◽  
Todd D. Murphey
Keyword(s):  
Human Perception ◽  
Human Motion ◽  
Human Robot Interaction ◽  
The Body ◽  
Human Motion Analysis ◽  
Robotic Assistance ◽  
Information Channel ◽  
Neural Encoding ◽  
Robot Interaction ◽  
Information Theoretic

This paper applies information theoretic principles to the investigation of physical human-robot interaction. Drawing from the study of human perception and neural encoding, information theoretic approaches offer a perspective that enables quantitatively interpreting the body as an information channel and bodily motion as an information-carrying signal. We show that ergodicity, which can be interpreted as the degree to which a trajectory encodes information about a task, correctly predicts changes due to reduction of a person’s existing deficit or the addition of algorithmic assistance. The measure also captures changes from training with robotic assistance. Other common measures for assessment failed to capture at least one of these effects. This information-based interpretation of motion can be applied broadly, in the evaluation and design of human-machine interactions, in learning by demonstration paradigms, or in human motion analysis.

scholarly journals Quantitative Characterization of Complex Systems—An Information Theoretic Approach

2021 ◽  
Vol 4 (4) ◽  
pp. 99
Author(s):  
Aditya Akundi ◽  
Eric Smith
Keyword(s):  
Information Theory ◽  
Complex Systems ◽  
Theoretic Approach ◽  
Systems Of Systems ◽  
Information Theoretic ◽  
Theoretical Approaches ◽  
The Social ◽  
Information Theoretic Approach

A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic two-stage examination structure for complex systems aimed towards developing an information theory-based approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains.

scholarly journals Nearly maximal information gain due to time integration in central dogma reactions

2022 ◽  
Author(s):  
Swarnavo Sarkar ◽  
Jayan Rammohan
Keyword(s):  
Rate Constants ◽  
Cellular Response ◽  
Channel Model ◽  
Information Gain ◽  
Time Integration ◽  
Information Channel ◽  
Information Theoretic ◽  
Central Dogma ◽  
New Information ◽  
In Series

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we show that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain due to time integration while also keeping the loss due to stochasticity in translation relatively low (< 0.5 bits).

scholarly journals The Search for Candidate Relevant Subsets of Variables in Complex Systems

2015 ◽  
Vol 21 (4) ◽  
pp. 412-431 ◽  
Author(s):  
M. Villani ◽  
A. Roli ◽  
A. Filisetti ◽  
M. Fiorucci ◽  
I. Poli ◽  
...  
Keyword(s):  
Complex Systems ◽  
Flow Reactor ◽  
Mapk Signaling ◽  
Boolean Networks ◽  
The Other ◽  
Information Theoretic ◽  
Random Boolean Networks ◽  
Topological Relationships ◽  
Cluster Index ◽  
System Variables

We describe a method to identify relevant subsets of variables, useful to understand the organization of a dynamical system. The variables belonging to a relevant subset should have a strong integration with the other variables of the same relevant subset, and a much weaker interaction with the other system variables. On this basis, extending previous work on neural networks, an information-theoretic measure, the dynamical cluster index, is introduced in order to identify good candidate relevant subsets. The method does not require any previous knowledge of the relationships among the system variables, but relies on observations of their values over time. We show its usefulness in several application domains, including: (i) random Boolean networks, where the whole network is made of different subnetworks with different topological relationships (independent or interacting subnetworks); (ii) leader-follower dynamics, subject to noise and fluctuations; (iii) catalytic reaction networks in a flow reactor; (iv) the MAPK signaling pathway in eukaryotes. The validity of the method has been tested in cases where the data are generated by a known dynamical model and the dynamical cluster index is applied in order to uncover significant aspects of its organization; however, it is important that it can also be applied to time series coming from field data without any reference to a model. Given that it is based on relative frequencies of sets of values, the method could be applied also to cases where the data are not ordered in time. Several indications to improve the scope and effectiveness of the dynamical cluster index to analyze the organization of complex systems are finally given.

scholarly journals Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 398
Author(s):  
Gianluca D’Addese ◽  
Laura Sani ◽  
Luca La Rocca ◽  
Roberto Serra ◽  
Marco Villani
Keyword(s):  
Dynamical Systems ◽  
Complex Systems ◽  
Origin Of Life ◽  
Asymptotic Distribution ◽  
Random Variables ◽  
Computationally Efficient ◽  
Information Theoretic ◽  
Simple Index ◽  
Formidable Challenge ◽  
The Origin Of Life

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

scholarly journals Gaze Information Channel in Van Gogh’s Paintings

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 540
Author(s):  
Qiaohong Hao ◽  
Lijing Ma ◽  
Mateu Sbert ◽  
Miquel Feixas ◽  
Jiawan Zhang
Keyword(s):  
Markov Chain ◽  
Eye Tracking ◽  
Conditional Entropy ◽  
Information Channel ◽  
Information Theoretic ◽  
First Order ◽  
The Gaze ◽  
Order Markov Chain ◽  
Computational Aesthetics ◽  
Information Theoretic Measures

This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all.

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