Measuring Information Dynamics in Swarms

Miller, Jennifer M.; Wang, X. Rosalind; Lizier, Joseph T.; Prokopenko, Mikhail; Rossi, Louis F.

doi:10.1007/978-3-642-53734-9_12

Cited by 13 publications

(10 citation statements)

References 36 publications

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“…Average and local active information have been used been used to understand information storage in biological systems as diverse as swarm behavior [418][419][420], gene regulatory networks [421], neural information processing [40,41], and to evolve artificial information processing systems [415]. Application to neural information processing is particularly relevant to the cancer problem since the measurements of voltage potential tracking V mem across populations of cells is similar in data content to that acquired by fMRI of brain tissue.…”

Section: Information Storagementioning

confidence: 99%

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Moore

Walker

Levin

2017

Converg. Sci. Phys. Oncol.

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TOPICAL REVIEWCancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem IntroductionCancer is well-recognized not only as an extremely pernicious medical problem, but also as a group of phenomena deeply connected to the fundamental questions of evolution, multicellularity, pattern (disregulation, and the interplay between the genome and the environment [1,2]. Two fundamental paradigms currently divide the field. One is that cancer results from disorders of genetics: cancer cells are fundamentally broken due to genomic mutation or instability, and their clonal progeny form tumors and metastases. This view is sometimes called the SMT, somatic mutation theory [3][4][5]. A competing perspective is that of cancer as a circuit disease-a disorder of the complex biophysical, transcriptional, and epigenetic dynamics that regulate cellular state and the ability of cells to cooperate in vivo [6][7][8]. Under this view (sometimes called the tissue organization field theory or TOFT, [9,10]), cancer is akin to a traffic jam [11]-the problem is not a permanent discrete alteration within a founder cell and all of its clonal descendants, but an undesirable stable attractor in the complex network of controls that normally guides anatomical homeostasis [12].The relative merits of the two models have been discussed extensively [10,[13][14][15][16][17] AbstractThe current paradigm views cancer as arising clonally from a degradation of genetic information in single cells. A complementary perspective, originating at the dawn of modern developmental biology, is that cancer is the result of a system disorder of algorithms that normally orchestrate individual cell activities toward specific anatomical structures and away from tumorigenesis. A view of cancer as a disease of geometry focuses on the pathways that allow cells to cooperate to build and maintain large-scale anatomical patterning. Cancer may result when cells stop maintaining higher-order structures and reduce the boundary of their computational selves to a single-cell level, reverting to a unicellular lifestyle in which the rest of the organism is merely part of the environment at the expense of which all living things survive. While this view has been widely discussed, little progress has been made in providing a quantitative, mechanistic framework within which this perspective's specific and unique implications for treatment strategies can be tested and biomedically exploited. Here, we highlight two recent areas of progress which may facilitate much-needed progress on the cancer problem. First, we review the roles that endogenous bioelectrical networks, operating across many tissues in vivo, play as a medium of information processing in tumor suppression, progression, and reprogramming. Second, we provide a primer to the development of computational theory and tools for quantifying the information and causal control structures in cancer and other complex biological systems. Rigorous mathematical formalisms now exist to...

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Section: Information Storagementioning

confidence: 99%

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Moore

Walker

Levin

2017

Converg. Sci. Phys. Oncol.

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“…We could use the principles in information theory to solve the problem in self-organization. He also gave two examples of the application of the information theory in self-organization systems: and Transfer Entropy (TE) [40,22]. This work discovers how an individual's position is related to its information processing role and provides the first information-theoretic evidence that the information cascades occur in waves rippling through the swarm.…”

Section: Case Study Ii: Extension For Two Individuals Systemmentioning

confidence: 98%

Modeling and Analyzing Large Swarms with Covert Leaders

Sun

Rossi

Luan

et al. 2013

2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems

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“…Information theoretic measures such as information storage or information modification could prove useful to CASN analysis as well. Although these ideas require more research relative to CASN specifically, other disciplines have found correlations between information theoretic measures and the concepts of self-organization and emergence [32,33], network stability [34,35], interdependencies [36], and distributed computation [37] in many types of networks. Being able to quantify these behaviors and characteristics for CASN would enable significant advances in a research area that has been primarily grounded in empirical data, models and simulations, and social network analysis.…”

Section: Transfer Entropy Applicationmentioning

confidence: 99%

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

Rodewald

Colombi

Oyama

et al. 2016

Entropy

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Abstract:Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure's dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN's self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system's structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.

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Measuring Information Dynamics in Swarms

Cited by 13 publications

References 36 publications

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

Modeling and Analyzing Large Swarms with Covert Leaders

Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

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