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Journal articles on the topic 'Information dynamics'

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Published: 4 June 2021
Last updated: 5 February 2022

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1

Golan, Amos. "Information Dynamics." Minds and Machines 24, no. 1 (October 1, 2013): 19–36. http://dx.doi.org/10.1007/s11023-013-9326-2.

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2

Stevens, Ronald, Trysha Galloway, Ann Willemson-Dunlap, Jamie Gorman, and Donald Halpin. "Making Sense of Team Information." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 114–18. http://dx.doi.org/10.1177/1541931218621026.

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This paper describes how meaning can be extracted from large-scale dynamical data to make inferences about teamwork that are useful in both the theoretical and practical sense. The dynamics of an anesthesiology team are viewed from the perspectives of: 1) changes in the team’s neurodynamic organizations with large and small changes in the task; 2) how team member’s neurodynamics contribute to team neurodynamics; 3) the relationships between task events, heart-rate and neural dynamic organizations; 4) the linkages between speech flow, team and team member neurodynamics and topic discussions during Debriefing; and, 5) the micro-scale neural dynamics reflecting the involvement of the parietal lobes and gamma frequencies. These examples show how different sources of team data can contribute to multi-modal understandings of individual and teams dynamics that span micro and macro scales of teamwork.
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3

Kadomtsev, Boris B. "Dynamics and information." Uspekhi Fizicheskih Nauk 164, no. 5 (1994): 449. http://dx.doi.org/10.3367/ufnr.0164.199405a.0449.

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4

Kadomtsev, Boris B. "Dynamics and information." Physics-Uspekhi 37, no. 5 (May 31, 1994): 425–99. http://dx.doi.org/10.1070/pu1994v037n05abeh000109.

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5

Zenil, Hector, Narsis Kiani, Felipe Abrahão, and Jesper Tegnér. "Algorithmic Information Dynamics." Scholarpedia 15, no. 7 (2020): 53143. http://dx.doi.org/10.4249/scholarpedia.53143.

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6

Fox, Robert. "Information fluid dynamics." OCLC Systems & Services: International digital library perspectives 27, no. 2 (May 30, 2011): 87–94. http://dx.doi.org/10.1108/10650751111135382.

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7

CIORUŢA, Alin-Andrei, and Bogdan CIORUŢA. "REGARDING THE POPULATION DYNAMICS INVESTIGATION USING ENVIRONMENTAL INFORMATION SYSTEMS." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (June 24, 2016): 411–16. http://dx.doi.org/10.19062/2247-3173.2016.18.1.56.

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8

Warren, Kenneth S. "FROM INFORMATION STATICS TO INFORMATION DYNAMICS." Infectious Disease Clinics of North America 9, no. 2 (June 1995): 367–75. http://dx.doi.org/10.1016/s0891-5520(20)30666-8.

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9

Sun, Zejun, Jinfang Sheng, Bin Wang, Aman Ullah, and FaizaRiaz Khawaja. "Identifying Communities in Dynamic Networks Using Information Dynamics." Entropy 22, no. 4 (April 9, 2020): 425. http://dx.doi.org/10.3390/e22040425.

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Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.
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10

Matsuno, Koichiro. "Dynamics of time and information in dynamic time." Biosystems 46, no. 1-2 (April 1998): 57–71. http://dx.doi.org/10.1016/s0303-2647(97)00081-6.

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11

Coghlan, David. "The Interlevel Dynamics of Information Technology." Journal of Information Technology 13, no. 2 (June 1998): 139–49. http://dx.doi.org/10.1177/026839629801300206.

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Levels of analysis are common frameworks for describing organizational dynamics, both in the general organizational and the information technology (IT)-organizational literature. Levels of analysis – individual, group, intergroup and organizational – are typically presented as constructs under which particular organizational dynamics can be grouped and analysed. Rarely is there any emphasis placed on how events on one level can affect the dynamics of other levels. This article introduces Rashford and Coghlan's framework of organizational levels which describes levels as systemically interrelated and interdependent and, through examples, argues that the IT–organizational literature needs to adopt a more dynamic construct of levels of analysis to account for how IT affects individuals, teams, interteam coordination and organizations concurrently.
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12

Hahn, F. H. "INFORMATION, DYNAMICS AND EQUILIBRIUM*." Scottish Journal of Political Economy 34, no. 4 (November 1987): 321–34. http://dx.doi.org/10.1111/j.1467-9485.1987.tb00290.x.

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13

Lewis-Swan, R. J., A. Safavi-Naini, A. M. Kaufman, and A. M. Rey. "Dynamics of quantum information." Nature Reviews Physics 1, no. 10 (August 28, 2019): 627–34. http://dx.doi.org/10.1038/s42254-019-0090-y.

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14

Dinlersoz, Emin M., and Mehmet Yorukoglu. "Information and Industry Dynamics." American Economic Review 102, no. 2 (April 1, 2012): 884–913. http://dx.doi.org/10.1257/aer.102.2.884.

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This paper develops a model of industry dynamics where firms compete to acquire customers over time by disseminating information about themselves in the presence of random shocks to their efficiency. The properties of the model's stationary equilibrium are related to empirical regularities on firm and industry dynamics. As an application of the model, the effects of a decline in the cost of information dissemination on firm and industry dynamics are explored. (JEL D11, D83, L11, L81, M37)
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15

Bagnoli, Mark, and Susan G. Watts. "Information acquisition, information release and trading dynamics." Journal of Financial Markets 1, no. 2 (August 1998): 221–52. http://dx.doi.org/10.1016/s1386-4181(97)00008-6.

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16

Wang, Jian, Xiaolin Qin, and Hongying Fang. "Virus-Information Coevolution Spreading Dynamics on Multiplex Networks." Complexity 2021 (March 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/6624612.

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Virus and information spreading dynamics widely exist in complex systems. However, systematic study still lacks for the interacting spreading dynamics between the two types of dynamics. This paper proposes a mathematical model on multiplex networks, which considers the heterogeneous susceptibility and infectivity in two subnetworks. By using a heterogeneous mean-field theory, we studied the dynamic process and outbreak threshold of the system. Through extensive numerical simulations on artificial networks, we find that the virus’s spreading dynamics can be suppressed by increasing the information spreading probability, decreasing the protection power, or decreasing the susceptibility and infectivity.
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17

Lizier, Joseph T., Siddharth Pritam, and Mikhail Prokopenko. "Information Dynamics in Small-World Boolean Networks." Artificial Life 17, no. 4 (October 2011): 293–314. http://dx.doi.org/10.1162/artl_a_00040.

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Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity.
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18

Watanabe, S., and H. Ogawa. "1SD51 Network dynamics and information." Seibutsu Butsuri 45, supplement (2005): S14. http://dx.doi.org/10.2142/biophys.45.s14_1.

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19

Melgarejo, Miguel, and Nelson Obregon. "Information Dynamics in Urban Crime." Entropy 20, no. 11 (November 14, 2018): 874. http://dx.doi.org/10.3390/e20110874.

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Information production in both space and time has been highlighted as one of the elements that shapes the footprint of complexity in natural and socio-technical systems. However, information production in urban crime has barely been studied. This work copes with this problem by using multifractal analysis to characterize the spatial information scaling in urban crime reports and nonlinear processing tools to study the temporal behavior of this scaling. Our results suggest that information scaling in urban crime exhibits dynamics that evolve in low-dimensional chaotic attractors, and this can be observed in several spatio-temporal scales, although some of them are more favorable than others. This evidence has practical implications in terms of defining the characteristic scales to approach urban crime from available data and supporting theoretical perspectives about the complexity of urban crime.
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20

Ciucci, Davide. "Temporal Dynamics in Information Tables." Fundamenta Informaticae 115, no. 1 (2012): 57–74. http://dx.doi.org/10.3233/fi-2012-640.

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21

Enßlin, Torsten A. "Turbulence via information field dynamics." Proceedings of the International Astronomical Union 11, A29B (August 2015): 730. http://dx.doi.org/10.1017/s1743921316006554.

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AbstractTurbulent flows exhibit scale-free regimes, for which information on the statistical properties of the dynamics exists for many length-scales. The simulation of turbulent systems can benefit from the inclusion of such information on sub-grid process. How can statistical information about the flow on small scales be optimally incorporated into simulation schemes? Information field dynamics (IFD) is a novel information theoretical framework to design schemes that exploit such statistical knowledge on sub-grid flow fluctuations.
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22

Goranson, Ted. "Soft mathematics and information dynamics." Biosystems 46, no. 1-2 (April 1998): 163–67. http://dx.doi.org/10.1016/s0303-2647(97)00094-4.

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23

Suo, Qi, Jin-Li Guo, and Ai-Zhong Shen. "Information spreading dynamics in hypernetworks." Physica A: Statistical Mechanics and its Applications 495 (April 2018): 475–87. http://dx.doi.org/10.1016/j.physa.2017.12.108.

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24

VERONA, FABIO. "Investment Dynamics with Information Costs." Journal of Money, Credit and Banking 46, no. 8 (November 21, 2014): 1627–56. http://dx.doi.org/10.1111/jmcb.12161.

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25

NAG, TANAY, AMIT DUTTA, and AYOTI PATRA. "QUENCHING DYNAMICS AND QUANTUM INFORMATION." International Journal of Modern Physics B 27, no. 01n03 (November 26, 2012): 1345036. http://dx.doi.org/10.1142/s0217979213450367.

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We review recent studies on the measures of zero temperature quantum correlations namely, the quantum entanglement (concurrence) and discord present in the final state of a transverse XY spin chain following a quench through quantum critical points; the aim of these studies is to explore the scaling of the above quantities as a function of the quench rate. A comparative study between the concurrence and the quantum discord shows that their behavior is qualitatively the same though there are quantitative differences. For the present model, the scaling of both the quantities are given by the scaling of the density of the defect present in the final state though one cannot find a closed form expression for the discord. We also extend our study of quantum discord to a transverse Ising chain in the presence of a three spin interaction. Finally, we present a study of the dynamical evolution of quantum discord and concurrence when two central qubits, initially prepared in a Werner state, are coupled to the environmental XY spin chain which is driven through quantum critical points. The qualitative behavior of quantum discord and concurrence are found to be similar as that of the decoherence factor.
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26

de Fontnouvelle, Patrick. "INFORMATION DYNAMICS IN FINANCIAL MARKETS." Macroeconomic Dynamics 4, no. 2 (June 2000): 139–69. http://dx.doi.org/10.1017/s1365100500015017.

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A noisy rational expectations model of asset trading is extended to incorporate costs of information acquisition and expectation formation. Because of the information costs, how much information to acquire becomes an important decision. Agents make this decision by choosing an expectations strategy about the future value of information. Because expectation formation is costly, agents often choose strategies that are simpler (and thus cheaper) than rational expectations. The model's dynamics can be expressed in terms of the market precision, which represents the amount of information acquired by the average agent. Under certain conditions, market precision follows an unstable and highly irregular time path. This irregularity directly affects observable market quantities. In particular, simulated time series for return volatility and trading volume display a copersistence similar to that found in actual financial data.
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27

Kobayashi, Tetsuya J., and Atsushi Kamimura. "Dynamics of intracellular information decoding." Physical Biology 8, no. 5 (August 10, 2011): 055007. http://dx.doi.org/10.1088/1478-3975/8/5/055007.

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28

Chinoy, Bilal. "Dynamics of internet routing information." ACM SIGCOMM Computer Communication Review 23, no. 4 (October 1993): 45–52. http://dx.doi.org/10.1145/167954.166242.

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29

Whiting, Stewart. "Temporal Dynamics in Information Retrieval." ACM SIGIR Forum 50, no. 1 (June 27, 2016): 97–98. http://dx.doi.org/10.1145/2964797.2964818.

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30

Martin, M. T., J. Perez, and A. Plastino. "Fisher information and nonlinear dynamics." Physica A: Statistical Mechanics and its Applications 291, no. 1-4 (March 2001): 523–32. http://dx.doi.org/10.1016/s0378-4371(00)00531-8.

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31

Hawkins, Raymond J., Masanao Aoki, and B. Roy Frieden. "Asymmetric information and macroeconomic dynamics." Physica A: Statistical Mechanics and its Applications 389, no. 17 (September 2010): 3565–71. http://dx.doi.org/10.1016/j.physa.2010.04.032.

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32

THACKER, W. C., and R. LEWANDOWICZ. "Dynamics of information and uncertainty." Tellus A 46, no. 5 (October 1994): 651–70. http://dx.doi.org/10.1034/j.1600-0870.1994.t01-3-00007.x.

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33

Liu, Q. "Information Acquisition and Reputation Dynamics." Review of Economic Studies 78, no. 4 (February 3, 2011): 1400–1425. http://dx.doi.org/10.1093/restud/rdq039.

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34

Thacker, W. C., and R. Lewandowicz. "Dynamics of information and uncertainty." Tellus A: Dynamic Meteorology and Oceanography 46, no. 5 (January 1994): 651–70. http://dx.doi.org/10.3402/tellusa.v46i5.15650.

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35

LERNER, VLADIMIR S. "INFORMATION MODELING OF NEURONAL DYNAMICS." Journal of Biological Systems 11, no. 01 (March 2003): 57–84. http://dx.doi.org/10.1142/s0218339003000786.

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The information transformation of neuronal dynamics into a human's built information network that structures and assembles an incoming information is introduced, based on Information Macrodynamics. Formalized functions of this mechanism represent a general attribute of a system's cognition, which is useful in understanding brain functions and artificial design.
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36

Dutta, Amitava, and Rahul Roy. "Dynamics of organizational information security." System Dynamics Review 24, no. 3 (September 2008): 349–75. http://dx.doi.org/10.1002/sdr.405.

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37

Lister, Bradford C. "Information, behaviour and population dynamics." Oikos 123, no. 12 (June 12, 2014): 1431–38. http://dx.doi.org/10.1111/oik.01423.

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38

Workman, Samuel, Bryan D. Jones, and Ashley E. Jochim. "Information Processing and Policy Dynamics." Policy Studies Journal 37, no. 1 (February 2009): 75–92. http://dx.doi.org/10.1111/j.1541-0072.2008.00296.x.

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39

Kohda, T. "Information sources using chaotic dynamics." Proceedings of the IEEE 90, no. 5 (May 2002): 641–61. http://dx.doi.org/10.1109/jproc.2002.1015000.

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40

Ligomenides, Panos A. "Notions and dynamics of information." Journal of Information Science 10, no. 4 (April 1985): 149–58. http://dx.doi.org/10.1177/016555158501000402.

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Spatiotemporal 'form' appears in the symbolic space, either as an output in the course of reasoning or as a product of natural processes of law and chance. Form may be recognized as 'information', filtered, interpreted, abstracted into experi ential and expert knowledge, and acted upon by biological or artificial reasoning, to produce more form and information. Information is viewed as the commodity of communication and mentation, and as a central actor and force in shaping the world around us. We are developing greater sensitivity to the importance of understanding the basic notions and dynamics of information, regarded as commodity and force. This is of essence, if we are to reveal the mechanisms (if any) of biologi cal reasoning, and of the evolution of biological and human cybernetic systems. The development of models and machines that will be used in the management and regulation of complex, human and robotic, cybernetic systems, and for the support of human decision-makers, lunges on such conceptualizations and comprehension. In this paper, we set up the backdrop for raising some basic questions related to the notions and dy namics of information.
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41

Fleck, James, Juliet Webster, and Robin Williams. "Dynamics of information technology implementation." Futures 22, no. 6 (July 1990): 618–40. http://dx.doi.org/10.1016/0016-3287(90)90131-z.

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42

Andersen, Torbert M. "Price dynamics under imperfect information." Journal of Economic Dynamics and Control 9, no. 3 (November 1985): 339–61. http://dx.doi.org/10.1016/0165-1889(85)90011-9.

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43

Holliday, Wesley H., Tomohiro Hoshi, and Thomas F. Icard. "Information dynamics and uniform substitution." Synthese 190, S1 (April 24, 2013): 31–55. http://dx.doi.org/10.1007/s11229-013-0278-0.

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44

Lerner, Vladimir S. "An observer’s information dynamics: Acquisition of information and the origin of the cognitive dynamics." Information Sciences 184, no. 1 (February 2012): 111–39. http://dx.doi.org/10.1016/j.ins.2011.08.003.

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45

Rui, Zhou, and Qi Guijie. "A System Dynamics Model for Open Innovation Community." International Journal of Enterprise Information Systems 14, no. 4 (October 2018): 78–88. http://dx.doi.org/10.4018/ijeis.2018100106.

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In order to promote the development speed of the open innovation community, a dynamical system model of it is constructed, and the simulation is carried out to find out the rules of the running rules for the open innovation community. First, the basic characteristics of open innovation community are summarized. Second, the system dynamics model of open innovation community is constructed. Finally, the simulation analysis based on dynamic system model of the open innovation community is carried out, and the effect of different factors on the variables of the open innovation community is obtained. Results show that system dynamics is an effective tool for analyzing the open innovation community.
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46

Wiggins, Geraint A. "Creativity, information, and consciousness: The information dynamics of thinking." Physics of Life Reviews 34-35 (December 2020): 1–39. http://dx.doi.org/10.1016/j.plrev.2018.05.001.

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47

Lerner, Vladimir S. "Reality, Information, and Information Observer." Proceedings 47, no. 1 (May 20, 2020): 10. http://dx.doi.org/10.3390/proceedings2020047010.

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Up to now both information and its connection to reality do not have scientifically conclusive definitions, nor implicit origin. They emerge in observing multiple impulses’ interactive yes‒no actions modeling information bits. Each memorized bit of information binds a reversible microprocess with an irreversible information macroprocess along a multi-dimensional observing process. Interactive information dynamics assemble geometrical and information structures of observer cognition and intelligence in double spiral rotating code. Information Path Functional integrates multiple interactive dynamics in finite bits which observe and measure reality. The time and space of reality exists only as discrete units of information. The observed information process, creating its Observer, connects reality, information, and the Observer.
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48

Lerner, Vladimir S. "Reality, Information, and Information Observer." Proceedings 47, no. 1 (May 20, 2020): 10. http://dx.doi.org/10.3390/proceedings47010010.

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Up to now both information and its connection to reality do not have scientifically conclusive definitions, nor implicit origin. They emerge in observing multiple impulses’ interactive yes‒no actions modeling information bits. Each memorized bit of information binds a reversible microprocess with an irreversible information macroprocess along a multi-dimensional observing process. Interactive information dynamics assemble geometrical and information structures of observer cognition and intelligence in double spiral rotating code. Information Path Functional integrates multiple interactive dynamics in finite bits which observe and measure reality. The time and space of reality exists only as discrete units of information. The observed information process, creating its Observer, connects reality, information, and the Observer.
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49

Darmon, David, Christopher J. Cellucci, and Paul E. Rapp. "Information dynamics with confidence: Using reservoir computing to construct confidence intervals for information-dynamic measures." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 8 (August 2019): 083113. http://dx.doi.org/10.1063/1.5100742.

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50

Kallinikos, Jannis. "Information out of information: on the self‐referential dynamics of information growth." Information Technology & People 19, no. 1 (January 2006): 98–115. http://dx.doi.org/10.1108/09593840610649989.

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