Information processing as a paradigm to model and simulate complex systems

Sloot, Peter M. A.; Quax, Rick

doi:10.1016/j.jocs.2012.07.001

Cited by 18 publications

(9 citation statements)

References 46 publications

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“…Topologies of networks evolve over time in order to maximize the systemic efficiency. An important part of this efficiency is the optimal information exchange, even though the meaning and spreading of information varies considerably among such fields as mathematics15, physics16, biology1718, social science, communication and computer science1920.…”

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confidence: 99%

Noise enhances information transfer in hierarchical networks

Czaplicka

Hołyst

Sloot

2013

Sci Rep

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We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Barabási networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor.

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confidence: 99%

Noise enhances information transfer in hierarchical networks

Czaplicka

Hołyst

Sloot

2013

Sci Rep

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“…In order to quantify, understand and even predict complex phenomena in a networked world, we need to combine the expressiveness of Individual Based Models with the structural information of Networks and quantify the way information flows through it a network [20,30,36,38].…”

Section: Complex Temporal Dynamics Of Networkmentioning

confidence: 99%

Advances in dynamic temporal networks: Understanding the temporal dynamics of complex adaptive networks

Sloot

Kampis

Gulyás

2013

Eur. Phys. J. Spec. Top.

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“…Specifically, interactions among human users essentially depend on their circadian cycles and a number of user-specific characteristics, such as its psychology profile, activity pattern, mutual trust or preferences towards particular communication contents. Recent advances in the computational science [18] with individual-based modelling provide the right platform where these attributes of humans can be effectively considered [19]. In this approach, that we also use here, the empirical data of a concrete online system are analysed to infer the activity patterns and statistical distributions that characterize the users' behavior.…”

Section: Introductionmentioning

confidence: 99%

Collective emotion dynamics in chats with agents, moderators and Bots

Šuvakov¹,

Tadić²

2014

Condens. Matter Phys.

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Using agent-directed simulations, we investigate fluctuations in the collective emotional states on a chat network where agents interchange messages with a fixed number of moderators and emotional Bot. To design a realistic chat system, the interaction rules and some statistical parameters, as well as the agent's attributes, are inferred from the empirical chat channel Ubuntu. In the simulations, the Bot's emotion is fixed; the moderators tune the level of its activity by passing a fraction of messages to the Bot. At 0, the collective emotional state matching the Bot's emotion polarity gradually arises; the average growth rate of the dominant emotional charge serves as an order parameter. Due to self-organizing effects, the collective dynamics is more explosive when positive emotions arise by positive Bot than the onset of negative emotions in the presence of negative Bot at the same . Furthermore, when the emotions matching the Bot's emotion polarity are spread over the system, the underlying fractal processes exhibit higher persistence and stronger clustering of events than the processes spreading of emotion polarity opposite to the Bot's emotion. On the other hand, the relaxation dynamics is controlled by the external noise; the related nonextensive parameter, estimated from the statistics of returns, is virtually independent of the Bot's activity level and emotion contents.

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Information processing as a paradigm to model and simulate complex systems

Cited by 18 publications

References 46 publications

Noise enhances information transfer in hierarchical networks

Noise enhances information transfer in hierarchical networks

Advances in dynamic temporal networks: Understanding the temporal dynamics of complex adaptive networks

Collective emotion dynamics in chats with agents, moderators and Bots

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