Guided self-organization through an entropy-based self-advising approach

Somayeh Kalantari, Eslam Nazemi & Behrooz Masoumi
Computing (2022)

Nowadays, the study of self-organizing systems has attracted much attention. However, since these systems are run in dynamic, changing, and evolving environments, it is possible that undesirable behaviors that are contrary to the system goals occur. Therefore, it is necessary to provide mechanisms to guide the self-organizing system. However, several approaches were proposed to guide self-organizing systems, more effective approaches are required due to the variation of the contexts in which they are deployed and their complexity. This paper aims to use the self-advising property to provide guidelines about the context of self-organizing systems. The agents of these systems are guided implicitly by using the guidelines provided. In the proposed approach, contextual data is made by an advisor agent that produces them based on the agents’ behavioral entropy. The proposed approach is evaluated using a case study based on the NASA ANTS mission. According to experiments, the proposed approach causes adaptation activities’ costs to decrease at all radio ranges. Besides, in some radio ranges, i.e., 110 and 120 GHz, the guiding state’s adaptive time is less than the no-guiding state’s adaptive time. The evaluations also show that the ruler agents’ mean entropy in the guiding state is less than the no-guiding state in 75 % of radio ranges. This approach’s success in reducing the agents’ entropy indicates its ability to guide self-organizing systems.

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Postdoc Position in Large-Scale Traffic Simulation and Swarm Intelligence for Smart Cities

Research at the Professorship of Computational Social Science (COSS) is focused on:
* bringing modelling and computer simulation of social processes and transportation phenomena together with technology, experimental, and data-driven work,
* combining the perspectives of different scientific disciplines (e.g., social science, computer science, complexity science and sociophysics),
* bridging fundamental and applied for work,
* developing digital tools to support people and studying the resulting behaviour.

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[Classics] Principles of the self-organizing system

W. Ross Ashby

The brilliant British psychiatrist, neuroscientist, and mathematician Ross Ashby was one of the pioneers in early and mid-phase cybernetics and thereby one of the leading progenitors of modern complexity theory. Not one to take either commonly used terms or popular notions for granted, Ashby probed deeply into the meaning of supposedly self-organizing systems. At the time of the following article, he had been working on a mathematical formalism of his homeostat, a hypothetical machine established on an axiomatic, set theoretical foundation that was supposed to offer a sufficient description of a living organism’s learning and adaptive intelligence. Ashby’s homeostat had a small number of essential variables serving to maintain its operation over a wide range of environmental conditions so that if the latter changed and thereby shifted the variables beyond the range where the homeostat could safely function, a new ‘higher’ level of the machine was activated in order to randomly reset the lower level’s internal connections or organization (see Dupuy, 2000). Like the role of random mutations during evolution, if the new range set at random proved functional, the homeostat survived, otherwise it expired.

One of Ashby’s goals was to repudiate that interpretation of the notion of self-organization, one commonly held to this day, which would have it that either a machine or a living organism could by itself change its own organization (or, in his phraseology, the functional mappings). For Ashby, self-organization in this sense was a bit of superfluous metaphysics since he believed not only could his formalism by itself completely delineate the homeostat’s lower level organization, the adaptive novelty of his homeostat was purely the result of its upper level randomization that could reorganize the lower level and not some innate propensity for autonomous change. We offer Ashby’s careful reasoning here as an enlightening guide for coming to terms with key ideas in complexity theory whose genuine significance lies less with facile bandying about and more with an intensive and extensive examination of the underlying assumptions.

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Conference on Complex Systems 2022: Call for Abstracts

17-21/10/2022, Palma de Mallorca, Spain.
Deadline for submission: May 31st, 2022 (strict deadline)
Author notification: end of June.
Author registration: end of July.
The call for contributions to the Conference on Complex Systems 2022 (CCS 2022) is officially open. Share the news!
Accepted abstracts will be presented following one of the three possible formats:
  • oral presentation (12-min talk + 3-min questions) during a parallel session
  • lightening presentation (5-min talk) during a plenary session
  • poster presentation during poster sessions
Abstracts must be prepared using CCS2022 official template (Latex or Word) and submitted through Easychair as a PDF file.
More info on abstract submission can be found at: