Complexity measurement of natural and artificial languages

Febres, Gerardo; Jaffé, Klaus; Gershenson, Carlos

doi:10.1002/cplx.21529

Cited by 24 publications

(32 citation statements)

References 21 publications

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“…This work shows an example of the usefulness of our proposed measures to guide the self-organization of systems, which are being applied in other areas as well [63][64][65]. Since everything can be described as information [60], there is the potential of measuring C, E, and S of every phenomena.…”

Section: Discussionmentioning

confidence: 99%

Measuring the Complexity of Self-Organizing Traffic Lights

Zubillaga

Cruz

Aguilar

et al. 2014

Entropy

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We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby's law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.

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Section: Discussionmentioning

confidence: 99%

Measuring the Complexity of Self-Organizing Traffic Lights

Zubillaga

Cruz

Aguilar

et al. 2014

Entropy

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“…We can for example consider the use of a simulation model, for which interactions between elementary agents translate as a coded message at the upper level: it is then possible by exploiting the degrees of freedom to minimize the quantity of information contained in the message. The different languages require different cognitive efforts and compress the information in a different way, having different levels of measurable complexity [Febres et al, 2013]. In a similar way, architectural artefacts are the result of a process of natural and cultural evolution, and witness more or less this trajectory.…”

Section: Complexity and Complexitiesmentioning

confidence: 99%

Relating Complexities for the Reflexive Study of Complex Systems

Raimbault

2020

Lecture Notes in Morphogenesis

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Several approaches and corresponding definitions of complexity have been developed in different fields. Urban systems are the archetype of complex socio-technical systems concerned with these different viewpoints. We suggest in this chapter some links between three types of complexity, namely emergence, computational complexity and informational complexity. We discuss the implication of these links on the necessity of reflexivity to produce a knowledge of the complex, and how this connects to the interdisciplinary of approaches in particular for socio-technical systems. We finally synthesize this positioning as a proposal of an epistemological framework called applied perspectivism, and discuss the implications for the study of urban systems.

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“…The ratio P (W) between maximum and minimum vales for each of them was 2.7 and 3 respectively 3.6. Synergies and language (Febres, Jaffe, Gershenson 2014 and.…”

Section: Brain Complexity and Social Synergy (Jaffe And Perez 1989)mentioning

confidence: 99%

Measuring synergy using entropy

Jaffé

Febres

2015

Proceedings of 2nd International Electronic Conference on Entropy and Its Applications

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Synergy is phenomenon found everywhere in nature at all levels of organization in physics, chemistry, biology, social sciences, and the arts. The English Wikipedia defines Synergy as "the creation of a whole that is greater than the simple sum of its parts. The term synergy comes from the Attic Greek word συνεργία synergia from synergos, συνεργός, meaning working together". Synergy is an important concept that is difficult to define precisely and even more difficult to quantify. Entropy on the other hand has been used as a way to measure order or complexity. Here we explore the changes in entropy induced by diverse process described as synergistic. The paper uses seven examples reported in the literature to quantify synergy in different settings. The result shows that synergistic processes are associated to large decreases in entropy and slightly less large increases in work output of the system where they occur. Systems which suffer changes in entropy that are not associated to synergistic processes show a different pattern These results open the door to use entropy measures to identify and classify synergistic processes.

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Complexity measurement of natural and artificial languages

Cited by 24 publications

References 21 publications

Measuring the Complexity of Self-Organizing Traffic Lights

Measuring the Complexity of Self-Organizing Traffic Lights

Relating Complexities for the Reflexive Study of Complex Systems

Measuring synergy using entropy

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