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Search Results (3)

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Authors = David A. Rosenblueth

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18 pages, 1084 KiB  
Article
Random Networks with Quantum Boolean Functions
by Mario Franco, Octavio Zapata, David A. Rosenblueth and Carlos Gershenson
Mathematics 2021, 9(8), 792; https://doi.org/10.3390/math9080792 - 7 Apr 2021
Cited by 7 | Viewed by 3598
Abstract
We propose quantum Boolean networks, which can be classified as deterministic reversible asynchronous Boolean networks. This model is based on the previously developed concept of quantum Boolean functions. A quantum Boolean network is a Boolean network where the functions associated with the nodes [...] Read more.
We propose quantum Boolean networks, which can be classified as deterministic reversible asynchronous Boolean networks. This model is based on the previously developed concept of quantum Boolean functions. A quantum Boolean network is a Boolean network where the functions associated with the nodes are quantum Boolean functions. We study some properties of this novel model and, using a quantum simulator, we study how the dynamics change in function of connectivity of the network and the set of operators we allow. For some configurations, this model resembles the behavior of reversible Boolean networks, while for other configurations a more complex dynamic can emerge. For example, cycles larger than 2N were observed. Additionally, using a scheme akin to one used previously with random Boolean networks, we computed the average entropy and complexity of the networks. As opposed to classic random Boolean networks, where “complex” dynamics are restricted mainly to a connectivity close to a phase transition, quantum Boolean networks can exhibit stable, complex, and unstable dynamics independently of their connectivity. Full article
(This article belongs to the Special Issue Boolean Networks Models in Science and Engineering)
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24 pages, 549 KiB  
Article
Measuring the Complexity of Self-Organizing Traffic Lights
by Darío Zubillaga, Geovany Cruz, Luis Daniel Aguilar, Jorge Zapotécatl, Nelson Fernández, José Aguilar, David A. Rosenblueth and Carlos Gershenson
Entropy 2014, 16(5), 2384-2407; https://doi.org/10.3390/e16052384 - 25 Apr 2014
Cited by 45 | Viewed by 25899
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Entropy Methods in Guided Self-Organization)
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19 pages, 316 KiB  
Article
Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments
by Hector Zenil, Carlos Gershenson, James A. R. Marshall and David A. Rosenblueth
Entropy 2012, 14(11), 2173-2191; https://doi.org/10.3390/e14112173 - 5 Nov 2012
Cited by 20 | Viewed by 26272
Abstract
In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the [...] Read more.
In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA). Full article
(This article belongs to the Special Issue Selected Papers from Symposium on Natural/Unconventional Computing and Its Philosophical Significance)
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