Canals, C., Goles, E., Mascareno, A., Rica, S., & Ruz, G. A. (2018). School Choice in a Market Environment: Individual versus Social Expectations. Complexity, 3793095, 11 pp.
Abstract: School choice is a key factor connecting personal preferences (beliefs, desires, and needs) and school offer in education markets. While it is assumed that preferences are highly individualistic forms of expectations by means of which parents select schools satisfying their internal moral standards, this paper argues that a better matching between parental preferences and school offer is achieved when individuals take into account their relevant network vicinity, thereby constructing social expectations regarding school choice. We develop two related models (individual expectations and social expectations) and prove that they are driven by a Lyapunov function, obtaining that both models converge to fixed points. Also, we assess their performance by conducting computational simulations. While the individual expectations model shows a probabilistic transition and a critical threshold below which preferences concentrate in a few schools and a significant amount of students is left unattended by the school offer, the social expectations model presents a smooth dynamics in which most of the schools have students all the time and no students are left out. We discuss our results considering key topics of the empirical research on school choice in educational market environments and conclude that social expectations contribute to improve information and lead to a better matching between school offer and parental preferences.
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Garcia, J., Altimiras, F., Pena, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, , 15 pp.
Abstract: The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.
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Garcia, J., Pope, C., & Altimiras, F. (2017). A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition. Complexity, , 14 pp.
Abstract: Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.
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Goles, E., Slapnicar, I., & Lardies, M. A. (2021). Universal Evolutionary Model for Periodical Species. Complexity, 2021, 2976351.
Abstract: Real-world examples of periodical species range from cicadas, whose life cycles are large prime numbers, like 13 or 17, to bamboos, whose periods are large multiples of small primes, like 40 or even 120. The periodicity is caused by interaction of species, be it a predator-prey relationship, symbiosis, commensalism, or competition exclusion principle. We propose a simple mathematical model, which explains and models all those principles, including listed extremal cases. This rather universal, qualitative model is based on the concept of a local fitness function, where a randomly chosen new period is selected if the value of the global fitness function of the species increases. Arithmetically speaking, the different interactions are related to only four principles: given a couple of integer periods either (1) their greatest common divisor is one, (2) one of the periods is prime, (3) both periods are equal, or (4) one period is an integer multiple of the other.
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Hilbert, M. (2014). Scale- Free Power- Laws as Interaction between Progress and Diffusion. Complexity, 19(4), 56–65.
Abstract: While scale-free power-laws are frequently found in social and technological systems, their authenticity, origin, and gained insights are often questioned, and rightfully so. The article presents a newly found rank-frequency power-law that aligns the top-500 supercomputers according to their performance. Pursuing a cautious approach in a systematic way, we check for authenticity, evaluate several potential generative mechanisms, and ask the so what question. We evaluate and finally reject the applicability of well-known potential generative mechanisms such as preferential attachment, self-organized criticality, optimization, and random observation. Instead, the microdata suggest that an inverse relationship between exponential technological progress and exponential technology diffusion through social networks results in the identified fat-tail distribution. This newly identified generative mechanism suggests that the supply and demand of technology (technology push and demand pull) align in exponential synchronicity, providing predictive insights into the evolution of highly uncertain technology markets. (c) 2013 Wiley Periodicals, Inc. Complexity 19: 56-65, 2014
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Mascareno, A., Goles, E., & Ruz, G. A. (2016). Crisis in complex social systems: A social theory view illustrated with the chilean case. Complexity, 21(S2), 13–23.
Abstract: The article argues that crises are a distinctive feature of complex social systems. A quest for connectivity of communication leads to increase systems' own robustness by constantly producing further connections. When some of these connections have been successful in recent operations, the system tends to reproduce the emergent pattern, thereby engaging in a non-reflexive, repetitive escalation of more of the same communication. This compulsive growth of systemic communication in crisis processes, or logic of excess, resembles the dynamic of self-organized criticality. Accordingly, we first construct the conceptual foundations of our approach. Second, we present three core assumptions related to the generative mechanism of social crises, their temporal transitions (incubation, contagion, restructuring), and the suitable modeling techniques to represent them. Third, we illustrate the conceptual approach with a percolation model of the crisis in Chilean education system. (c) 2016 Wiley Periodicals, Inc. Complexity 21: 13-23, 2016
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Medina, P., Goles, E., Zarama, R., & Rica, S. (2017). Self-Organized Societies: On the Sakoda Model of Social Interactions. Complexity, , 16 pp.
Abstract: We characterize the behavior and the social structures appearing from a model of general social interaction proposed by Sakoda. The model consists of two interacting populations in a two-dimensional periodic lattice with empty sites. It contemplates a set of simple rules that combine attitudes, ranges of interactions, and movement decisions. We analyze the evolution of the 45 different interaction rules via a Potts-like energy function which drives the system irreversibly to an equilibriumor a steady state. We discuss the robustness of the social structures, dynamical behaviors, and the existence of spatial long range order in terms of the social interactions and the equilibrium energy.
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Pinto-Rios, J., Calderon, F., Leiva, A., Hermosilla, G., Beghelli, A., Borquez-Paredes, D., et al. (2023). Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach. Complexity, 2023, 4140594.
Abstract: A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment was designed and implemented to emulate the operation of MCF-EONs – taking into account the modulation format-dependent reach and intercore crosstalk (XT) – and four DRL agents were trained to solve the RMSCA problem. The blocking performance of the trained agents was compared through simulation to 3 baselines RMSCA heuristics. Results obtained for the NSFNet and COST239 network topologies under different traffic loads show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability with respect to the best-performing baseline heuristic method.
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Quinteros, M. J., & Villena, M. J. (2022). On the Dynamics and Stability of the Crime and Punishment Game. Complexity, 2022, 2449031.
Abstract: We study the dynamics and stability of the economics of crime and punishment game from an evolutionary perspective. Specifically, we model the interaction between agents and controllers as an asymmetric game exploring the dynamics of the classic static model using a replicator dynamics equation, given exogenous levels of monitoring and criminal sanctions. The dynamics show five possible equilibria, from which three are stable. Our results show that a culture of honest agents is never stable; however when the penalty is high enough, the system will neutrally tend to an equilibrium of honest agents and a monitoring firm. By contrast, when the probability of detecting wrongdoing is small, the system, in some cases, will remain in a transient state, in which it is impossible to predict the proportion of honest agents.
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Ruz, G. A., & Araya-Diaz, P. (2018). Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers. Complexity, 4075656, 14 pp.
Abstract: Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.
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