TY - Generic T1 - On two information-theoretic measures of random fuzzy networks T2 - Artificial Life Conference Proceedings Y1 - 2020 A1 - Zapata, Octavio A1 - Kim, Hyobin A1 - Gershenson, Carlos JF - Artificial Life Conference Proceedings VL - 32 UR - https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00342 ER - TY - JOUR T1 - Effects of Antimodularity and Multiscale Influence in Random Boolean Networks JF - Complexity Y1 - 2019 A1 - Escobar, Luis A. A1 - Kim, Hyobin A1 - Gershenson, Carlos VL - 2019 UR - https://doi.org/10.1155/2019/8209146 ER - TY - JOUR T1 - A Multilayer Structure Facilitates the Production of Antifragile Systems in Boolean Network Models JF - Complexity Y1 - 2019 A1 - Kim, Hyobin A1 - Pineda, Omar K. A1 - Gershenson, Carlos AB - Antifragility is a property from which systems are able to resist stress and furthermore benefit from it. Even though antifragile dynamics is found in various real-world complex systems where multiple subsystems interact with each other, the attribute has not been quantitatively explored yet in those complex systems which can be regarded as multilayer networks. Here we study how the multilayer structure affects the antifragility of the whole system. By comparing single-layer and multilayer Boolean networks based on our recently proposed antifragility measure, we found that the multilayer structure facilitated the production of antifragile systems. Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures and creating more antifragile engineered systems. VL - 2019 UR - https://doi.org/10.1155/2019/2783217 ER - TY - JOUR T1 - A Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks JF - Complexity Y1 - 2019 A1 - Pineda, Omar K. A1 - Kim, Hyobin A1 - Gershenson, Carlos AB - Antifragility is a property that enhances the capability of a system in response to external perturbations. Although the concept has been applied in many areas, a practical measure of antifragility has not been developed yet. Here we propose a simply calculable measure of antifragility, based on the change of ``satisfaction'' before and after adding perturbations, and apply it to random Boolean networks (RBNs). Using the measure, we found that ordered RBNs are the most antifragile. Also, we demonstrated that seven biological systems are antifragile. Our measure and results can be used in various applications of Boolean networks (BNs) including creating antifragile engineering systems, identifying the genetic mechanism of antifragile biological systems, and developing new treatment strategies for various diseases. VL - 2019 UR - https://doi.org/10.1155/2019/3728621 ER -