scholarly journals Heterogeneity: method and applications for complex systems analysis

2022 ◽  
Vol 2159 (1) ◽  
pp. 012013
Author(s):  
J M Redondo ◽  
J S Garcia ◽  
C Bustamante-Zamudio ◽  
M F Pereira ◽  
H F Trujillo
Keyword(s):  
Machine Learning ◽  
Complex Systems ◽  
Research And Development ◽  
Systems Analysis ◽  
Ecological Systems ◽  
Agent Based Modeling ◽  
Time Lags ◽  
Agent Based ◽  
Physical Systems ◽  
Sustainability Analysis

Abstract Socio-ecological systems like another physical systems are complex systems in which are required methods for analyzes their non-linearities, thresholds, feedbacks, time lags, and resilience. This involves understanding the heterogeneity of the interactions in time and space. In this article, we carry out the proposition and demonstration of two methods that allow the calculation of heterogeneity in different contexts. The practical effectiveness of the methods is presented through applications in sustainability analysis, land transport, and governance. It is concluded that the proposed methods can be used in various research and development areas due to their ease of being considered in broad modeling frameworks as agent-based modeling, system dynamics, or machine learning, although it could also be used to obtain point measurements only by replacing values.

Use of a sonification system for science learning by people who are blind

2016 ◽  
Vol 10 (4) ◽  
pp. 187-198 ◽  
Author(s):  
Orly Lahav ◽  
Nuha Chagab ◽  
Vadim Talis
Keyword(s):  
Complex Systems ◽  
Conceptual Knowledge ◽  
Science Curriculum ◽  
Low Cost ◽  
Agent Based Modeling ◽  
Learning System ◽  
Learning Ability ◽  
Content Type ◽  
Agent Based ◽  
K 12

Purpose The purpose of this paper is to examine a central need of students who are blind: the ability to access science curriculum content. Design/methodology/approach Agent-based modeling is a relatively new computational modeling paradigm that models complex dynamic systems. NetLogo is a widely used agent-based modeling language that enables exploration and construction of models of complex systems by programming and running the rules and behaviors. Sonification of variables and events in an agent-based NetLogo computer model of gas in a container is used to convey phenomena information. This study examined mainly two research topics: the scientific conceptual knowledge and systems reasoning that were learned as a result of interaction with the listen-to-complexity (L2C) environment as appeared in answers to the pre- and post-tests and the learning topics of kinetic molecular theory of gas in chemistry that was learned as a result of interaction with the L2C environment. The case study research focused on A., a woman who is adventitiously blind, for eight sessions. Findings The participant successfully completed all curricular assignments; her scientific conceptual knowledge and systems reasoning became more specific and aligned with scientific knowledge. Practical implications A practical implication of further studies is that they are likely to have an impact on the accessibility of learning materials, especially in science education for students who are blind, as equal access to low-cost learning environments that are equivalent to those used by sighted users would support their inclusion in the K-12 academic curriculum. Originality/value The innovative and low-cost learning system that is used in this research is based on transmittal of visual information of dynamic and complex systems, providing perceptual compensation by harnessing auditory feedback. For the first time the L2C system is based on sound that represents a dynamic rather than a static array. In this study, the authors explore how a combination of several auditory representations may affect cognitive learning ability.

Drivers of Late Pleistocene human survival and dispersal: an agent-based modeling and machine learning approach

2019 ◽  
Vol 221 ◽  
pp. 105867 ◽  
Author(s):  
Ali R. Vahdati ◽  
John David Weissmann ◽  
Axel Timmermann ◽  
Marcia S. Ponce de León ◽  
Christoph P.E. Zollikofer
Keyword(s):  
Machine Learning ◽  
Late Pleistocene ◽  
Agent Based Modeling ◽  
Learning Approach ◽  
Agent Based ◽  
Human Survival ◽  
Machine Learning Approach

scholarly journals Topology Applied to Machine Learning: From Global to Local

2021 ◽  
Vol 4 ◽  
Author(s):  
Henry Adams ◽  
Michael Moy
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Klein Bottle ◽  
Persistent Homology ◽  
Agent Based Modeling ◽  
Learning Problems ◽  
Local Geometry ◽  
Agent Based ◽  
Learning Tasks ◽  
Circle Model

Through the use of examples, we explain one way in which applied topology has evolved since the birth of persistent homology in the early 2000s. The first applications of topology to data emphasized the global shape of a dataset, such as the three-circle model for 3 × 3 pixel patches from natural images, or the configuration space of the cyclo-octane molecule, which is a sphere with a Klein bottle attached via two circles of singularity. In these studies of global shape, short persistent homology bars are disregarded as sampling noise. More recently, however, persistent homology has been used to address questions about the local geometry of data. For instance, how can local geometry be vectorized for use in machine learning problems? Persistent homology and its vectorization methods, including persistence landscapes and persistence images, provide popular techniques for incorporating both local geometry and global topology into machine learning. Our meta-hypothesis is that the short bars are as important as the long bars for many machine learning tasks. In defense of this claim, we survey applications of persistent homology to shape recognition, agent-based modeling, materials science, archaeology, and biology. Additionally, we survey work connecting persistent homology to geometric features of spaces, including curvature and fractal dimension, and various methods that have been used to incorporate persistent homology into machine learning.

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