The C3-UNAM announces that each year there will be 2 periods, April-May and December-January, that applications will be received for 2 postdoctoral grants from the UNAM to realize research at the C3-UNAM, starting in September and March, respectively (4 postdoc grants yearly). The purpose of the grants is to realize research in complexity science in one of the following areas: computational intelligence and mathematical modeling, complexity and health, neurosciences, ecological complexity and environment (postdoctoral grants for research in humanistic sciences such as social complexity, and arts, science and complexity will be announced separately), please find the academic programs that are developed at the C3-UNAM in the page:
Technical details for the application are explained in the page:
The grants are for 1 year and renewable for a 2nd year in function of the results obtained.
The Human Generosity Project is the first large-scale transdisciplinary research project to investigate the interrelationship between biological and cultural influences on human generosity. We use multiple methodologies to understand the nature and evolution of human generosity including fieldwork, laboratory experiments and computational modeling.
Gain new insights that reframe your thinking, specific tools to advance current projects, and perspectives to set new directions.
Dates: June 2 – 14, 2019
Location: MIT, Cambridge, MA
The NECSI Summer School offers two intensive week-long courses on complexity science: modeling and networks, and data analytics. You may register for any of the weeks. If desired, arrangements for credit at a home institution may be made in advance.
Lab 1: June 2 CX102: Computer Programming for Complex Systems
Week 1: June 3-7 CX201B: Concepts and Modeling
Lab 2: June 9 CX103: Setting up for Data Analytics
Week 2: June 10-14 CX202B: Networks and Data Analytics
Complexity occurs when relevant interactions prevent the study of elements of a system in isolation. These interactions between elements may lead to the self-organization of the system. In computational intelligence, complexity and self-organization have been studied and exploited with different purposes. This Research Topic aims to bring together novel research into a coherent collection, spanning from theory and methods to simulations and applications.
Computational measures of complexity and self-organization have been proposed and applied to study a broad range of phenomena. Methodologies for facing complexity and harnessing self-organization have been used to design and build a variety of systems. Computer simulations have been tools which enabled us to study complexity and self-organization, from cellular automata and artificial neural networks to multi-agent systems and computational social science. The applications of these approaches have been vast.
Considering that complexity and self-organization are very broad themes, this Research Topic focusses only on the aspects related to computational intelligence.
31 July 2019 Abstract
30 September 2019 Manuscript
The last centuries have seen a great surge in our understanding and control of ‘simple’ physical, chemical, and biological processes through data analysis and the mathematical modelling of their underlying dynamics. Encouraged by its success, researchers have recently embarked on extending such approaches to gain qualitative and quantitative understanding of social and economic systems and the dynamics in and of them. This has become possible due to the massive amounts of data generated by information-communication technologies and the unprecedented fusion of off- and on-line human activity. However, due to the presence of adaptability, feedback loops, and strong heterogeneities of the individuals and interactions making up our modern digital societies, it is yet unclear if statistical ‘laws’ of socio-technical behaviour even exist, akin to those found for natural processes. Such continuing search has resulted in the fields of computational social science and social network science, which share the goal of first analysing social phenomena and then modelling them with enough accuracy to make reliable predictions. This Special Issue invites contributions to such fields of study, with focus on the temporal evolution and dynamics of complex social systems. As topics of interest, we propose research on more realistic models of social dynamics, the use of statistical inference, machine learning, and other cross-disciplinary techniques to complement the analysis of social dynamics, and the creation of loops between data acquisition and model analysis to increase accuracy in the prediction of social trends. We hope this Special Issue will bring together expertise from a wide range of research communities interested in similar topics, including computational social science, network science, information science, and complexity science.