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.
Lakeside Labs is a research and innovation company driven by the vision to create solutions for networked systems using concepts from self-organization. To further strengthen our team, we have an opening for a senior researcher position in complex systems engineering with emphasis on robotics/drones and autonomous transportation.
* Perform outstanding research in the field of complex systems
* Publish in high-tier scientific journals and conferences
* Actively participate in research projects
* Collaborate with companies and research partners
* Take responsibility in project management
* Contribute to project proposals on a national and European level
The successful candidate will initially work, in a team of three researchers, in a European research project on design methods for cyber-physical systems with emphasis on swarm intelligence and its integration into a model-based library.
Established in 2016, this Scholarship has been generously funded by the School of Civil Engineering to encourage and assist students with completing studies in complex systems at the University of Sydney.
Applicants must have an unconditional offer of admission for the Masters of Complex Systems within the Faculty of Engineering and Information Technologies at the University of Sydney.
Applicants must have achieved a WAM of 75 and above, or equivalent, in their previous tertiary studies.
Deadline: February 14th, 2019.
Call For Papers:
Deadline for manuscript submissions: 15 March 2019
Dr. Hector Zenil
Prof. Dr. Selmer Bringsjord
Current popular approaches to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) are mostly statistical in nature, and are not well equipped to deal with abstraction and explanation. In particular, they cannot generate candidate models or make generalizations directly from data to discover possible causal mechanisms. One method that researchers are resorting to in order to discover how deep learning algorithms work involves using what are called ‘generative models’ (a possible misnomer). They train a learning algorithm and handicap it systematically whilst asking it to generate examples. By observing the resulting examples they are able to make inferences about what may be happening in the algorithm at some level.
However, current trends and methods are widely considered black-box approaches that have worked amazingly well in classification tasks, but provide little to no understanding of causation and are unable to deal with forms of symbolic computation such as logical inference and explanation. As a consequence, they also fail to be scalable in domains they have not been trained for, and require tons of data to be trained on, before they can do anything interesting—-and they require training every time they are presented with (even slightly) different data.
Furthermore, how other cognitive features, such as human consciousness, may be related to current and future directions in deep learning, and whether such features may prove advantageous or disadvantageous remains an open question.
The aim of this special issue is thus to attempt to ask the right questions and shed some light on the achievements, limitations and future directions in reinforcement/deep learning approaches and differentiable programming. Its particular focus will be on the interplay of data and model-driven approaches that go beyond current ones, which for the most part are based on traditional statistics. It will attempt to ascertain whether a fundamental theory is needed or whether one already exists, and to explore the implications of current and future technologies based on deep learning and differentiable programming for science, technology and society.
Special issue website:
Business and society are transforming and becoming increasingly complex. Artificial Intelligence, machine learning, big data analytics and hybrid human-machine systems are playing an increasing role in business products, strategy, and in the organization itself.
NECSI is hosting two courses as part of its week-long NECSI Executive 2018 Fall Program in Boston, MA. Each course can stand alone, but together they form a potent and practical training experience for the executive leader.