This special session aims to promote and expand Morphogenetic Engineering, a field of research exploring the artificial design and implementation of autonomous systems capable of developing complex, heterogeneous morphologies. Particular emphasis is set on the programmability and computing abilities of self-organization, properties that are often underappreciated in complex systems science—while, conversely, the benefits of self-organization are often underappreciated in engineering methodologies.
8th MEW at ALife 2018
Morphogenetic Engineering (Workshop) Special Session, at the
2018 Conference on Artificial Life
July 23-27, 2018
National Museum of Emerging Science and Innovation, Odaiba, Tokyo, Japan
Presentation by Dirk Helbing
This book aims to bring together researchers and practitioners working across domains and research disciplines to measure, model, and visualize complex networks. It collects the works presented at the 9th International Conference on Complex Networks (CompleNet) 2018 in Boston, MA in March, 2018. With roots in physical, information and social science, the study of complex networks provides a formal set of mathematical methods, computational tools and theories to describe prescribe and predict dynamics and behaviors of complex systems. Despite their diversity, whether the systems are made up of physical, technological, informational, or social networks, they share many common organizing principles and thus can be studied with similar approaches. This book provides a view of the state-of-the-art in this dynamic field and covers topics such as group decision-making, brain and cellular connectivity, network controllability and resiliency, online activism, recommendation systems, and cyber security.
Complex Networks IX: Proceedings of the 9th Conference on Complex Networks CompleNet 2018
Sean Cornelius, Kate Coronges, Bruno Gonçalves, Roberta Sinatra, Alessandro Vespignani
Springer, Mar 19, 2018
The booming field of artificial intelligence (AI) is grappling with a replication crisis, much like the ones that have afflicted psychology, medicine, and other fields over the past decade. Just because algorithms are based on code doesn’t mean experiments are easily replicated. Far from it. Unpublished codes and a sensitivity to training conditions have made it difficult for AI researchers to reproduce many key results. That is leading to a new conscientiousness about research methods and publication protocols. Last week, at a meeting of the Association for the Advancement of Artificial Intelligence in New Orleans, Louisiana, reproducibility was on the agenda, with some teams diagnosing the problem—and one laying out tools to mitigate it.
Artificial intelligence faces reproducibility crisis
Science 16 Feb 2018:
Vol. 359, Issue 6377, pp. 725-726
In this episode, Angie interviews author of Embracing Complexity: Strategic Perspectives for an Age of Turbulence, Jean Boulton, who is also an academic and management consultant, specializing in complexity theory. Boulton talks with us about many different concepts including: how complexity thinking compares to systems thinking, change management, organizational strategy, complexity as a worldview, and even how this field is shining a light on climate change.