Submission Deadlines:
Abstracts: 01 April 2025
Full Papers: 01 August 2025
Publication: March-April 2026

Cybernetics and systems education have long played a vital role in understanding complex, purposeful and adaptive systems. With the advent of next generation artificial intelligence and with the vast range of complex socio-technical systems that require collective transformation, cybernetic and systems principles have only become more relevant. There is a growing, transnational need for education programs to prepare the current and next generation to operate within and beyond these frameworks.

This special issue seeks to bring together educators, researchers and practitioners to explore the past, present, and future of cybernetics and systems education. We aim to examine how cybernetic concepts and systems thinking have been previously and/or are currently integrated into educational paradigms, showcase novel approaches to teaching these principles, and envision transformative methodologies that may shape the future of cybernetics and systems education.

Through this special issue we seek to create and promote a transnational network to further cybernetic and cybernetically informed systems education.

Read the full article at: onlinelibrary.wiley.com

The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the scientific understanding of intelligence and the development of true biologically inspired AI.

Course/Program Dates:
Aug 03, 2025 – Aug 24, 2025
Application due date:
Mar 24, 2025

The basis of intelligence – how the brain produces intelligent behavior and how to endow machines with human-like intelligence – is arguably the greatest problem in science and technology. To solve it, we will need to understand how natural intelligence emerges from computations in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor will ultimately enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies are impressive but quite different from human intelligence. We still do not understand the mechanisms underlying the robustness, the generalization, and the continual learning capabilities of biological intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise of significant progress. Elucidating how human intelligence works will in turn lead to more sophisticated AI algorithms. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the scientific understanding of intelligence and the development of true biologically inspired AI.

Apply at: www.mbl.edu

Complexity and Networks COmmunity and REsources (CORE) is an organization dedicated to connecting scientists and organizations in the fields of Complexity and Network Science. We enhance collaboration among organizations that organize talks, seminars, and events, and we collect all conference and job application deadlines to create a hub where scientists can find relevant news and information. On this page, you will find information about the organizations and contributors involved in this project.

CORE Members: NetPlace, yrCSS, WiNS, Complexity Cat, WWCS, Complexity72h.

More at: complexity-core.github.io

EPJ Data Science

Submission deadline
31 July 2025

Digital, information and communication technologies, together with Big Data, the Internet of Things, and Artificial Intelligence, are reshaping almost every aspect of our societies. From traffic to logistics, from mobility to smart cities and societies, much of this gears towards more predictability, controllability, and automation, using digital twins and many other approaches. Optimizing performance, sustainability, resilience, and health are often stated goals. But what roles will complexity and collective intelligence, democracy and human rights, ethics, agency and freedom, co-creation and co-evolution play? And how can scientific disciplines – from data science and complexity theory to computational social science, network analysis, transportation modeling, game theory, and agent-based as well as AI-driven models – contribute to understanding these challenges and to shaping future solutions?

This special collection seeks to reflect on recent advances in these fields and explore visions for the future. It will provide a platform for critical reflection on the scientific methodologies and technological strategies currently driving our world. We invite inspiring contributions that provoke innovative thoughts and stimulate rigorous debate on the future trajectory of these technologies and the socio-technical systems that are expected to result from them.

Submit at: link.springer.com

The scientific community is increasingly aware of the profound challenges associated with research evaluation, particularly the reliance on quantitative journal metrics such as the impact factor as proxies for scientific quality. These practices have entrenched a system where researchers are compelled to publish in high-cost, high-impact journals to advance their careers, often at the expense of broader scientific contributions. Despite the growing adoption of initiatives like DORA (https://sfdora.org), Plan S (https://www.coalition-s.org), CoARA (https://coara.eu), or PEER Community (https://peercommunityin.org), which aim to reform research assessment and promote open science, progress has been slow, and deeply-ingrained evaluation schemes still dominate. Thus, initiatives that support dissemination of knowledge (outreach, extension), data curation and sharing, research in non-academic contexts (which is more “messy”, difficult to conduct) and in response to real-world needs and with impact is not sufficiently valued. These issues are compounded by the dominance of commercial publishers, whose exorbitant article processing charges (APCs) and profit-driven models exploit the academic community creating inequalities, fostering an unsustainable and unfair publishing system that threatens the very preservation and dissemination of scholarly knowledge.

Read the full article at: cssociety.org