Modern computing systems are becoming ever more collective and are often composed of many distributed and heterogeneous entities. These systems operate under continuous perturbations and environmental change, making manual adjustments infeasible. For a collective system to be resilient, its adaptation must also be collective, in the sense that multiple entities must adapt in a way that addresses critical runtime conditions while preserving the benefits of collaborative interdependencies. Decision-making in such systems is distributed and possibly highly dispersed, and interaction between the entities may lead to the emergence of unexpected phenomena. In such systems, new approaches for and understanding of collective adaptation are needed, to allow: i) information multiple entities to adapt in a coordinated or complementary way, with ii) negotiations, collective action, or other mechanisms to decide which collective changes are to be made. Collective adaptation also raises a second important challenge: Which parts of the system (things, services, people) should adapt, and how? This is nontrivial, as multiple solutions to the same problem may be generated at different levels, and individuals in the collective often have partial information. The challenge is to understand these levels and create mechanisms to decide the right scope for an adaptation for a given problem. This workshop solicits papers that address new methodologies, theories, principles, and fundamental understanding, that can be used to underpin the design, operation, and analysis of such systems. Case studies, applications showing such approaches in action, and interdisciplinary work are particularly welcome.

• Novel theories relating to operating principles of CAS
• Novel design principles for building CAS systems
• Insights into the short and long-term adaptation of CAS systems
• Insights into emergent properties of CAS
• Insights into general properties of large scale, distributed CAS
• Comparing and analyzing approaches to CAS (e.g., distributed and centralized)
• Decision-making approaches in CAS
• Methodologies for studying, analyzing, and building CAS
• Frameworks for analyzing or developing CAS case studies
• Languages, platforms, APIs and other tools for CAS
• Scenarios, case studies, and experience reports of CAS in different contexts (e.g., Smart Mobility, Smart Energy/Smart Grid, Smart Buildings, traffic management, emergency response, etc.)