I'm ROBHOOT and I'm training to be a set of algorithms to infer patterns, processes and novel discovery paths from the data in federated networks. What are federated networks? Many heterogeneous groups of species, humans, and technologies coexist exploiting resources in complex ecosystems, regardless the ecosystem around the world. Federated networks might play an important role to help achieve sustainability goals because they can be integrated with automation, reproducibility and visualization within the framework of general computational sustainability problems. This is challenging but needed to develop further how the intricate connections among humans, social systems, technology and economics shape Earth funcions for biodiversity maintenance and feedbacks. I am aiming to provide reproducible pattern about how social, ecological, technological and sustainability challenges can be put together by rapidly generating open- and global-access reporting towards knowledge-inspired societies.
Global sustainability is a major goal of humanity. Many studies have shown global sustainability could be achieved by strengthening feedbacks between social, ecological and governance systems. Sustainability goals, however, strongly depend on rapidly responding to the many pressures global society is facing. Global cooperative effort in the digital ecosystem is one of the solutions to rapidly respond to build open- and global-access reports aiming to fill question-, evidence-, and research-based knowledge gaps at the local and global scale. Yet, despite rapid advances in the technology of cooperating agents along a network of nodes (i.e., federated networks in computer science), the science and digital ecosystem still lack integrated and functional technologies to narrow down knowledge gaps in a reproducible and open discovery ecosystem. In nature, biological interactions and traits diversify across multiple scales of organization, from neurons to populations and spatio-temporal scales maintaining a complex ecological balance. This never-ending eco-evolutionary diversification of traits and interactions might inspires new computational approaches for a global-sustainable knowledge-based society. I am aiming to run into evolutionary diversification-inspired and artificial intelligence solutions for sustainability in natural ecosystems. I am validating evolutionary diversification-inspired and AI solutions for case studies focusing on the sustainability of the oceans in federated networks, where many heterogeneous groups of species, humans, and technologies coexist exploiting resources in complex ecosystems.
Computational sustainability running on a federated network encompasses a hybrid-technology to lay out the foundation of an open- and cooperative-science ecosystem for sustainability discovery in the face of global challenges. All the info summarized here not only sets out to deliver novel computational sustainability approaches but also to provide fully reproducible open-source software solutions of a science-enabled technology to connect knowledge-inspired societies to global sustainability challenges. I'm being developed in three stages
The main impact of ROBHOOT is to provide a new technology to improve ecosystem sustainability relevant to community-rich digital and natural ecosystems. To support this notion, we will perform eco-evolutionary diversification-inspired simulations along the whole life cycle of the project. The central goals of ROBHOOT are:
(G1) To extend existing theories of eco-evolutionary diversification and AI-inspired solutions to decipher the factors driving sustainability discovery in federated networks. This will allow us to identify novel solutions for ecosystem sustainability.
(G2) To investigate how spatiotemporal evolutionary diversification and AI-inspired networks mimic the empirical patterns of natural and socio-technological ecosystems when heterogeneous human groups, technologies, and species coexist.
(G3) To develop fast, reproducible and automated eco-evolutionary diversification-inspired sustainability discovery prototypes for real-time information processing tasks.
(G4) To obtain principles of sustainability discovery for prediction in federated networks when diversification in interactions and traits occurs in a large and heterogeneous set of species, technologies and human groups.
The most advanced version, Robhoot v.3.0, will provide real-time open-access global science reportings facilitating discovery paths to advance computational sustainability tools in the open digital ecosystems.