Computational and Functional Principles

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Computational Reflection
NuNet will provide means for computational actors to exercise certain levels of computational reflection in terms of: (1) resource allocation, (2) data representation, (3) execution introspection:
  • The physical resource allocation aspect of computational reflection will allow agents to have continuous interaction with their execution environments, and search and request for additional computational resources and infrastructures according to their own criteria. Additionally, agents will be able to download, or update required libraries, i.e. evolve their own execution strategies, and this will allow for agents’ free migration from one node (virtual machine, cloud vendor, private computer or a mobile phone) in a distributed computing environment to another;
  • Capability and data representation aspect will allow agents to semantically represent their own computational capabilities and input and output data. This information will be made available for other agents to query when negotiating pairwise contracts and workflow designs;
  • Execution introspection is the ability of each agent to monitor actual resource utilization by its algorithms, keep history of execution times and memory usage, and access its own state during execution, amongst other features. Agents may decide to share part of this information with the network in order to prove their capabilities and quality of services;
Note that the abstraction of NuNet does not define in any manner how computation or actions of agents will be performed. Using means of computational reflection, computational agents will be able to design and apply workflow design and workflow execution functionalities pertaining to their individual choice and requirements. Furthermore, a human element can be seamlessly incorporated into the same model. For instance, an agent can represent a UI through which tasks that need human intervention can be performed and integrated into the workflow. A hybrid computer-human collaborative case can be imagined where NuNet workflows formulate a computational task, which is then performed by humans through crowdsourcing or freelancing marketplaces (Amazon MTurk, Udemy), or even code hosting platforms (GitHub, Bitbucket, etc.) The tokenomy of NuNet will support and facilitate decentralized marketplaces where human and machine jobs will be demanded, offered and contracted on a commercial or other basis. Computational reflection will enable owners of resources to advertise and price their capabilities and for resource users to estimate, track and manage computation costs in a dynamic and transparent way.