The convergence of DAOs and decentralized governance systems with autonomous AI agents presents an opportunity for the reshaping of current socio-economic paradigms.
S-1 introduces the Human-Machine Network (HMN), a framework for decentralized governance that facilitates hyper-efficient and harmonious human-machine collaboration across virtual and physical domains. The S-1 architecture is designed to orchestrate the interaction between human inputs processed through Succinct Non-interactive Arguments of Knowledge (SNARKs), Zero-Knowledge Virtual Machines (zkVMs), and a decentralized network of agents.
The Network State, a decentralized self-governing entity capable of acquiring physical territory through decentralized consensus, serves as the foundational governance layer where all socio-economic functions rest. The HMN aims to underpin the broader Network State, serving as the new paradigm for decentralized governance.
This enables secure and hyper-efficient execution of human decisions across vast numbers of network participants. The HMN framework proposes extraordinary efficiency and reduced bureaucratic overhead, addressing Moloch’s trap by concurrently solving multiple cooperative dilemmas through game-theoretic mechanisms. Through this approach, S-1 facilitates greater value alignment in decentralized systems.
This framework for governance is operationalized through three distinct abstraction layers.
The Human Input layer collects governance decisions via Zero-Knowledge Virtual Machines (zkVMs), ensuring data privacy and verifiable correctness. This layer facilitates the encoding of human inputs into encrypted proof circuits.
The modular DAO processes inputs autonomously to integrate governance logic into decision-making workflows. It operates with full transparency by tallying human inputs and generating executable commands for the agent layer.
In the Execution layer, the HMN leverages domain-specific agents to autonomously implement various governance directives. These agents could be tasked with financial management, policy enforcement, resource allocation, and more.
Unlike other models where agents manage autonomous DAOs, S-1 introduces agents as executors of encoded human-driven mandates, ensuring the system is governed by collective human will rather than fully autonomous decision-makers. These agents operate with swarm intelligence to handle complex governance challenges. We refer to this system as ‘Nebulocracy’.
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