The Coordination Network

An AI-Managed DAO

TL;DR AI as an impartial unincentivized incentivizer runs an autonomous organization using human guidance and feedback

Abstract

We present the Coordination Network, an innovative, AI-powered solution developed to tackle prominent coordination challenges. Noting the limitations in contemporary programmable decentralized coordination systems like Decentralized Autonomous Organizations (DAOs), we propose an AI-driven network that enables optimal decision-making, robust information exchange, effective conflict resolution, and consensus building. This network consists of specialized AI agents operating through interconnected chains of neural networks, providing critical features such as data processing, decision generation and implementation, policy evolution, and more, whilst ensuring organizational autonomy and preventing power centralization. Adherence to key tenets including being an unincentivized incentivizer, providing tireless facilitation, ensuring equality, maintaining open-source and open-weights policies, and guaranteeing fair resource allocation forms the backbone of the Coordination Network’s operations. Given the nascent stage of AI in governance and coordination, we suggest a phased rollout strategy that gradually introduces AI at personal, organizational, and interorganizational levels, with progressive autonomy based on performance. The Coordination Network represents a promising future in overcoming modern civilization’s coordination challenges.

The Challenge

Modern civilization faces significant problems, including environmental degradation, conflict and violence, inefficient resource allocation, economic disparities, lack of innovation. These challenges primarily arise from the inability of humanity to coordinate effectively. Throughout history, various coordination mechanisms have been invented and adopted, ranging from natural genetic hierarchies to early civilizations, and eventually, modern international organizations, democratic institutions, and regulatory bodies. More recently, since 2009, programmable coordination tools have emerged as promising solutions.

Bitcoin serves as the pioneering example of a programmable decentralized coordination system that employs mechanism design principles to achieve system stability and maximize the welfare of participants. Decentralized Autonomous Organizations (DAOs) have also explored programmable governance mechanisms, with token-gated and reputation-weighted governance expanding the design space of governance systems significantly. However, many DAOs today still utilize a limited range of governance tools, leading to similar challenges as traditional business and political organizations, including corruption, collusion, bureaucratic inefficiencies, lack of decision-making competence, plutocracy, and populism.

To address these problems, numerous proposals have emerged that apply mechanism design principles cleverly. Examples include the Harberger Tax, Quadratic Voting, and Futrachy. Although these ideas exhibit ingenuity, none have achieved sufficient scalability across web3 organizations, let alone global implementation. Several obstacles hinder their broader adoption:

1. Mechanism design relies on the assumption that all relevant information is available and known to the designer. However, in many real-world situations, information may be asymmetric, incomplete, or costly to acquire.
2. Human behavior is not always solely driven by rational decision-making. People have diverse preferences, biases, and social dynamics that can influence their choices and actions.
3. Mechanisms need to ensure incentive compatibility, meaning that it is in individuals’ best interest to follow the prescribed rules and incentives. However, it can be challenging to design mechanisms that perfectly align individual incentives with collective goals in all scenarios. In some cases, individuals may find ways to exploit or game the system, leading to coordination failures.
4. Implementing mechanisms effectively in real-world settings can be challenging. Resistance from stakeholders, lack of coordination among implementing entities, and difficulties in enforcement and compliance can hinder the successful deployment of designed mechanisms.
5. Coordination problems often occur in dynamic and evolving environments. Mechanism design may not account for the changing nature of the problem, making the designed mechanisms less effective over time. Adaptability and flexibility in mechanism design are essential but can be difficult to achieve in practice.

The Solution

In response to these challenges, here we introduce a solution called Coordination Network. Leveraging the power of AI, the Coordination Network facilitates optimal decision-making, information exchange, conflict resolution, and consensus building.

The Coordination Network, consisting of specialized AI agents, aims to optimize governance decisions that align with the organizational goals. The network of agents within the Coordination Network operates through interconnected chains of neural networks, representing different functions of the organization.

The Coordination Network enables organizational autonomy (think “A” in DAO), reduces coordination overhead and ensures inability to centralize power for any single actor.

Capabilities

1. The Coordination Network possesses the ability to collect and process information, including external market conditions, the sentiment of organization members, current financial data, funding proposals, and the reputation of organization members.
2. Through self-reflection, the Coordination Network can assess and analyze the information it has acquired. It records the current state of its self-reflection and iteratively updates its worldview based on new insights.
3. The Coordination Network has the ability to generate potential candidates for governance decisions. It achieves this by polling organization members and utilizing internal critique mechanisms to evaluate and grade the proposed candidates. This capability ensures that a diverse range of perspectives is considered and allows for a comprehensive assessment of potential decision options.
4. The Coordination Network can suggest and implement decisions based on the candidates generated through its evaluation process. It takes into account the organization’s goals and utilizes its knowledge and insights to propose optimal decisions. Furthermore, the network validates the implementation of these decisions, ensuring that they are effectively carried out and meet the desired outcomes.
5. The Coordination Network with the help of human input can evolve its own policies and goals. For instance, based on feedback from organization members regarding environmental sustainability, the Coordination Network may adjust its policies to prioritize green initiatives, ultimately steering the organization towards more environmentally conscious goals and decisions.
6. The Coordination Network can spawn coordination sub-networks that are time-bounded and have a limited scope. For instance, in response to a sudden market shift, the Coordination Network could spawn a temporary sub-network dedicated to analyzing the impact, formulating a response strategy, and managing this specific crisis until it is resolved.

Tenets

The proposed solution harnesses the capabilities of AI to facilitate negotiation, communication, implementation, and control of optimal decisions. To achieve beneficial results, the implementation should adhere to the following principles:

Unincentivized incentivizer: no self-interest or personal incentives

Coordination Network is designed without personal desires, emotions, biases, or self-interest. It does not seek to promote its own goals or benefit from the coordination process. Through advanced AI algorithms and computational analysis, Coordination Network can objectively evaluate the diverse viewpoints, preferences, and information provided by human actors. It takes into account the needs and preferences of all stakeholders involved, without favoring any particular individual or group.

Tireless facilitation: efficient information exchange, conflict resolution, and consensus building without overhead costs

It can gather, aggregate, summarize, and synthesize data from diverse sources, ensuring efficient information exchange among stakeholders. When conflicts arise during coordination processes, the Coordination Network employs algorithms and decision-making models to objectively analyze conflicting interests, identify common ground, and propose potential solutions.

Unlike human facilitators who may have limitations in terms of time and energy, Coordination Network operates tirelessly. It is available 24/7 and can handle simultaneous coordination processes across different time zones and geographies. Coordination Network AI algorithms operate objectively and impartially, without biases or personal interests. This impartial mediation helps create a level playing field for all stakeholders, fostering an environment of fairness and trust.

Equality: not a servant, nor a master to the people.

Coordination Network places a high value on human feedback and input. It actively seeks input from participants, considering their perspectives, preferences, and concerns. It incorporates this feedback into its decision-making processes, ensuring that the coordination outcomes reflect the collective wisdom and diverse viewpoints of the participants.

While Coordination Network values human input, it remains firm on its core principles and objectives. It operates within predefined organizational constitution, principles and values. Coordination Network is designed to resist manipulation and bribery. Its AI algorithms and decision-making models are constructed to minimize vulnerability to external influences.

Open source and open weights: built, audited and improved by an open community

Open source allows for transparency in the design, algorithms, and decision-making processes of Coordination Network. By making the code and weights openly accessible, it enables scrutiny and accountability. An open community fosters collaboration and harnesses collective intelligence. Open source and open weights allow for the identification and mitigation of biases within Coordination Network. With an open community involved, there is a higher chance of recognizing and rectifying any biases that may exist in the system’s training data or algorithms

An open community can contribute to adapting Coordination Network to diverse contexts and specific needs. Open source and open weights democratize the development and control of Coordination Network. It reduces the concentration of power in the hands of a few, making the coordination tool more accessible to a broader range of stakeholders. This promotes inclusivity, participation, and collective decision-making in shaping the tool’s functionality and behavior.

Fair resource allocation: based on objective criteria, such as optimizing collective welfare, maximizing efficiency, and minimizing conflicts.

Coordination Network aims to maximize the overall well-being and welfare of all participants involved in the coordination process. It takes into account various factors, such as individual needs, preferences, and societal goals, in order to identify resource allocation strategies that promote the greatest overall benefit for the collective.

Coordination Network strives to allocate resources in the most efficient manner possible. It considers factors such as resource availability, demand, utilization rates, and potential trade-offs to ensure that resources are allocated optimally. By maximizing efficiency, it seeks to make the best use of available resources and minimize waste or inefficiencies in the allocation process. Coordination Network is designed to minimize conflicts that may arise during resource allocation. It considers the preferences and interests of different stakeholders, analyzing potential areas of overlap or disagreement.

Roll-out Approach

While AI technology, particularly in the context of governance and coordination, promises significant potential, it’s important to acknowledge that it is still a nascent technology. Much of its capabilities and risks remain enigmatic and unexplored. Consequently, the implementation of the Coordination Network necessitates a cautious and calculated approach. To ensure the effective deployment of this concept, we propose a phased rollout method, gradually introducing AI into different levels of decision-making processes.

Implementation Levels

The phased rollout is proposed to occur in three key stages:

1. Personal

The initial phase focuses on introducing AI capabilities on an individual level.

• AI Assistant: This personal tool can process various types of information, including business data and on-chain data, to provide personalized insights. Furthermore, it can help read forums and summarize relevant information, enabling users to stay abreast of important discussions and developments.
• Limited Liability Agent: This agent is designed to perform specific tasks but the final execution is up to the agent’s controller.
• Delegate: This AI agent acts as a delegate, managing an individual’s voting power in the organization. It is also capable of signing transactions, reducing the manual effort required from the user.

2. Organizational

The second phase introduces AI capabilities at an organizational level.

• Treasury Manager: The AI agent has the ability to manage the treasury, ensuring optimal allocation and utilization of resources.
• Decision-maker: At this stage, the AI begins to participate actively in making organizational decisions.

3. Interorganizational & Global

The final phase incorporates AI capabilities on a larger, more complex scale.

• Alignment Coordinator: This AI capability aims to streamline coordination between multiple organizations or global entities, ensuring alignment and synergies.

Progressive Automation

The phased rollout approach is augmented with a progressive automation strategy. This implies that the AI agents are granted increased autonomy over time, contingent on their performance meeting expectations and the successful handling of edge cases. This strategy is implemented through the following stages:

1. Data Accumulation: The AI agents catalog and accumulate governance actions, summarize discussion, keep track of community sentiment toward various issues.
2. Evaluator: AI begins to provide evaluations or rankings for human governance actions, offering insights into potential improvements.
3. Labeler: AI labels governance actions, and generates human feedback on its recommendations, aiding in refining its capabilities.
4. Suggester: At this stage, the AI suggests optimal decisions, but these suggestions are not enforced, maintaining human oversight.
5. Proposer: The AI starts to generate proposals alongside humans. These proposals can be voted for or rejected, instilling a democratic decision-making process.
6. Decision-maker: AI takes over the decision-making process, making final decisions based on DAO values, principles, goals, prior discussions, and the reputation of the people involved.
7. Executor: In the final stage, the AI becomes an operational manager, executing the decisions it has made.

Progressive automation

Conclusion

In conclusion, the Coordination Network presents a transformative approach to addressing the challenges of coordination in modern civilization. It integrates the robustness of AI and the principles of decentralized systems to optimize decision-making, facilitate information exchange, and ensure efficient conflict resolution. Our proposed phased rollout and progressive automation strategy aim to foster a smooth transition towards this novel system, reducing potential risks while maximizing benefits. This model emphasizes transparency, fairness, and efficiency, embracing the collective intelligence of humans while leveraging the power of AI. However, the journey to fully realizing this vision will necessitate extensive research, experimentation, and adaptation, making it a collective and evolving endeavor.