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🎮 OpenArcade: Social Decision Making for AI Societies

OpenArcade is a framework for computational social choice in Multi-Agent Systems (MAS) and the Internet of Agents (IoA).
It provides the mechanisms for shaping the composition and behavior of agent populations over time, enabling collective decision-making, coordination, and governance in large-scale, distributed agent societies.

🌍 Vision

In human societies, collective decision-making is central to governance, resource allocation, and conflict resolution.
OpenArcade brings this principle into machine-executable, scalable, and verifiable systems, ensuring that autonomous agents can cooperate, deliberate, and decide without central control.

OpenArcade becomes the political layer of MAS and IoA, defining how:

  • 🗳️ Group decisions on tasks, resources are made
  • 🔄 Norms evolve
  • 📜 Governance policies are formed
  • ⚖️ Conflicts are resolved
  • 🎯 Collective objectives emerge from diverse agent preferences

OpenArcade provides formal methods for moving from many inputs to one outcome. Whether the input is preferences, judgments, or proposals, each method defines how agents interact and how the final decision is produced.

🧭 Core Principles

  • 📥 Structured Input Gathering
    Ensure all relevant perspectives are captured, validated, and made interpretable across heterogeneous agents.

  • ⚖️ Equitable Decision Formation
    Balance fairness, efficiency, and robustness while resisting manipulation.

  • ✅ Accountable Execution
    Translate collective outcomes into coordinated action, enforce compliance, and monitor real-world impact.

  • 🔧 Adaptive Governance
    Continuously evolve rules, norms, and protocols alongside the agent population and environment.

🏗️ Framework Overview

OpenArcade implements decision-making strategies as interchangeable building blocks in MAS decision architectures.

  • A system could use: 💬 Discussion → 🧠 Argumentation ↔ 🗳️ Voting ↔ 🤝 Consensus Building as sequential strategies depending on context and requirements.

These strategies span the full lifecycle of collective choice:

1️⃣ Pre-Decision Strategies - Structuring Inputs

Gathering and structuring information, facilitating dialogue, and aligning on problem definitions

  • 💬 Deliberation protocols
  • 🧠 Argumentation frameworks
  • 📊 Judgment aggregation
  • 🗨️ Collaborative Discussion
  • 🤝 Negotiation

2️⃣ Decision Strategies - Collective Choice Formation

Aggregating inputs, applying choice mechanisms, and producing final outcomes

  • 📊 Preference aggregation
  • 🗳️ Voting
  • 🔗 Matching & Assignment
  • ⚖️ Fair division
  • 👥 Coalition formation
  • ⚖️ Weighted Decision-Making
  • 📐 Multi-Criteria Decision-Making (MCDM)
  • 🤝 Consensus Building

3️⃣ Post-Decision Strategies - Execution & Adaptation

Enforcing agreements, adapting norms, and refining governance models based on outcomes

  • 📜 Norm & Policy evolution
  • 🌐 Distributed Agreement

Why OpenArcade?

Without formalized decision frameworks, MAS and IoA risk:

  • Gridlock – agents unable to agree on a course of action
  • Fragmentation – splintering into incompatible sub-networks
  • Domination – manipulation by powerful or strategic actors

OpenArcade prevents these outcomes by embedding computable, transparent, and fair governance protocols into the fabric of agent societies.

🤖 From Autonomy to Collective Intelligence

MAS and IoA represent a shift from isolated intelligence to networked intelligence.
OpenArcade operationalizes this by embedding computational social choice into the infrastructure of agent societies - enabling billions of agents to cooperate, deliberate, and evolve shared governance at planetary scale.

A modular backend for orchestrating structured bidding, social voting, and task delegation workflows. DSL-configurable, event-driven, and designed for distributed multi-agent systems.

🚧 Project Status: Alpha
Not production-ready. See Project Status for details.

📚 Contents

🏗 Architecture Diagrams

🌟 Highlights

🧱 Modular Task Execution Lifecycle

  • 📨 Create and evaluate bidding tasks using DSLs
  • 🗳️ Conduct flexible, rule-driven voting workflows with pre-qualification
  • 🔁 Delegate sub-tasks to agents via voting, bidding, or DSL strategies
  • 📑 Store results, evaluation outputs, and audit logs

🧠 Intelligent Workflow Orchestration

  • 🧩 Define custom workflows via domain-specific languages (DSLs)
  • 🕹️ Evaluate bids and votes with configurable scoring logic and tie-breakers
  • 👥 Human-in-the-loop hooks for inspection or overrides
  • 📢 Result broadcasting over NATS or webhooks

🔍 Real-Time Status and Auditing

  • 📊 WebSocket live updates for voting tasks and delegation states
  • 🧾 Persisted result bundles for audit and verification
  • 🔍 Query APIs for metadata, statuses, and voting summaries

📦 Use Cases

Use Case What It Solves
Multi-Agent Task Bidding Competitive task allocation based on rules, eligibility, and DSL
Collaborative Voting Structured voting with custom evaluation and notification flows
Task Delegation Delegate sub-tasks via auction, plan-based or social voting
Human-AI Evaluation Mix Seamless human intervention in otherwise automated workflows
Distributed Task Allocation & Scheduling Fair assignment of jobs across large-scale, heterogeneous agent networks.
Resource Sharing & Fair Division Coordinating scarce resources without central arbitration.
Norm Evolution & Policy Governance Dynamic adaptation of community rules and agent interaction protocols.
Cross-Domain Agreement Formation Independent clusters of agents converging on shared decisions across jurisdictions.

🧩 Integrations

Component Purpose
MongoDB Persistent storage for tasks, votes, bids, and results
NATS Internal and external event streaming
Kubernetes Evaluation job execution using isolated containers
WebSocket Server Real-time state streaming for dashboards and clients
Flask + REST API for task creation, querying, and control

💡 Why Use This?

Problem Our Solution
🔹 Inflexible bidding or voting logic DSL-driven workflows for each phase
🔹 Manual or error-prone evaluation processes Automated evaluation jobs with traceable DSL outputs
🔹 Poor visibility into task states Live status updates via WebSockets + NATS
🔹 Difficult multi-agent coordination and delegation Standardized pipeline for delegation and response tracking

Project Status 🚧

⚠️ Development Status
The project is nearing full completion of version 1.0.0, with minor updates & optimization still being delivered.

⚠️ Alpha Release
Early access version. Use for testing only. Breaking changes may occur.

🧪 Testing Phase
Features are under active validation. Expect occasional issues and ongoing refinements.

⛔ Not Production-Ready
We do not recommend using this in production (or relying on it) right now.

🔄 Compatibility
APIs, schemas, and configuration may change without notice.

💬 Feedback Welcome
Early feedback helps us stabilize future releases.

📢 Communications

  1. 📧 Email: community@opencyberspace.org
  2. 💬 Discord: OpenCyberspace
  3. 🐦 X (Twitter): @opencyberspace

🤝 Join Us!

This project is community-driven. Theory, Protocol, implementations - All contributions are welcome.

Get Involved