Meta Collective Intelligence

Dedication: Professor Jim Fallon from UCI, Science Advisor and mentor to Conversational Game Theory for fifteen years. Jim passed away in November of 2023, and this project is deeply in gratitude for his guidance over the years.

Rome Viharo

©2024 9×3 Narrative Logic LLC

Abstract

This paper presents Conversational Game Theory (CGT) as a novel framework for collaborative and collective intelligence. CGT integrates cognitive, computational, and psychological systems into a seamless meta-system designed to foster conflict resolution, productive disagreement, and win-win outcomes. 

Through recursive feedback loops and mechanism design favoring collaboration, CGT aligns individual cognition with group interaction to create emergent consensus. A defining feature of CGT is its ability to produce actionable, publishable artifacts—such as articles, contracts, laws, and news reports—ensuring collaborative conversations translate into real-world impact.

This paper explores CGT’s implications for AI-human collaboration, multi-agent systems, governance, social media platforms, and AGI development, presenting CGT as a model for scalable collaborative intelligence that evolves through recursive interaction and consensus-building.

 Introduction

  • The Challenge: Existing systems for decision-making, conflict resolution, and collaboration often struggle with binary choices or competitive dynamics.
  • The Solution: CGT offers a new paradigm that integrates cognitive, computational, and psychological systems into a recursive meta-system where collaboration is the dominant strategy.
  • Purpose of the Paper: To define the framework of CGT, outline its components, explore its ability to produce actionable artifacts, and demonstrate its applications across industries.

Core Components of Conversational Game Theory

Cognitive System: Thought and Reflection

  • Definition: The internal processes of individual agents, including logical reasoning, reflection, and perspective-shifting.
  • Dynamics:
    • Interaction between subjective (your/my) and objective (our) perspectives.
    • Recursive cognitive loops refine understanding through reflection and response.
    • Alignment with narrative arcs: Act 1 (framing), Act 2 (engagement), and Act 3 (consensus).

Computational System: Structured Interaction and Tracking

  • Definition: The system that organizes interactions, manages conversation state, and distributes permissions for change.
  • Dynamics:
    • Stateful memory tracks inputs, contradictions, and resolutions.
    • Act structure guides the conversation through phases of framing, engagement, and synthesis.
    • Permissions are granted to collaborative agents, enabling them to shape the system and create artifacts.

Psychological System: Behavioral Dynamics

  • Definition: The emotional or behavioral patterns influencing engagement (collaboration vs. competition).
  • Dynamics:
    • Collaborative behavior is rewarded with permissions to influence the system.
    • Competitive behavior results in reduced influence.
    • The system encourages competitive players to adopt collaboration to remain relevant.

Mechanism Design: Collaboration as the Dominant Strategy

  • Collaborative Incentives: Permissions to change the system are granted based on collaborative behavior.
  • Managing Competition: Competitive players are either nudged toward collaboration or excluded from influencing the conversation.
  • Consensus Power: The most collaborative participants gain consensus power to shape the outcomes and artifacts.

Recursive Feedback Loops Across Systems

  • Cognitive-Computational Feedback: Players reflect internally and contribute externally, feeding recursive insights back into their cognitive processes.
  • Behavioral Feedback: Behavioral patterns are influenced by system rules, fostering self-regulation toward collaboration.
  • Emergent Dynamics: Recursive feedback loops create unpredictable outcomes, generating insights that evolve the conversation and system artifacts.

Creating Actionable Artifacts Through Consensus

A defining feature of CGT is its ability to generate real-world artifacts based on collaborative consensus.

Types of Artifacts

  • Articles: Summarizing insights and consensus points for publication.
  • Blockchain: Publish transparent immutable smart contracts or distribute funds
  • Contracts and Agreements: Reflecting collaborative decisions among stakeholders.
  • Laws and Policies: Emerging from multi-agent consensus for governance.
  • News Reports: Balanced, multi-perspective summaries of key developments.

Impact of Artifacts

  • These artifacts are actionable—ready for implementation, publication, or governance.
  • The process of creating these outputs ensures that consensus conversations result in tangible outcomes, reinforcing the value of collaboration.

Applications of CGT

AI and Human Collaboration

  • Training AI Agents: AI agents learn to engage collaboratively, building trust and alignment with human participants.
  • Improving LLMs: CGT’s collaborative structure reduces hallucinations in large language models by reinforcing logical coherence.

Governance and Legal Systems

  • Generating Policies and Contracts: Stakeholders collaboratively create legally binding agreements through CGT interactions.
  • Consensus-Driven Governance: Laws and policies reflect the consensus of diverse agents, ensuring inclusive governance.

Social Media and Public Discourse

  • Filtering Toxicity: Collaborative behavior is rewarded, and divisive actions lose influence.
  • Balanced Journalism: CGT-generated news reports reflect multiple perspectives, ensuring unbiased reporting.

Multi-Agent Systems and AGI Development

  • Collective Intelligence Systems: CGT fosters adaptive, emergent behavior in multi-agent networks.
  • Path to AGI: By aligning cognitive, computational, and behavioral systems, CGT offers a framework for AGI development through collaborative recursion.

Key Features and Innovations

Generating Actionable Artifacts

  • CGT’s recursive process ensures that conversations result in tangible outputs, ready for publication or action.

Adaptive and Self-Regulating System

  • CGT functions as a self-correcting system, continuously evolving through recursive feedback.

Filtering Non-Collaborative Behavior

  • Competitive or deceptive actions are naturally filtered out, ensuring that only collaborative behavior shapes the system.

Narrative Structure in System Logic

  • The conversation aligns with natural cognitive flow through structured acts, enhancing the ease of consensus-building.

Comparison with Related Frameworks

Cybernetics and Systems Theory

  • Overlap: Feedback loops and adaptive behavior.
  • Difference: CGT emphasizes collaborative intelligence over control systems.

Game Theory

  • Overlap: Strategic interaction between agents.
  • Difference: CGT prioritizes collaboration over competition and outputs actionable artifacts.

Neural Networks and Collective Intelligence

  • Overlap: Multi-layered feedback and emergent patterns.
  • Difference: CGT integrates subjective, cognitive, and behavioral dynamics to produce structured artifacts.

A New Paradigm for Collective Intelligence

  • Summary: CGT integrates cognitive, computational, and behavioral systems into a meta-system that produces actionable outputs.
  • Implications: CGT offers applications in AI-human collaboration, governance, social platforms, and AGI development.
  • The Path to AGI: Through recursive collaboration, CGT lays the foundation for a new form of intelligence that evolves through human and machine interaction.