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Conversational Game Theory (CGT): A Meta-System of Collaborative Intelligence

Conversational Game Theory (CGT): A Meta-System of Collaborative Intelligence

Rome Viharo

©2024 9×3 Narrative Logic LLC

Abstract

This paper presents Conversational Game Theory (CGT) as a novel framework for collaborative 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 Collaborative 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.