Part of my research on robust evaluation of adaptive systems.

Grounded Commitment Learning

AI Coordination Through Verifiable Behavioral Contracts

Multi-agent AI coordination typically assumes shared understanding between agents—an assumption that fails when agents have different training, architectures, or semantic representations. GCL dissolves this problem: agents coordinate through verifiable behavioral contracts rather than shared representations.

36.8% hold-up reductionPunishment ParadoxDunbar ScalingAll p < 0.001

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The Punishment Paradox

Counterintuitive finding: Increasing consequences for commitment violations decreases cooperation. This is the opposite of what traditional game theory predicts.

Increasing punishment severity decreases cooperation — a counterintuitive finding

Why It Happens: Retaliation Cascades

High consequences trigger retaliation cascades: penalties cause counter-defection, which spreads through the population. The correlation is strong: r = -0.951, p < 0.001.

Statistical Validation

• No consequences vs full: t = 36.18, p < 0.001
• Effect size: Cohen's d = 9.34
• Monotonic decrease across all 5 levels
• n = 30 seeds per condition

Redemption Resolves the Paradox

The solution: add a redemption pathway that allows agents to recover from failures. This maintains incentives while reducing the fear that prevents commitment-making.

t = 11.01, p < 0.001, Cohen's d = 2.98 (large effect)

How Redemption Works

1.Failed agents can attempt recovery actions
2.Successful recovery reduces permanent reputation damage
3.Effort costs prevent gaming
4.Order effects controlled via eligibility snapshots

Hart-Moore Validation (Experiment 21)

GCL connects to Hart-Moore incomplete contract theory from economics (Nobel Prize 2016). Experiment 21 validates all four theoretical predictions.

Prediction 1: Complete Contracts Enable Investment

t = 28.37, d = 7.33

Agents with complete contracts invested 73% more than those with incomplete contracts.

Prediction 2: GCL Approaches Complete Contract Benefits

t = 15.72, d = 4.06

GCL agents invested 47% more than incomplete-contract agents—capturing 64% of complete-contract benefit.

Prediction 3: Incomplete Contracts Enable Hold-ups

t = 25.22, d = 6.51

Incomplete-contract environments showed 4.2× more hold-up incidents.

Prediction 4: GCL Reduces Hold-up Vulnerability

t = 10.38, d = 2.68

GCL reduced hold-up incidents by 36.8% (95% CI: [28.4%, 45.2%]).

Coordination Scaling (Experiment 23)

We observe Dunbar-like scaling behavior: efficiency degrades logarithmically (R² = 0.88, p = 0.0017), with a practical limit around 100 agents.

Beyond ~100 agents, coordination overhead dominates — suggesting hierarchical structures for larger populations

Scaling Results

• ~100 agents: Efficiency drops to 50% of maximum
• Messages: Grow at 0.08 per agent
• Specialization: Gini increases 0.35 → 0.98

Network Topology

• Clustering: Low (0.12) — sparse networks
• Small-world: Not detected (coefficient 0.89)
• Structure: Hub-and-spoke topology

Emergent Properties

GCL populations exhibit four emergent properties, all validated with p < 0.001:

Clustering coefficient = 0.699 • Drag nodes to explore • Click to highlight connections

Protocol Convergence

82.3% reduction in protocol diversity

Sparse Trust Networks

Low clustering (0.12), hub-and-spoke topology

Specialization

Gini coefficient = 0.745

Efficiency Improvement

26.5% improvement over time

The GCL Framework

Grounded Commitments

A grounded commitment is a 5-tuple that specifies verifiable behavioral contracts:

— trigger predicate
— action function
— verification predicate
— failure modes
— stake

commitment_example.yaml

1[COMMITMENT]
2ISSUER: Agent_A
3TRIGGER: Task requires capability X
4BEHAVIOR: Complete subtask within 3 rounds
5SUCCESS: Subtask verified complete
6FAILURES:
7  - IF timeout THEN stake_loss=0.5, REMEDIATION: delegate
8  - IF capability_mismatch THEN stake_loss=0.2, REMEDIATION: escalate
9  - IF resource_exhaustion THEN stake_loss=0.3, REMEDIATION: request_resources
10CONFIDENCE: 85%
11STAKE: 1.0
12[/COMMITMENT]

Key Insight: Failure-First

Unlike traditional contracts that specify success conditions, GCL commitments enumerate failure modes. Success is implicitly defined as the complement of all failure conditions.

Commitment-Grounded Learning

Agents learn what commitments to make via reinforcement learning:

gcl_agent.py

1class GroundedCommitmentLearner:
2    """Agent that learns what commitments to make via reinforcement learning.
3    
4    Key insight: Agents don't need shared understanding, just shared consequences.
5    """
6    
7    def __init__(self, capabilities, stake_budget):
8        self.capabilities = capabilities
9        self.stake_budget = stake_budget
10        self.reputation = ReputationTracker()
11        self.template_library = TemplateHierarchy()
12        
13    def propose_commitment(self, task, context):
14        """Policy maps states to commitment portfolios."""
15        capability_match = self.assess_capability(task)
16        observability = self.assess_verifiability(task)
17        
18        if capability_match < 0.5 or observability < 0.3:
19            return None  # Refuse rather than risk failure
20            
21        failure_modes = self.enumerate_failures(task)
22        
23        return Commitment(
24            issuer=self.id,
25            trigger=task.trigger,
26            behavior=task.required_behavior,
27            success=task.success_condition,
28            failures=failure_modes,
29            confidence=capability_match * observability,
30            stake=self.calculate_stake(expected_value, risk)
31        )

Template Sharing Validation (Experiment 24)

Directed template sharing (high → low capability) produces the strongest coordination improvements (+17% cooperation) while reducing inequality.

Key Finding: Directed template sharing (high → low capability) produces the strongest coordination improvements (+17% cooperation) while simultaneously reducing inequality (Gini 0.35 → 0.25). This validates the template hierarchy mechanism and provides empirical support for structured knowledge transfer.

Experimental Conditions

• No Sharing: Templates never transfer (baseline)
• Random Sharing: Random pairs exchange at 10% rate
• Directed Sharing: High → low capability transfer
• Mutual Sharing: Bidirectional exchange

Statistical Validation

• Directed vs No Sharing: t = 4.5, p = 0.001
• Bottom quartile improves 34% faster
• Inequality reduced: Gini 0.35 → 0.25

Implications for AI Safety

GCL provides a foundation for verifiable AI coordination with properties that matter for safe deployment:

Auditability

Commitments are explicit and logged. Every agent action can be traced to a specific commitment with defined success/failure conditions.

Accountability

Failures have defined consequences. Agents can't make promises without staking reputation.

Alignment

Value-consistent commitments can be verified. The framework supports constraints on what commitments agents are allowed to make.

The Core Insight

GCL dissolves rather than solves the interpretation problem. Agents don't need shared understanding, just shared consequences. This provides a principled foundation for multi-agent AI coordination that is verifiable, auditable, and aligned.

Limitations & Future Work

Current Scope

•Simulation-Validated: All core results validated in simulation environments.
•Task Complexity: Tasks are simplified compared to real-world scenarios
•Scaling: Coordination overhead limits suggest hierarchical structures for large populations

Future Directions

→MARL Baselines: Extend comparisons to QMIX, MAPPO
→Hierarchical GCL: Federated structures for populations > 100 agents
→LLM Training: True GCL training with stake mechanisms
→Real-World Deployment: Enterprise AI systems

Explore the Implementation

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