Trigger Analysis Table

This table provides a comprehensive overview of the reactive decision pattern used in distributed computing environments. It shows how each trigger type follows the same fundamental compositional structure across all dimensions of the decision-making process.

Complete Trigger Analysis

Trigger Type	Specific Trigger	Past Context	Available Actions	Selection Function	History Components	Policy Type	Forward Output	Backward Flow
Network Events	make_offer	Offer details Sender identity Alternatives Network state	Accept offer Reject offer Propose Noop	argmax ε-greedy(ε) softmax(τ) system_rule other	Past make offers Internal valuation Sequence of events Negotiation history Outcome Payment amount Utility	Learned model for offer evaluation	Action	Utility Information
Node Monitoring	resource_imbalance	Resource utilization Trends Queue depth Performance	Resolve internally Outsource by make offer	argmax ε-greedy(ε) softmax(τ) system_rule other	Past resource imbalances Internal cost of resolution Sequence of events Outsourcing negotiations (if any) Outcome Cost incurred Utility	Learned model for resolution decision	Action	Utility Information
Timer Events	cron_jobs	Scheduled evaluation results Periodic metrics	Do something Noop	argmax ε-greedy(ε) softmax(τ) system_rule other	Past scheduled executions	Learned model for execution decision	Action	Utility Information
Market Signals	arbitrage_opportunity	State of the market	Make offer Take offer	argmax ε-greedy(ε) softmax(τ) system_rule other	Past arbitrage attempts	Learned model for arbitrage decision	Action	Utility Information

Key Insights from the Table

Compositional Structure

Each trigger type demonstrates the same fundamental pattern:

1. Past Context provides the input information for decision-making
2. Available Actions define the agent's choices in response to the trigger
3. Selection Function specifies how actions are chosen (argmax, ε-greedy, softmax, system_rule, other)
4. History Components capture what gets stored from each interaction
5. Policy Type represents the learned decision model
6. Forward Output is always an Action that flows to the next game
7. Backward Flow consistently provides Utility and Information for learning

Universal Pattern Properties

This table reveals the compositional nature of CGT - all reactive decisions follow identical structural patterns while having different domain-specific content. This enables:

• Modularity: Each trigger type can be analyzed independently
• Reusability: The same decision framework applies across different contexts
• Scalability: New trigger types can be added following the same pattern
• Learning: Consistent backward flow enables policy optimization across all trigger types

Trigger Interconnectedness

Note how make_offer appears both as:

• A trigger (Network Events) - when an agent receives an offer
• An action (Node Monitoring and Market Signals) - when an agent decides to make an offer

This demonstrates the compositional nature of the system where one agent's action becomes another agent's trigger, creating a network of interconnected reactive decisions.

Agent Resource Context

All triggers incorporate the agent's resource portfolio state as part of the Past Context:

Infrastructure Resources

• Compute - Physical nodes, computational capacity, performance characteristics
• Storage - Data storage capacity and access patterns
• Network - Bandwidth, connectivity, communication capabilities
• Energy - Power consumption and availability

Strategic Resources

• Financial assets - Token balances, credit limits, transaction history, reputation scores
• Informational assets - Private data, proprietary algorithms, credentials, market intelligence

The state of these resources influences decision-making across all trigger types, enabling resource-aware reactive decisions.

Usage Notes

• Each row represents a complete reactive decision pattern instance
• The table structure is extensible - new trigger types can be added following the same column format
• All entries maintain CGT compositional properties while adapting to domain-specific requirements
• The consistent structure enables systematic analysis and comparison across different reactive decision contexts