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Reactive Decision Pattern

Overview​

The Reactive Decision Pattern represents the fundamental structure for trigger-based decision making in distributed computing systems. This pattern models how agents respond to various environmental triggers by building context and selecting actions. It incorporates distributed computing concepts of agents, nodes, and network into the fundamental game-theoretic decision structure.

Architecture Diagram​

Note: A standalone version of this diagram is available in reactive-decision-pattern.mermaid

Information Flow Architecture​

  1. Trigger Detection: Events occur in the Environment and trigger sources detect them
  2. Context Building: Environmental state (via Event Processor) and Agent state (Resource Portfolio) combine to form Input Context
  3. Decision Input: Integrated context flows to the Agent's decision point
  4. Selection: Agent queries the Model with a selection function to retrieve appropriate policy
  5. Policy Application: Model returns the relevant policy to guide the decision
  6. Action Output: Decision produces an action that flows to Output
  7. Utility Feedback: Outcomes generate utility signals back to the Agent (backward pass)
  8. Experience Extraction: Agent extracts experience from the interaction
  9. Policy Update: Experience updates the Model's policy store for future decisions

This unified flow shows how information moves through the reactive decision pattern - from trigger detection through decision making to learning and adaptation.