Productive Value-Productive Power (PV-PP) framework
A four-paper series for program managers, business owners, decision-system architects, and technically interested readers responsible for the decisions AI agents are increasingly being asked to make.
White Paper 1
From Simple Rules to Scoring Systems and Automated Judgment
Explains how AI agents are assigned decisions, how simple rules differ from judgment, and how judgment is often converted into weighted scores and scalar aggregation. It also identifies what program managers and business owners are implicitly approving when they accept an agent decision model.
White Paper 2
What You Don’t Know About Scalar Aggregation Can Hurt You
Examines when a one-number model faithfully represents the decision the organization intended to delegate and when it preserves only the appearance of correctness. It distinguishes reproducing a winner from preserving the full ranking and the actual decision process.
White Paper 3
A Practical Test for Easy, Hard, and Impossible Scalarization
Provides an architecture-selection test for deciding whether to use a simple score, engineer a more complex scalar model, enrich the representation, or move to a broader structured decision architecture.
White Paper 4
From Business Rules to Structured Governance, Sidecar Control, and Runtime Execution
Describes a bounded path from business requirements to structured decision artifacts, testing, sidecar governance, replay, and runtime-integrated execution. The publication draft reflects the frozen PV-PP Runtime API v0.70 and distinguishes common runtime responsibilities from application-specific persistence and integration.