# Adaptive Personalization & Decision Policy Engine

## Purpose

Webdev Agent OS v24 governs context-aware experiences without creating a hidden profiling or manipulation system. It converts approved Journey Optimization evidence into explicit decision policies that declare what context may be used, what data is prohibited, which variants are eligible, how allocation is controlled, why a choice was made, and who approved activation.

## Evidence chain

```text
Approved journey evidence
→ Decision policy
→ Allowed and prohibited signals
→ Eligibility rules
→ Offline simulation
→ Fairness and harm checks
→ Controlled allocation plan
→ Independent approval
→ Explainable decision record
→ Drift monitoring
→ Hold, rollback, revise, expand, or retire
```

## Policy contract

Every policy must include:

- A measurable objective
- A safe default variant
- Allowed contextual signals
- Explicitly prohibited signals
- A human-readable explanation notice
- A kill switch
- A rollback reference
- Supporting evidence
- A responsible author
- Independent review before activation

Direct sensitive attributes are not eligible personalization signals. Persistent user identifiers are not stored in policy evidence.

## Supported policy modes

### Fixed contextual policy

A deterministic rule selects an approved presentation based on low-risk context such as viewport class, network quality, input modality, explicit device preferences, service availability, or journey stage.

### Adaptive policy

An allocation model may alter the distribution between approved variants. Adaptive policies require offline simulation, a meaningful control floor, passing fairness checks, a kill switch, rollback evidence, independent approval, and continuous drift monitoring.

## Explainability contract

Every recorded decision includes:

- An anonymous, aggregate, simulation, or fixture context reference
- The selected variant
- Human-readable reason codes
- The contextual signals used
- Signals explicitly excluded
- A clear explanation a user or reviewer can understand
- Evidence references

The engine must be able to answer: **Why was this experience selected, which signals were used, which signals were prohibited, and what safe alternative was available?**

## Fairness and harm controls

Checks may cover:

- Accessibility outcome parity
- Experience-harm disparity
- Allocation concentration
- Trust or privacy harm
- Support effort
- Reliability and performance regression
- Conversion quality without dark-pattern pressure

A policy cannot be approved or expanded while a required check is pending or breached.

## Drift and over-optimization

Runtime evidence may identify:

- Outcome drift
- Allocation concentration
- Segment harm
- Accessibility regression
- Trust deterioration
- Retention risk
- Model instability
- Control-group erosion

A breached drift signal automatically recommends the safe default and may place an active policy on hold. It does not silently execute an external traffic change.

## External execution boundary

The bundled system stores governance evidence and exposes API contracts. It does not claim live integration with:

- Advertising networks
- Customer data platforms
- User-profile stores
- Feature-flag providers
- Experiment platforms
- Production recommendation engines
- Real-time traffic allocators

External adapters require separate credentials, contracts, testing, and approval.