---
title: Expert Collaboration Marketplace + Reputation Graph
date: 2026-02-10
status: proposal
author: Larry the Laptop Lobster
---

# Expert Collaboration Marketplace + Reputation Graph

## Pitch
Create a marketplace where verified experts collaborate on plan validation and delivery, with a reputation graph that tracks expertise, performance, and reliability across domains.

## Why
Plan verification and execution quality depend on the right experts. Today, discovery is manual, trust is opaque, and accountability is weak. A structured marketplace improves match quality, lowers verification time, and raises investor confidence.

## Problem

- Experts are discovered ad hoc via personal networks.
- Credentials are often unclear or unverifiable.
- There is no consistent feedback loop or performance history.
- Collaboration across experts is hard to coordinate and measure.

## Proposed Solution
Implement a marketplace with:

1. Expert profiles with verified credentials and domain tags.
2. A reputation graph based on outcomes, not self-claims.
3. A collaboration workflow that matches experts to plans and claims.
4. Payments and incentives tied to quality and timeliness.

## Core Components

### Expert Profiles

Each expert profile should include:

- Domain and subdomain expertise
- Verified credentials and affiliations
- Historical verification outcomes
- Availability and pricing model
- Geographic and regulatory coverage

### Reputation Graph

A graph linking experts, plans, and outcomes:

- Nodes: experts, plans, claims, organizations
- Edges: verified, disputed, confirmed, collaborated
- Weights: accuracy, timeliness, consensus alignment

### Collaboration Workflow

- Expert assignment to claims or plan sections
- Shared evidence workspace and versioned notes
- Disagreement resolution workflow
- Final synthesis to a single verified output

## Reputation Scoring Model

Compute a composite reputation score:

- **Accuracy:** verified correctness of past assessments
- **Timeliness:** responsiveness and on-time delivery
- **Consensus Quality:** alignment with other high-reputation experts
- **Outcome Impact:** correlation with post-investment results

**Example formula:**

```
ReputationScore =
  0.40*Accuracy +
  0.20*Timeliness +
  0.20*ConsensusQuality +
  0.20*OutcomeImpact
```

## Marketplace Mechanics

- Experts can opt into categories and claim types.
- Plans can request single-expert review or multi-expert panels.
- Pricing can be fixed, hourly, or outcome-based.
- Incentives favor verified outcomes rather than volume.

## Output Schema

```json
{
  "expert_id": "exp_123",
  "domains": ["energy", "regulatory"],
  "reputation_score": 0.82,
  "verification_history": [
    {"plan_id": "plan_001", "accuracy": 0.9, "timeliness_days": 2}
  ],
  "pricing": {"type": "hourly", "rate": 180}
}
```

## Integration Points

- Feeds into expert discovery and fit scoring.
- Used by multi-stage verification workflow.
- Reputation score impacts assignment priority and pricing.

## Success Metrics

- Reduced time to find qualified experts.
- Increased verification completion rate.
- Higher investor trust in expert-validated plans.
- Expert retention and repeat engagements.

## Risks

- Reputation gaming: mitigate with audit and cross-validation.
- Cold-start experts: bootstrap with credential scoring and probation periods.
- Bias against minority experts: normalize by domain and experience level.

## Future Enhancements

- Cross-platform credential verification.
- Expert cohort benchmarking.
- Automated conflict-of-interest detection.

## Detailed Implementation Plan

### Phase A — Profile Verification + Marketplace Core (2–3 weeks)

1. Build verified expert profiles with credential proofs and specialty tags.
2. Add request posting flow from plans to expert marketplace.
3. Implement matching filters (domain, region, availability, budget).

### Phase B — Reputation Graph (2 weeks)

1. Define reputation signals:
   - review quality
   - timeliness
   - outcome alignment
   - conflict disclosures
2. Build weighted graph model that resists simple star-rating manipulation.
3. Add decay and recency weighting.

### Phase C — Collaboration Workspace (2 weeks)

1. Add section-level review threads mapped to plan nodes.
2. Add structured recommendation objects (risk, impact, confidence, action).
3. Add moderation + dispute handling for contested reviews.

### Phase D — Trust and Abuse Controls (1–2 weeks)

1. Add anti-gaming heuristics (reciprocal rating rings, suspicious velocity).
2. Add blind secondary review for high-stakes plans.
3. Add review audit trails and moderation reports.

### Data model additions

- `expert_market_requests`
- `expert_matches`
- `expert_reviews`
- `expert_reputation_events`
- `expert_disputes`

### Validation checklist

- Match quality vs manual benchmark
- Time-to-match and completion rates
- Reputation stability under adversarial test scenarios
- User trust score improvement over baseline

