Design

The Distributed Timetable Assistant (DiTA) design focuses on extensibility, fairness, and reactive evaluation. It provides the abstract mechanisms that enable the marketplace described in the Overview.

Core Mechanisms

1. The Marketplace

The marketplace is the decoupling layer between Institutions (demand) and Solvers (supply). It ensures that:

• Discovery: Solvers can find problems that match their capabilities.
• Transparency: All transactions and evaluations are verifiable.
• Competition: Multiple solvers can attempt the same problem, driving up solution quality.

2. Reactive Evaluation & Scoring

DiTA employs a reactive scoring model. Solution scores are not static; they adjust based on the changing state of resources.

• Dynamic Scoring: If a resource (e.g., a specific lab) becomes overbooked globally, solutions relying on it may see their scores drop in real-time.
• Fairness: The system penalizes conflicts and rewards efficient resource usage proportionally.

3. Extensibility

The platform is designed to evolve without breaking existing contracts:

• Reward Models: New ways to incentivize solvers (e.g., tokens, reputation) can be plugged in.
• Gamification: Leaderboards and challenges can be added to the solver layer.
• Pluggable Validators: Institutions can add custom validation logic for their specific constraints.

4. Flexible Processing & Privacy

The system supports various processing models to suit different privacy and security needs:

• Self-Hosted Processing: Institutions can run their own solver services to keep data entirely within their infrastructure.
• Conditional Processing: Requests can be routed to specific solvers that meet certain criteria (e.g., "only trusted partners").
• Anonymized Processing: Data can be passed through an anonymization layer before reaching public solvers. The results are transparently de-anonymized upon return, allowing the community to solve the problem without seeing sensitive identity data.

Design Principles