A1Joint project · In development
Hospital Operations
How should capacity and patient-flow decisions respond to uncertain demand and operational constraints?
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Decision Labs
Make the decision. See what the model predicts.
M2W Decision Labs are transparent, testable demonstrations. Each begins with a decision question, exposes the modeled mechanics, compares policies on the same inputs, and states what must happen before real-world use.
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One decision. Two working stages.
Interactive prototypeSyntheticReproducible
Decision Lab 01
When less shelf space makes more revenue.
Which headphones should a store offer when buyers substitute after finding a product unavailable?
Run the decision →Revenue leaderPro + Plus
Expected revenue$79.98
All four products$54.70
per synthetic visitor- ModelMarkov-chain choice
- Simulate / evaluateBuyer absorption
- DecideChoose the shelf
- TransferEstimate and test
Decision Lab 02
More data cannot reveal what you never offered.
How much data—and which data—justify changing the shelf when customer preferences are unknown?
Run the learning loop →Designed explorationPro + Everyday
Item coverage4 / 4
Oracle pair observed0 times
fixed-seed synthetic experiment- LogObserved choices
- EstimateRank breaking
- DecidePessimistic policy
- TestFresh holdout
Developing directions
What the portfolio can become.
Directions appear here without implying that a partner platform, private dataset, or deployment belongs to M2W.
A2Public-safe prototype · Planned
Marketplace Dynamics
Which policies improve service and participation without destabilizing a two-sided system?
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Build maturity and evidence are different.
- Build status
- Concept → In development → Interactive prototype → Evaluated demonstration → Transferred
- Evidence status
- Synthetic → Public data → Partner or shadow evidence → Real-world evidence