Dynamic Pricing in Ridesharing Platforms

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" summary

Argues that fine-grained surge pricing is modeled with a Jackson network model and a simple threshold state pricing rule that sets fares based on instantaneous available drivers; analysis uses stochastic queueing, equilibrium entry of drivers, numerical solves and a large-market scaling limit. Surprisingly the scaling limit shows dynamic two-price policies collapse to the same throughput as an optimal static price, so the real gain is robustness to demand estimation error rather than higher peak throughput; implemented heuristics run at Lyft and pass A/B tests.

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" chapters (10)

1. 0:00Introduction and motivation
2. 2:32Marketplace dynamics context
3. 5:30Ride-sharing platform model
4. 9:22Dynamic pricing mechanics
5. 13:03Comparing static and dynamic
6. 17:08Literature and background
7. 19:46Stochastic model development
8. 31:45Scaling and throughput analysis
9. 37:41Robustness and control benefits
10. 44:41Q&A and concluding remarks