Keynote: Seeking Order amidst Chaos and Uncertainty
summary
Shows how revenue management and dynamic pricing models succeed only when they forecast uncertainty not just point estimates and exploit real‑world structure to avoid NP‑hard intractability. Demonstrates clustering tricks for traveling‑salesman, a hotel example where 100 rooms appear “sold out” yet true demand is unobserved, a cow‑feed LP that cuts cost to one‑third but fails in practice, and a bottles‑and‑prices numerical case where volatility changes optimal allocation. Argues for hybrid use of optimization, game theory and machine learning.
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chapters (13)
- 0:00Introduction
- 1:51Business decisions under uncertainty
- 3:34Problem definition over solution
- 6:52Modeling complexity and Einstein’s lesson
- 9:31Exponential complexity solutions
- 15:12Optimization with a cow example
- 18:51Mathematical versus business optimality
- 22:29Decomposing problems for optimality
- 27:46Forecasting accuracy and uncertainty
- 32:34Revenue management pitfalls
- 38:05Decision-making under pricing uncertainty
- 41:42Key takeaways
- 42:04Closing story and Einstein’s game
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