Decision-making is an essential part of everyday life and one of the main applications of data science is making the decision-making process easier.
However, mostly when data scientists build models, it’s to make a single decision. But in real life, decision-making is rarely that simple.
In this episode, Prof Warren Powell joins Dr Genevieve Hayes to discuss one way in which the decision-making process can become more complicated, in the form of sequential decision problems.
Guest Bio
Warren Powell is the co-founder and Chief Innovation Officer of Optimal Dynamics and a Professor Emeritus after retiring from Princeton, where he was a faculty member in the Department of Operations Research and Financial Engineering. He is also the author of Sequential Decision Analytics and Modelling and Reinforcement Learning and Stochastic Optimization.
Talking Points
- What is a sequential decision problem?
- Real-life examples of sequential decision problems and the disciplines in which they occur.
- The four main classes of techniques for solving sequential decision problems.
- How Warren’s approach to addressing sequential decision problems differs from the standard approach in this space.
- The challenges of implementing sequential decision analysis techniques in practice.
Links
- Connect with Genevieve on LinkedIn
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