Episode 36: Sequential Decision Problems

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 Warren on LinkedIn Warren’s website (SDA Links) Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE