Episode 15: Uncertainty, Risk, and Simulation in Data Science

Hugo speaks with JD Long, agricultural economist, quant, and stochastic modeler, about decision making under uncertainty and how we can use our knowledge of risk, uncertainty, probabilistic thinking, causal inference, and more to help us use data science and machine learning to make better decisions in an uncertain world.
This is part 1 of a two part conversation. In this, part 1, we discuss risk, uncertainty, probabilistic thinking, and simulation, all with a view towards improving decision making and we draw on examples from our personal lives, the pandemic, our jobs, the reinsurance space, and the corporate world. In part 2, we’ll get into the nitty gritty of decision making under uncertainty.
As JD says, and I paraphrase, “You may think you train your models, but your models are really training you.”
Links
Vanishing Gradients' new YouTube channel! (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA)
JD on twitter (https://twitter.com/CMastication)
Executive Data Science, episode 5 of Vanishing Gradients, in which Jim Savage and Hugo talk through decision making and why you should always be integrating your loss function over your posterior (https://vanishinggradients.fireside.fm/5)
Fooled by Randomness by Nassim Taleb (https://en.wikipedia.org/wiki/Fooled_by_Randomness)
Superforecasting: The Art and Science of Prediction Philip E. Tetlock and Dan Gardner (https://en.wikipedia.org/wiki/Superforecasting:_The_Art_and_Science_of_Prediction)
Thinking in Bets by Annie Duke (https://www.penguin.com.au/books/thinking-in-bets-9780735216372)
The Signal and the Noise: Why So Many Predictions Fail by Nate Silver (https://en.wikipedia.org/wiki/The_Signal_and_the_Noise)
Thinking, Fast and Slow by Daniel Kahneman (https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow)

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