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 2 of a two part conversation in which we delve into decision making under uncertainty. Feel free to check out part 1 here but this episode should also stand alone.

Why am I speaking to JD about all of this? Because not only is he a wild conversationalist with a real knack for explaining hard to grok concepts with illustrative examples and useful stories, but he has worked for many years in re-insurance, that’s right, not insurance but re-insurance – these are the people who insure the insurers so if anyone can actually tell us about risk and uncertainty in decision making, it’s him!

In part 1, we discussed risk, uncertainty, probabilistic thinking, and simulation, all with a view towards improving decision making.

In this, part 2, we discuss the ins and outs of decision making under uncertainty, including

How data science can be more tightly coupled with the decision function in organisations;
Some common mistakes and failure modes of making decisions under uncertainty;
Heuristics for principled decision-making in data science;
The intersection of model building, storytelling, and cognitive biases to keep in mind;

As JD says, and I paraphrase, “You may think you train your models, but your models are really training you.”

Links

Episode 16: Data Science and Decision Making Under Uncertainty

Vanishing Gradients