Pricing Under Uncertainty: A Bayesian Workflow

Today's clip is from Episode 152 of the podcast, featuring Daniel Saunders. In this conversation, Daniel explores how Bayesian decision theory handles real-world risk aversion beyond the textbook maximum expected utility framework. The key insight: classical Bayesian decision theory assumes risk neutrality, but in practice, people and businesses are risk-averse. Using a pricing optimization example, Daniel shows how uncertainty varies dramatically across price points—lower prices have predictable demand, while higher prices create wide uncertainty in profits. This asymmetry matters when you want safer decisions. Daniel introduces exponential utility functions—a technique from economics that models diminishing returns on money. By adjusting a risk-aversion parameter, you can see how increasing risk aversion shifts optimal decisions away from high-uncertainty, high-profit scenarios toward more predictable outcomes. The broader lesson: optimal decision-making requires separating the modeling process from the decision process, allowing you to build in constraints and risk adjustments that pure expected utility maximization would miss. Get the full discussion here Support & Resources → Support the show on Patreon: → Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at !

You can listen to Pricing Under Uncertainty: A Bayesian Workflow online on Radio and Podcast. Open the player on this page to stream the available audio.

Pricing Under Uncertainty: A Bayesian Workflow is an episode from Learning Bayesian Statistics by Alexandre ANDORRA.

This episode is 00:05:03 long.

This episode was published on Apr 16, 2026.

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