Pricing Under Uncertainty: A Bayesian Workflow

About this episode

Daniel Saunders discusses how Bayesian decision theory addresses real-world risk aversion in pricing optimization.

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 &amp…

People in this episode

Host: Alexandre Andorra

Guest: Daniel Saunders

Topics covered

• Bayesian decision theory
• risk aversion
• pricing optimization
• uncertainty
• exponential utility functions
• optimal decision-making

Keywords

• Bayesian statistics
• risk aversion
• pricing strategy
• decision theory
• utility functions
• uncertainty
• optimal decisions

Mentioned in this episode

Organizations: Patreon, Bayesian Modeling Course, topmate.io

Books & works: Good Bayesian

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