Deep dive into sensitivity analysis for decision problems, implementing the oil field purchase decision using PyAgrum's library, and creating an interactive web interface with Gradio to explore how changes in probabilities and utilities affect optimal decisions.

Explores the strengths and limitations of decision trees, introduces influence diagrams as a scalable alternative, details their evaluation using the arc-reversal / node-reduction algorithm, and surveys Python libraries (PyAgrum, PyCID) for practical decision analysis.

An introduction to decision theory through a practical oil field investment example, covering key concepts like expected utility, risk preferences, and decision trees.