There is a wave towards using data more and more in decision making across all levels of business and society. However, people often use data quite informally: look at an Excel sheet or a graph, then make a decision based on your impression. This often works well, but it can be fragile because of our many deep-skin biases as well as a general poor ability to reason about quantities and complex interactions.
Decision theory to the rescue! By adding a few axioms to the basic axioms of probility theory, we can extend statistical modeling to make decisions which maximize utility – whether that utility is happiness, profit, health, public support, or something else entirely. I’m not saying that we should blindly hand over decisions to algorithms, but seeing their limited-worldview pure-quantitative solution can be a nice decision support to keep some of our biases at bay.
I was motivated to write this tutorial to fill in a gap: there is a lack of practical entry-level guides that scale well to complex problems. The entry-level here is someone who just want to update an Excel sheet with new data, and see how that changes the decision. Here’s the accompanying twitter thread:
New tutorial on data-driven optimal decisions! I present a simple three-step approach which scales well from simple to complicated problems. https://t.co/bG5p1uc6j9 1/5
— Jonas K. Lindeløv (@jonaslindeloev) May 21, 2019
I made a first draft of this for an elective course in the fall. Then a second draft for my presentation at the Bayes@Lund 2019 conference. And now I finally got to brush it off. OK, I become overly excited when I get to talk about Bayesian inference AND utility at the same time:
