An Introduction to Statistical Decision Theory, or: #ABYLFOYPE - Always Be Integrating Your Loss Function Over Your Posterior Estimate By Jim Savage Abstract: Making sound choices can be thought of as choosing between competing uncertain forecasts, each being the consequence of the choice. Statistical decision theory offers a principled method for making choices: we simply compare how we feel about each forecast--in a formal setting. In this talk, Jim walks through the ingredients required for conducting formal decision analysis in R and Stan. He provides examples from his career in making frontier markets and social impact investments. Bio: Jim Savage is a Manager: Data Scientist at Schmidt Futures, where he assists portfolio projects prototype their data science solutions, and helps to source grant and investment opportunities. Before Schmidt Futures, Jim was Head of Data Science at Lendable, where he built systems to automate due diligence processes and price portfolios of small loans in Sub-Saharan Africa. He also worked at the Grattan Institute in Australia, where he worked primarily on retirement savings policy, and at the Australian Treasury, on the ill-fated 2011 carbon price. He is a Bayesian statistician who specializes in discrete choice, causal inference, time series analysis, and the incorporation of contemporary machine-learning methods into all three fields.Jim holds a Masters in Economics from the University of Melbourne and a first-class honours in Economics from La Trobe University. He was also a 2014 Eric and Wendy Schmidt Data Science for Social Good fellow at the University of Chicago. Twitter: @jim_savage_ Presented at the 2019 New York Conference (May 11th, 2019)