“Whatever else it produces, an organization is a factory that manufactures judgments and decisions. Every factory must have ways to ensure the quality of its products in the initial design, in fabrication, and in final inspections.”
Daniel Kahneman (2002, Nobel Prize in Economics; author of Thinking, Fast and Slow )
This blog entry is based on a talk entitled “Decision quality, problem-solving, and data science — Lean concepts for human-centered, data-informed decision-making”.
Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.— Daniel Kahneman 
Read working paper here: http://bit.ly/ph-thinking
Introduction: data science and decision quality Population health is a systems framework for studying and improving the health of populations through collective action and learning . Population health data science (PHDS) is the art and science of transforming data into actionable knowledge to improve health .
Introduction At the San Francisco Department of Public Health, Population Health Division, we promote Decision Quality (DQ) [1,2]. DQ starts by knowing what a good decision looks like. A good decision is built with six quality requirements (Table 1).
Table 1: Decision quality requirements: A decision is only as strong as its weakest link Name Quality requirements Key question Frame Appropriate frame What are we deciding and why?
The nonconscious influence of cognitive biases Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.
… Daniel Kahneman1
Table of Contents The nonconscious influence of cognitive biases System 1 and System 2 (a.k.a. the “elephant” and the “rider”) Cognitive biases in decision making 1. Protection of mindset 2. Personality and habits 3.