Figuring Out Repeatable Decision Guidelines is So Valuable

I’m currently reading a book called Simple Heuristics That Make Us Smart by Gerd Gigerenzer, Peter M. Todd, and the ABC Research Group. A heuristic is a guide for investigation or problem solving. It helps get to answers. In today’s world of ever increasing complexity, having someone figure out repeatable decision guidelines is so valuable. It requires a great deal of analysis and insight, but it can save money and save lives. Below is the opening except from the book and is a great example:

A man is rushed to a hospital in the throes of a heart attack. The doctor needs to decide quickly whether the victim should be treated as a low-risk or a high-risk patient. He is at high risk if his life is truly threatened, and should receive the most expensive and detailed care. Although this decision can save or a cost a life, the doctor does not have the luxury of extensive deliberation: She or he must decide under time pressure  using only the available cues, each of which is, at best, merely an uncertain predictor of the patient’s risk level. For instance, at the University of California, San Diego Medical Center, as many as 19 such cues, including blood pressure and age, are measured as soon as a heart attack patient is admitted. Common sense dictates that the best way to make the decision is to look at the results of each of those measurements, rank them according to their importance, and combine them somehow in to a final conclusion, preferable using some fancy statistical software package.

Consider in contract the simple decision tree below, which was designed by Breiman and colleagues to classify heart attack patients according to risk using only a maximum of three variables. A patient who has  a systolic blood pressure of less than 91 is immediately classified as high risk – no further information is needed. Otherwise, the decision is left to the second cue, age. A patient under 62.5 years old is classified as low risk; if he or she is older, the one more cue (sinus tachycardia) is needed to classify the patient as high or low risk. Thus, the tree requires the doctor to answer a maximum of three yes/no questions to reach a decision rather than to measure and consider 19 predicators, letting life-saving treatment proceed sooner.

This decision strategy is simple in several respects. First, it ignores the great majority of possible measured predictors. Second, it ignores quantitative information by using only yes/no answers to the three questions. For instance, it does not care how much older or younger the patient is than the 62.5-year cutoff. Third, the strategy is a step-by-step process; it may end after the first question and does not combine (e.g. weight and add) the values on the three predicators. Asking at most three yes/no questions is a fast and frugal strategy for making a decision. It is fast because it does not involve much computation, and it is frugal because it only searches for some of the available information. Its simplicity raises the suspicion that it might be highly inaccurate, compared to standard statistical classification methods that process and combine all available predicators. Yet it is actually more accurate in classifying heart attack patients according to risk status than are some rather complex statistical classification methods.

About benleeson
My name is Ben Leeson. I currently work for a large financial company in IT. I went to school at Marist College in Poughkeepsie, NY. I graduated with a B.S. in Business Administration concentrating in HR. Professor William Brown taught me and I enjoyed his classes; even acquiring an appreciation for just about all things HR. I didn’t pursue a job in that field after college but I’ve kept up with it. This blog will further my fascination with all things HR. I hope to grow my knowledge of the area through thoughtful writings and spirited feedback. I will attempt to have a fairly routine style so anyone reading can come to expect certain segments. Please excuse my incorrect grammar and occasional misspelling.

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