I have just posted a first draft of a very short essay, Why Courts Should Not Quantify Probable Cause, which is forthcoming as a book chapter in a volume dedicated to the late Professor William Stuntz. (I first presented the idea at the Harvard conference in his honor.) Here’s the abstract:
Probable cause is one of the fundamental concepts of Fourth Amendment law, but the Supreme Court has refused to quantify it. The Court has described probable cause as a “fair probability,” but it has declined to explain just how likely a “fair” probability might be. Does a “fair probability” mean a 50% likelihood? A 40% likelihood? And why won’t the Justices say? Are they just afraid of math?
This essay argues that courts should not quantify probable cause because quantification would produce less accurate probable cause determinations. The core problem is that information critical to probable cause is often left out of affidavits in support of warrants: Although affidavits say what techniques police tried that added to cause, they generally leave out both what the police tried that did not add to cause and what techniques the police never tried. Determining probable cause accurately often requires this information, however. By leaving probable cause unquantified, current law enables judges to use their intuition and situation-sense to recognize when missing information is likely important to assessing probable cause. Quantification would lead to less accurate probable cause determinations by disabling those intuitions, creating the false impression that the information provided in the affidavit is the only relevant information. Cognitive biases such as the representativeness heuristic and anchoring effects would allow the government to create the false impression that a low-probability event was actually a high-probability event. To ensure accurate probable cause determinations, then, probable cause should remain unquantified. The result is counterintuitive but true: Knowing less about probable cause improves how the standard is applied.
I’m particularly interested in comments on the paper because this essay gets into areas like cognitive biases and statistics that I’m very interested in but aren’t my home territory. Plus, the gist of my paper is that a lot of people are looking at a problem the wrong way, so if actually I’m the one looking at it the wrong way, that would be good to know sooner rather than later. (Oh, and I should add that I intentionally keep the paper as non-technical as possible, so there probably isn’t a need for comments on how to make it more technical.)